MYC analyses in Pathways

In Leiserson et al. MYC is left out, though it is presented as a rather "hot" node with frequent copynumber amplifications.

Leiserson:Cancer cells are thought to harbor multiple driver mutations that perturb multiple biological functions15. Consistent with this model, we found that four pairs of subnetworks, including TP53 and NOTCH signaling, TP53 and RTK signaling, PI3K signaling and the cohesin complex, and PI3K signaling and the ASCOM complex, exhibited significant co-occurrence (P < 0.05, multiple-hypotheses corrected) across the pan-cancer cohort (Fig. 2b) or in individual cancer types (Fig. 2c). Multiple pairs of genes within these subnetworks showed co-occurring mutations (Supplementary Table 24). In contrast, mutually exclusive mutations are typically expected within a pathway and not across pathways32,33.

Ref 32 Yeang, C.-H., McCormick, F. & Levine, A. Combinatorial patterns of somatic gene mutations in cancer. FASEB J. 22, 2605–2622 (2008).

Ref 33 Vandin, F., Upfal, E. & Raphael, B.J. De novo discovery of mutated driver pathways in cancer. Genome Res. 22, 375–385 (2012).

Where does MYC belong to?



In [1]:

    
import NotebookImport
from TCGA_analysis_BRCA_import import *
from IPython.display import HTML









    



importing IPython notebook from TCGA_analysis_BRCA_import.ipynb
Loading Focal Amplification data
Loading RNASeq data
Loading RNASeq data of controls
Loading CNV Data
Loading Somatic Mutation Data
TCGA-A8-A07C-01A not found
TCGA-BH-A0HN-01A not found
TCGA-BH-A0B8-01A not found
TCGA-BH-A0HF-01A not found
TCGA-A7-A4SC-01A not found
TCGA-AR-A0TU-01A not found
TCGA-AR-A1AT-01A not found
TCGA-E2-A1LS-01A not found
TCGA-A2-A0CZ-01A not found
TCGA-BH-A0HL-01A not found
TCGA-AN-A0G0-01A not found
TCGA-BH-A0B1-01A not found
TCGA-B6-A0I8-01A not found
TCGA-B6-A0I6-01A not found
Loading Clinical Data
Samples: 1086
  --Focal Data: 1086
  --CNV:        1086
  --RNASeq:     1081
  --Somatic:    968
  --Clinical:   1058

Which pathways are affected in MYC focally amplified samples

For all pathways described by Leiserson: Check which pathway is more often co-mutated by MYC



In [2]:

    
TP53_pathway=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK", "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668"]
STAG1_pathway=["PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A"]
MLL_pathway=["KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1"]
BAP1_pathway=["ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B"]
KRAS_pathway=["BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2"]
HLA_pathway=["B2M", "CD1D","HLA-A", "HLA-B", "P4HTM"]
HER2_pathway=["EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR"]
CDON_pathway=["BOC", "CDON"]
KEAP1_pathway=["KEAP1", "NFE2L2","CHD6", "PTMA", "WAC"]
SPOP_pathway=["MYD88", "SPOP"]
RUNX1_pathway=["CBFB", "RUNX1","ELF4"]
NOTCH_pathway=["FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH"]
ARID_pathway=["ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2"]
SMARC_pathway=["SMARCB1", "SMARCA4"]
SMC_pathway=["NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2"]
CLASP_pathway=["CLASP1","CLASP2","CLIP2"]

pathways={"TP53_pathway": TP53_pathway, "STAG1_pathway":STAG1_pathway, "MLL_pathway":MLL_pathway,"BAP1_pathway":BAP1_pathway,"KRAS_pathway":KRAS_pathway, "HLA_pathway":HLA_pathway,  "HER2_pathway":HER2_pathway, "CDON_pathway":CDON_pathway, "KEAP1_pathway":KEAP1_pathway,"SPOP_pathway":SPOP_pathway, "RUNX1_pathway":RUNX1_pathway, "NOTCH_pathway":NOTCH_pathway, "ARID_pathway":ARID_pathway, "SMARC_pathway":SMARC_pathway, "SMC_pathway":SMC_pathway,"CLASP_pathway":CLASP_pathway }

MYC_pos_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_pos_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_affected[pathway] += 1
        elif not pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_unaffected[pathway] += 1
        elif pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_affected[pathway] += 1
        elif not pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 16)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 16)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[2]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAG1_pathway 2 59 54 853 0.54 0.57 9.15 0.54 0.88 14.13 0.54 0.30 4.77
KRAS_pathway 21 40 373 534 0.75 0.35 5.55 0.75 0.88 14.06 0.75 0.19 2.97
CLASP_pathway 4 57 21 886 2.96 0.07 1.07 2.96 0.07 1.07 2.96 0.98 15.73
SMARC_pathway 0 61 9 898 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.56 8.88
SPOP_pathway 0 61 5 902 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.72 11.55
RUNX1_pathway 1 60 58 849 0.24 0.17 2.72 0.24 0.98 15.70 0.24 0.10 1.59
MLL_pathway 14 47 115 792 2.05 0.03086 0.49383 2.05 0.02357 0.37711 2.05 0.99 15.84
TP53_pathway 49 12 544 363 2.72 0.00159 0.02544 2.72 0.00085 0.01354 2.72 1.00 16.00
ARID_pathway 3 58 66 841 0.66 0.80 12.72 0.66 0.83 13.25 0.66 0.35 5.63
KEAP1_pathway 4 57 30 877 2.05 0.16 2.57 2.05 0.16 2.57 2.05 0.94 15.09
SMC_pathway 2 59 27 880 1.10 0.70 11.26 1.10 0.56 8.91 1.10 0.73 11.61
NOTCH_pathway 12 49 130 777 1.46 0.26 4.19 1.46 0.17 2.70 1.46 0.90 14.47
BAP1_pathway 5 56 58 849 1.31 0.59 9.40 1.31 0.36 5.83 1.31 0.80 12.82
CDON_pathway 2 59 16 891 1.89 0.32 5.04 1.89 0.32 5.04 1.89 0.90 14.42
HLA_pathway 1 60 27 880 0.54 1.00 16.00 0.54 0.84 13.48 0.54 0.46 7.40
HER2_pathway 18 43 167 740 1.85 0.04239 0.67832 1.85 0.02890 0.46235 1.85 0.99 15.78

Remove TP53 from TP53 pathway



In [3]:

    
TP53_pathway=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK", "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668"]
STAG1_pathway=["PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A"]
MLL_pathway=["KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1"]
BAP1_pathway=["ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B"]
KRAS_pathway=["BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2"]
HLA_pathway=["B2M", "CD1D","HLA-A", "HLA-B", "P4HTM"]
HER2_pathway=["EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR"]
CDON_pathway=["BOC", "CDON"]
KEAP1_pathway=["KEAP1", "NFE2L2","CHD6", "PTMA", "WAC"]
SPOP_pathway=["MYD88", "SPOP"]
RUNX1_pathway=["CBFB", "RUNX1","ELF4"]
NOTCH_pathway=["FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH"]
ARID_pathway=["ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2"]
SMARC_pathway=["SMARCB1", "SMARCA4"]
SMC_pathway=["NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2"]
CLASP_pathway=["CLASP1","CLASP2","CLIP2"]

pathways={"TP53_pathway": TP53_pathway, "STAG1_pathway":STAG1_pathway, "MLL_pathway":MLL_pathway,"BAP1_pathway":BAP1_pathway,"KRAS_pathway":KRAS_pathway, "HLA_pathway":HLA_pathway,  "HER2_pathway":HER2_pathway, "CDON_pathway":CDON_pathway, "KEAP1_pathway":KEAP1_pathway,"SPOP_pathway":SPOP_pathway, "RUNX1_pathway":RUNX1_pathway, "NOTCH_pathway":NOTCH_pathway, "ARID_pathway":ARID_pathway, "SMARC_pathway":SMARC_pathway, "SMC_pathway":SMC_pathway,"CLASP_pathway":CLASP_pathway }

MYC_pos_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_pos_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_affected[pathway] += 1
        elif not pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_unaffected[pathway] += 1
        elif pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_affected[pathway] += 1
        elif not pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 16)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 16)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[3]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAG1_pathway 2 59 54 853 0.54 0.57 9.15 0.54 0.88 14.13 0.54 0.30 4.77
KRAS_pathway 21 40 373 534 0.75 0.35 5.55 0.75 0.88 14.06 0.75 0.19 2.97
CLASP_pathway 4 57 21 886 2.96 0.07 1.07 2.96 0.07 1.07 2.96 0.98 15.73
SMARC_pathway 0 61 9 898 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.56 8.88
SPOP_pathway 0 61 5 902 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.72 11.55
RUNX1_pathway 1 60 58 849 0.24 0.17 2.72 0.24 0.98 15.70 0.24 0.10 1.59
MLL_pathway 14 47 115 792 2.05 0.03086 0.49383 2.05 0.02357 0.37711 2.05 0.99 15.84
TP53_pathway 34 27 401 506 1.59 0.09 1.36 1.59 0.05 0.85 1.59 0.97 15.52
ARID_pathway 3 58 66 841 0.66 0.80 12.72 0.66 0.83 13.25 0.66 0.35 5.63
KEAP1_pathway 4 57 30 877 2.05 0.16 2.57 2.05 0.16 2.57 2.05 0.94 15.09
SMC_pathway 2 59 27 880 1.10 0.70 11.26 1.10 0.56 8.91 1.10 0.73 11.61
NOTCH_pathway 12 49 130 777 1.46 0.26 4.19 1.46 0.17 2.70 1.46 0.90 14.47
BAP1_pathway 5 56 58 849 1.31 0.59 9.40 1.31 0.36 5.83 1.31 0.80 12.82
CDON_pathway 2 59 16 891 1.89 0.32 5.04 1.89 0.32 5.04 1.89 0.90 14.42
HLA_pathway 1 60 27 880 0.54 1.00 16.00 0.54 0.84 13.48 0.54 0.46 7.40
HER2_pathway 18 43 167 740 1.85 0.04239 0.67832 1.85 0.02890 0.46235 1.85 0.99 15.78

Which pathways are affected in MYC CN>4 samples

For all pathways described by Leiserson: Check which pathway is more often co-mutated by MYC



In [4]:

    
TP53_pathway=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK", "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668"]
STAG1_pathway=["PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A"]
MLL_pathway=["KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1"]
BAP1_pathway=["ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B"]
KRAS_pathway=["BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2"]
HLA_pathway=["B2M", "CD1D","HLA-A", "HLA-B", "P4HTM"]
HER2_pathway=["EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR"]
CDON_pathway=["BOC", "CDON"]
KEAP1_pathway=["KEAP1", "NFE2L2","CHD6", "PTMA", "WAC"]
SPOP_pathway=["MYD88", "SPOP"]
RUNX1_pathway=["CBFB", "RUNX1","ELF4"]
NOTCH_pathway=["FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH"]
ARID_pathway=["ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2"]
SMARC_pathway=["SMARCB1", "SMARCA4"]
SMC_pathway=["NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2"]
CLASP_pathway=["CLASP1","CLASP2","CLIP2"]

pathways={"TP53_pathway": TP53_pathway, "STAG1_pathway":STAG1_pathway, "MLL_pathway":MLL_pathway,"BAP1_pathway":BAP1_pathway,"KRAS_pathway":KRAS_pathway, "HLA_pathway":HLA_pathway,  "HER2_pathway":HER2_pathway, "CDON_pathway":CDON_pathway, "KEAP1_pathway":KEAP1_pathway,"SPOP_pathway":SPOP_pathway, "RUNX1_pathway":RUNX1_pathway, "NOTCH_pathway":NOTCH_pathway, "ARID_pathway":ARID_pathway, "SMARC_pathway":SMARC_pathway, "SMC_pathway":SMC_pathway,"CLASP_pathway":CLASP_pathway }

MYC_pos_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_pos_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    copynumber = "n/a"
    log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
    if (log2 != None):
        copynumber = 2.0 * 2.0 ** log2
    else:
        copynumber="n/a"
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if copynumber != "n/a" and pathway_affected and copynumber > 4:
            MYC_pos_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and  copynumber > 4:
            MYC_pos_pathway_unaffected[pathway] += 1
        elif copynumber != "n/a" and pathway_affected and not  copynumber > 4:
            MYC_neg_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and not  copynumber > 4:
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 16)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 16)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[4]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAG1_pathway 1 111 55 800 0.13 0.01558 0.24926 0.13 1.00 15.99 0.13 0.00721 0.11543
KRAS_pathway 42 70 351 504 0.86 0.54 8.63 0.86 0.79 12.70 0.86 0.27 4.31
CLASP_pathway 4 108 21 834 1.47 0.52 8.32 1.47 0.33 5.23 1.47 0.85 13.55
SMARC_pathway 1 111 8 847 0.95 1.00 16.00 0.95 0.67 10.74 0.95 0.72 11.52
SPOP_pathway 0 112 5 850 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.54 8.63
RUNX1_pathway 2 110 57 798 0.25 0.05 0.88 0.25 0.99 15.92 0.25 0.02352 0.37631
MLL_pathway 16 96 113 742 1.09 0.77 12.28 1.09 0.42 6.78 1.09 0.69 10.96
TP53_pathway 90 22 502 353 2.88 0.00001 0.00011 2.88 0.00000 0.00006 2.88 1.00 16.00
ARID_pathway 8 104 61 794 1.00 1.00 16.00 1.00 0.56 8.95 1.00 0.59 9.52
KEAP1_pathway 5 107 29 826 1.33 0.58 9.31 1.33 0.36 5.72 1.33 0.81 12.94
SMC_pathway 5 107 24 831 1.62 0.37 5.91 1.62 0.24 3.81 1.62 0.89 14.27
NOTCH_pathway 19 93 123 732 1.22 0.48 7.65 1.22 0.27 4.39 1.22 0.81 12.94
BAP1_pathway 8 104 55 800 1.12 0.69 11.01 1.12 0.45 7.18 1.12 0.70 11.20
CDON_pathway 3 109 15 840 1.54 0.45 7.27 1.54 0.35 5.56 1.54 0.85 13.67
HLA_pathway 3 109 25 830 0.91 1.00 16.00 0.91 0.65 10.37 0.91 0.59 9.43
HER2_pathway 29 83 156 699 1.57 0.06 0.90 1.57 0.03849 0.61581 1.57 0.98 15.64

Remove TP53 from TP53 pathway



In [5]:

    
TP53_pathway=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK", "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668"]
STAG1_pathway=["PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A"]
MLL_pathway=["KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1"]
BAP1_pathway=["ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B"]
KRAS_pathway=["BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2"]
HLA_pathway=["B2M", "CD1D","HLA-A", "HLA-B", "P4HTM"]
HER2_pathway=["EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR"]
CDON_pathway=["BOC", "CDON"]
KEAP1_pathway=["KEAP1", "NFE2L2","CHD6", "PTMA", "WAC"]
SPOP_pathway=["MYD88", "SPOP"]
RUNX1_pathway=["CBFB", "RUNX1","ELF4"]
NOTCH_pathway=["FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH"]
ARID_pathway=["ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2"]
SMARC_pathway=["SMARCB1", "SMARCA4"]
SMC_pathway=["NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2"]
CLASP_pathway=["CLASP1","CLASP2","CLIP2"]

pathways={"TP53_pathway": TP53_pathway, "STAG1_pathway":STAG1_pathway, "MLL_pathway":MLL_pathway,"BAP1_pathway":BAP1_pathway,"KRAS_pathway":KRAS_pathway, "HLA_pathway":HLA_pathway,  "HER2_pathway":HER2_pathway, "CDON_pathway":CDON_pathway, "KEAP1_pathway":KEAP1_pathway,"SPOP_pathway":SPOP_pathway, "RUNX1_pathway":RUNX1_pathway, "NOTCH_pathway":NOTCH_pathway, "ARID_pathway":ARID_pathway, "SMARC_pathway":SMARC_pathway, "SMC_pathway":SMC_pathway,"CLASP_pathway":CLASP_pathway }

MYC_pos_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_pos_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    copynumber = "n/a"
    log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
    if (log2 != None):
        copynumber = 2.0 * 2.0 ** log2
    else:
        copynumber="n/a"
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if copynumber != "n/a" and pathway_affected and copynumber > 4:
            MYC_pos_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and  copynumber > 4:
            MYC_pos_pathway_unaffected[pathway] += 1
        elif copynumber != "n/a" and pathway_affected and not  copynumber > 4:
            MYC_neg_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and not  copynumber > 4:
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 16)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 16)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[5]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAG1_pathway 1 111 55 800 0.13 0.01558 0.24926 0.13 1.00 15.99 0.13 0.00721 0.11543
KRAS_pathway 42 70 351 504 0.86 0.54 8.63 0.86 0.79 12.70 0.86 0.27 4.31
CLASP_pathway 4 108 21 834 1.47 0.52 8.32 1.47 0.33 5.23 1.47 0.85 13.55
SMARC_pathway 1 111 8 847 0.95 1.00 16.00 0.95 0.67 10.74 0.95 0.72 11.52
SPOP_pathway 0 112 5 850 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.54 8.63
RUNX1_pathway 2 110 57 798 0.25 0.05 0.88 0.25 0.99 15.92 0.25 0.02352 0.37631
MLL_pathway 16 96 113 742 1.09 0.77 12.28 1.09 0.42 6.78 1.09 0.69 10.96
TP53_pathway 59 53 375 480 1.42 0.09 1.38 1.42 0.04841 0.77451 1.42 0.97 15.50
ARID_pathway 8 104 61 794 1.00 1.00 16.00 1.00 0.56 8.95 1.00 0.59 9.52
KEAP1_pathway 5 107 29 826 1.33 0.58 9.31 1.33 0.36 5.72 1.33 0.81 12.94
SMC_pathway 5 107 24 831 1.62 0.37 5.91 1.62 0.24 3.81 1.62 0.89 14.27
NOTCH_pathway 19 93 123 732 1.22 0.48 7.65 1.22 0.27 4.39 1.22 0.81 12.94
BAP1_pathway 8 104 55 800 1.12 0.69 11.01 1.12 0.45 7.18 1.12 0.70 11.20
CDON_pathway 3 109 15 840 1.54 0.45 7.27 1.54 0.35 5.56 1.54 0.85 13.67
HLA_pathway 3 109 25 830 0.91 1.00 16.00 0.91 0.65 10.37 0.91 0.59 9.43
HER2_pathway 29 83 156 699 1.57 0.06 0.90 1.57 0.03849 0.61581 1.57 0.98 15.64

Which genes are affected in MYC focally amplified samples

Check all genes which appear in any pathway in Leiserson paper Only output genes where any p-value is lower than 0.05



In [6]:

    
genes=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK",
       "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668",
       "PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A",
       "KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1",
       "ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B",
       "BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2",
       "B2M", "CD1D","HLA-A", "HLA-B", "P4HTM",
       "EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR",
       "BOC", "CDON",
       "KEAP1", "NFE2L2","CHD6", "PTMA", "WAC",
       "MYD88", "SPOP",
       "CBFB", "RUNX1","ELF4",
       "FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH",
       "ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2",
       "SMARCB1", "SMARCA4",
       "NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2",
       "CLASP1","CLASP2","CLIP2"]

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Gene</th><th>MYC + Gene affected</th><th>MYC + Gene unaffected</th><th>MYC - Gene affected</th><th>MYC - Gene unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

gene_count = 0
for gene in genes:
    gene_count += 1
    MYC_pos_gene_affected = 0
    MYC_pos_gene_unaffected = 0
    MYC_neg_gene_affected = 0
    MYC_neg_gene_unaffected = 0

    for sample in samples.values():
        if not sample.somatic_mutation_data:
            continue
        if sample.checkFocalGeneAmp(gene) or gene in sample.genes_affected:
            if sample.checkFocalGeneAmp("MYC"):
                MYC_pos_gene_affected += 1
            else:
                MYC_neg_gene_affected += 1
        else:
            if sample.checkFocalGeneAmp("MYC"):
                MYC_pos_gene_unaffected += 1
            else:
                MYC_neg_gene_unaffected += 1

    oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]])
    oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='greater')
    oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='less')

        
    s += "<tr><th>"+gene+"</th><td>"+str(MYC_pos_gene_affected)+"</td>"
    s += "<td>"+str(MYC_pos_gene_unaffected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_affected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_unaffected)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
    if pvalue < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
    if pvalue_greater < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_greater * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_greater * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
    if pvalue_less < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_less * 134)+"</td></tr>"
    else: 
       s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_less * 134)+"</td></tr>"

s += "</table>"
print "Genes: "+str(gene_count)
h = HTML(s);h









    



Genes: 134






    Out[6]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Gene MYC + Gene affected MYC + Gene unaffected MYC - Gene affected MYC - Gene unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
CCND1 10 51 182 725 0.78 0.62 82.90 0.78 0.80 107.69 0.78 0.31 40.95
CDKN2A 1 60 4 903 3.76 0.28 37.29 3.76 0.28 37.29 3.76 0.97 129.37
CUL9 1 60 8 899 1.87 0.44 59.60 1.87 0.44 59.60 1.87 0.89 119.84
NPM1 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
PTEN 2 59 33 874 0.90 1.00 134.00 0.90 0.66 88.65 0.90 0.62 82.92
TP53 36 25 261 646 3.56 0.00000 0.00032 3.56 0.00000 0.00027 3.56 1.00 134.00
AGBL2 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
ALS2CR8 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
AMOTL1 0 61 10 897 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.52 69.67
ANKRD12 0 61 20 887 0.00 0.63 84.58 0.00 1.00 134.00 0.00 0.27 35.97
BRPF1 0 61 6 901 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.68 90.58
CACHD1 1 60 6 901 2.50 0.37 49.16 2.50 0.37 49.16 2.50 0.93 125.04
CARNS1 2 59 44 863 0.66 0.76 102.23 0.66 0.80 107.49 0.66 0.43 58.24
CDKN2AIP 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
CELSR3 0 61 12 895 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.46 61.08
CHD8 0 61 15 892 0.00 0.62 82.66 0.00 1.00 134.00 0.00 0.37 50.11
EPHA3 3 58 22 885 2.08 0.20 27.46 2.08 0.20 27.46 2.08 0.93 125.08
HECW2 2 59 13 894 2.33 0.24 32.56 2.33 0.24 32.56 2.33 0.94 125.56
IFT140 2 59 7 900 4.36 0.11 14.16 4.36 0.11 14.16 4.36 0.98 131.96
IWS1 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
MAGI2 0 61 9 898 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.56 74.40
MAST3 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
MDM4 3 58 23 884 1.99 0.22 29.72 1.99 0.22 29.72 1.99 0.92 123.94
MLL5 2 59 10 897 3.04 0.17 23.02 3.04 0.17 23.02 3.04 0.97 129.33
PLEKHA8 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
PRDM2 1 60 7 900 2.14 0.41 54.55 2.14 0.41 54.55 2.14 0.91 122.53
PRKRIR 7 54 85 822 1.25 0.51 67.77 1.25 0.36 47.93 1.25 0.79 105.25
SCAPER 0 61 10 897 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.52 69.67
SETD2 2 59 15 892 2.02 0.29 39.02 2.02 0.29 39.02 2.02 0.91 122.47
SMG1 1 60 18 889 0.82 1.00 134.00 0.82 0.71 95.56 0.82 0.66 88.55
SMG5 0 61 8 899 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.59 79.45
SMG7 0 61 8 899 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.59 79.45
SNRK 2 59 5 902 6.12 0.07 8.96 6.12 0.07 8.96 6.12 0.99 133.07
SPTBN2 2 59 41 866 0.72 1.00 134.00 0.72 0.77 103.17 0.72 0.48 64.57
STK11IP 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
STRADA 0 61 7 900 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.63 84.84
SWAP70 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
TEP1 2 59 15 892 2.02 0.29 39.02 2.02 0.29 39.02 2.02 0.91 122.47
TRIP12 0 61 8 899 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.59 79.45
TTLL5 0 61 10 897 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.52 69.67
UACA 1 60 6 901 2.50 0.37 49.16 2.50 0.37 49.16 2.50 0.93 125.04
ZFP91 0 61 10 897 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.52 69.67
ZMIZ1 4 57 22 885 2.82 0.08 10.06 2.82 0.08 10.06 2.82 0.98 131.34
ZNF227 1 60 1 906 15.10 0.12 16.36 15.10 0.12 16.36 15.10 1.00 133.48
ZNF668 1 60 4 903 3.76 0.28 37.29 3.76 0.28 37.29 3.76 0.97 129.37
PDS5B 0 61 13 894 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.43 57.18
STAG1 1 60 11 896 1.36 0.54 72.92 1.36 0.54 72.92 1.36 0.83 110.98
STAG2 0 61 13 894 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.43 57.18
WAPAL 0 61 2 905 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.88 117.64
PDS5A 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
RAD21 1 60 5 902 3.01 0.32 43.42 3.01 0.32 43.42 3.01 0.95 127.34
SMC1A 0 61 11 896 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.49 65.24
KDM6A 2 59 14 893 2.16 0.27 35.79 2.16 0.27 35.79 2.16 0.93 124.07
MLL2 1 60 22 885 0.67 1.00 134.00 0.67 0.78 104.54 0.67 0.57 76.15
MLL3 7 54 60 847 1.83 0.18 24.70 1.83 0.12 16.17 1.83 0.95 126.96
E2F3 4 57 18 889 3.47 0.04435 5.94300 3.47 0.04435 5.94300 3.47 0.99 132.71
N4BP2 0 61 12 895 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.46 61.08
PROSER1 0 61 7 900 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.63 84.84
ASXL1 1 60 5 902 3.01 0.32 43.42 3.01 0.32 43.42 3.01 0.95 127.34
ASXL2 0 61 16 891 0.00 0.62 82.69 0.00 1.00 134.00 0.00 0.35 46.90
BAP1 0 61 7 900 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.63 84.84
ANKRD17 2 59 22 885 1.36 0.66 88.37 1.36 0.45 60.81 1.36 0.81 108.77
FOXK1 0 61 1 906 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.94 125.56
FOXK2 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
KDM1B 2 59 7 900 4.36 0.11 14.16 4.36 0.11 14.16 4.36 0.98 131.96
BRAF 1 60 12 895 1.24 0.57 76.82 1.24 0.57 76.82 1.24 0.80 107.84
KRAS 2 59 31 876 0.96 1.00 134.00 0.96 0.63 84.28 0.96 0.65 87.70
PIK3CA 14 47 317 590 0.55 0.07 9.26 0.55 0.98 131.65 0.55 0.03528 4.72707
ARHGAP4 0 61 6 901 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.68 90.58
EIF2AK1 1 60 6 901 2.50 0.37 49.16 2.50 0.37 49.16 2.50 0.93 125.04
FAM118B 2 59 6 901 5.09 0.09 11.47 5.09 0.09 11.47 5.09 0.99 132.58
NRAS 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
PLCE1 1 60 15 892 0.99 1.00 134.00 0.99 0.65 87.10 0.99 0.73 98.21
RASGRF2 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
RASGRP2 1 60 6 901 2.50 0.37 49.16 2.50 0.37 49.16 2.50 0.93 125.04
RGL1 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
VARS2 0 61 1 906 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.94 125.56
B2M 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
CD1D 1 60 5 902 3.01 0.32 43.42 3.01 0.32 43.42 3.01 0.95 127.34
HLA-A 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
HLA-B 0 61 8 899 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.59 79.45
P4HTM 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
EGFR 0 61 24 883 0.00 0.39 52.79 0.00 1.00 134.00 0.00 0.21 27.56
ERBB2 15 46 120 787 2.14 0.02042 2.73654 2.14 0.01532 2.05318 2.14 0.99 133.15
AREG 2 59 10 897 3.04 0.17 23.02 3.04 0.17 23.02 3.04 0.97 129.33
ELF3 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
ERBB4 2 59 21 886 1.43 0.65 87.30 1.43 0.43 57.85 1.43 0.83 110.94
LRIG1 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
OSMR 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
BOC 0 61 10 897 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.52 69.67
CDON 2 59 7 900 4.36 0.11 14.16 4.36 0.11 14.16 4.36 0.98 131.96
KEAP1 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
NFE2L2 1 60 5 902 3.01 0.32 43.42 3.01 0.32 43.42 3.01 0.95 127.34
CHD6 2 59 18 889 1.67 0.36 48.61 1.67 0.36 48.61 1.67 0.87 117.07
PTMA 0 61 1 906 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.94 125.56
WAC 1 60 8 899 1.87 0.44 59.60 1.87 0.44 59.60 1.87 0.89 119.84
MYD88 0 61 1 906 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.94 125.56
SPOP 0 61 4 903 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.77 103.24
CBFB 0 61 23 884 0.00 0.39 52.52 0.00 1.00 134.00 0.00 0.22 29.46
RUNX1 1 60 31 876 0.47 0.72 95.92 0.47 0.88 117.89 0.47 0.39 52.02
ELF4 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
FBXW7 2 59 15 892 2.02 0.29 39.02 2.02 0.29 39.02 2.02 0.91 122.47
NOTCH1 1 60 11 896 1.36 0.54 72.92 1.36 0.54 72.92 1.36 0.83 110.98
CCNE1 8 53 37 870 3.55 0.00528 0.70689 3.55 0.00528 0.70689 3.55 1.00 133.83
DCLRE1C 1 60 7 900 2.14 0.41 54.55 2.14 0.41 54.55 2.14 0.91 122.53
DLL1 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
DTX4 0 61 8 899 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.59 79.45
FAM101A 1 60 4 903 3.76 0.28 37.29 3.76 0.28 37.29 3.76 0.97 129.37
FOXM1 1 60 6 901 2.50 0.37 49.16 2.50 0.37 49.16 2.50 0.93 125.04
JAG1 0 61 13 894 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.43 57.18
KLF5 0 61 0 907 nan 1.00 134.00 nan 1.00 134.00 nan 1.00 134.00
LFNG 0 61 1 906 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.94 125.56
LPP 0 61 6 901 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.68 90.58
MAML1 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
MMS22L 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
NOTCH3 0 61 14 893 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.40 53.53
NOTCH4 0 61 10 897 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.52 69.67
SHPRH 0 61 11 896 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.49 65.24
ARID1A 0 61 27 880 0.00 0.41 54.58 0.00 1.00 134.00 0.00 0.17 22.55
PBRM1 1 60 6 901 2.50 0.37 49.16 2.50 0.37 49.16 2.50 0.93 125.04
ADNP 1 60 14 893 1.06 1.00 134.00 1.06 0.63 83.89 1.06 0.76 101.44
ARID1B 1 60 15 892 0.99 1.00 134.00 0.99 0.65 87.10 0.99 0.73 98.21
ARID2 0 61 11 896 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.49 65.24
SMARCB1 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
SMARCA4 0 61 7 900 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.63 84.84
NCAPD3 0 61 11 896 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.49 65.24
SMC4 0 61 8 899 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.59 79.45
NCAPG2 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
NCAPH2 0 61 3 904 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.82 110.21
SMC2 0 61 5 902 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.72 96.71
NCAPD2 2 59 3 904 10.21 0.03454 4.62781 10.21 0.03454 4.62781 10.21 1.00 133.71
CLASP1 1 60 8 899 1.87 0.44 59.60 1.87 0.44 59.60 1.87 0.89 119.84
CLASP2 1 60 7 900 2.14 0.41 54.55 2.14 0.41 54.55 2.14 0.91 122.53
CLIP2 2 59 7 900 4.36 0.11 14.16 4.36 0.11 14.16 4.36 0.98 131.96

Which genes are affected in MYC Copynumber > 4 samples



In [7]:

    
genes=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK",
       "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668",
       "PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A",
       "KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1",
       "ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B",
       "BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2",
       "B2M", "CD1D","HLA-A", "HLA-B", "P4HTM",
       "EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR",
       "BOC", "CDON",
       "KEAP1", "NFE2L2","CHD6", "PTMA", "WAC",
       "MYD88", "SPOP",
       "CBFB", "RUNX1","ELF4",
       "FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH",
       "ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2",
       "SMARCB1", "SMARCA4",
       "NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2",
       "CLASP1","CLASP2","CLIP2"]

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Gene</th><th>MYC + Gene affected</th><th>MYC + Gene unaffected</th><th>MYC - Gene affected</th><th>MYC - Gene unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

gene_count = 0
for gene in genes:
    gene_count += 1
    MYC_pos_gene_affected = 0
    MYC_pos_gene_unaffected = 0
    MYC_neg_gene_affected = 0
    MYC_neg_gene_unaffected = 0

    for sample in samples.values():
        if not sample.somatic_mutation_data:
            continue
        copynumber = "n/a"
        log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
        if (log2 != None):
            copynumber = 2.0 * 2.0 ** log2
        else:
            copynumber="n/a"
        if sample.checkFocalGeneAmp(gene) or gene in sample.genes_affected:
            if copynumber != "n/a" and copynumber > 4:
                MYC_pos_gene_affected += 1
            elif  copynumber != "n/a" and not copynumber > 4:
                MYC_neg_gene_affected += 1
        else:
            if copynumber != "n/a" and copynumber > 4:
                MYC_pos_gene_unaffected += 1
            elif  copynumber != "n/a" and not copynumber > 4:
                MYC_neg_gene_unaffected += 1

    oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]])
    oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='greater')
    oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='less')

    #if (pvalue > 0.05 and pvalue_greater > 0.05 and pvalue_less > 0.05):
        #continue
        
    s += "<tr><th>"+gene+"</th><td>"+str(MYC_pos_gene_affected)+"</td>"
    s += "<td>"+str(MYC_pos_gene_unaffected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_affected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_unaffected)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
    if pvalue < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
    if pvalue_greater < 0.0005: 
       s += "<td>"+"{:.10f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.10f}".format(pvalue_greater * 134)+"</td>"
    elif pvalue_greater < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_greater * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_greater * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
    if pvalue_less < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_less * 134)+"</td></tr>"
    else: 
       s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_less * 134)+"</td></tr>"

s += "</table>"
print "Genes: "+str(gene_count)
h = HTML(s);h









    



Genes: 134






    Out[7]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Gene MYC + Gene affected MYC + Gene unaffected MYC - Gene affected MYC - Gene unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
CCND1 27 85 165 690 1.33 0.26 34.35 1.33 0.14 19.02 1.33 0.91 121.33
CDKN2A 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
CUL9 0 112 9 846 0.00 0.61 81.59 0.00 1.00 134.00 0.00 0.33 44.04
NPM1 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
PTEN 3 109 32 823 0.71 0.79 105.69 0.71 0.79 106.22 0.71 0.41 54.53
TP53 63 49 233 622 3.43 0.00000 0.00000 3.43 0.0000000017 0.0000002249 3.43 1.00 134.00
AGBL2 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
ALS2CR8 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
AMOTL1 2 110 8 847 1.93 0.33 43.62 1.93 0.33 43.62 1.93 0.90 120.74
ANKRD12 0 112 20 835 0.00 0.15 20.66 0.00 1.00 134.00 0.00 0.08 11.13
BRPF1 1 111 5 850 1.53 0.52 70.11 1.53 0.52 70.11 1.53 0.85 114.41
CACHD1 1 111 6 849 1.27 0.58 77.55 1.27 0.58 77.55 1.27 0.81 108.57
CARNS1 5 107 41 814 0.93 1.00 134.00 0.93 0.63 84.90 0.93 0.55 74.28
CDKN2AIP 2 110 1 854 15.53 0.03689 4.94355 15.53 0.03689 4.94355 15.53 1.00 133.80
CELSR3 1 111 11 844 0.69 1.00 134.00 0.69 0.77 103.68 0.69 0.59 78.59
CHD8 0 112 15 840 0.00 0.24 32.22 0.00 1.00 134.00 0.00 0.16 20.84
EPHA3 5 107 20 835 1.95 0.20 26.47 1.95 0.15 20.55 1.95 0.94 126.08
HECW2 2 110 13 842 1.18 0.69 92.36 1.18 0.53 71.52 1.18 0.75 100.90
IFT140 2 110 7 848 2.20 0.28 37.55 2.20 0.28 37.55 2.20 0.92 123.89
IWS1 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
MAGI2 0 112 9 846 0.00 0.61 81.59 0.00 1.00 134.00 0.00 0.33 44.04
MAST3 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
MDM4 5 107 21 834 1.86 0.21 28.44 1.86 0.17 23.22 1.86 0.93 124.65
MLL5 3 109 9 846 2.59 0.15 20.53 2.59 0.15 20.53 2.59 0.96 128.58
PLEKHA8 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
PRDM2 0 112 8 847 0.00 0.61 81.32 0.00 1.00 134.00 0.00 0.37 49.86
PRKRIR 13 99 79 776 1.29 0.39 52.91 1.29 0.26 34.42 1.29 0.84 112.01
SCAPER 1 111 9 846 0.85 1.00 134.00 0.85 0.71 95.11 0.85 0.67 90.38
SETD2 2 110 15 840 1.02 1.00 134.00 1.02 0.60 80.96 1.02 0.69 91.97
SMG1 2 110 17 838 0.90 1.00 134.00 0.90 0.67 89.28 0.90 0.62 82.97
SMG5 1 111 7 848 1.09 1.00 134.00 1.09 0.63 84.14 1.09 0.77 102.55
SMG7 0 112 7 848 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.42 56.45
SNRK 2 110 5 850 3.09 0.19 25.43 3.09 0.19 25.43 3.09 0.96 128.99
SPTBN2 3 109 40 815 0.56 0.47 62.42 0.56 0.89 119.82 0.56 0.24 32.75
STK11IP 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
STRADA 1 111 6 849 1.27 0.58 77.55 1.27 0.58 77.55 1.27 0.81 108.57
SWAP70 0 112 3 852 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.69 92.59
TEP1 3 109 14 841 1.65 0.43 58.26 1.65 0.31 42.03 1.65 0.88 117.42
TRIP12 1 111 7 848 1.09 1.00 134.00 1.09 0.63 84.14 1.09 0.77 102.55
TTLL5 1 111 9 846 0.85 1.00 134.00 0.85 0.71 95.11 0.85 0.67 90.38
UACA 2 110 5 850 3.09 0.19 25.43 3.09 0.19 25.43 3.09 0.96 128.99
ZFP91 0 112 10 845 0.00 0.62 82.51 0.00 1.00 134.00 0.00 0.29 38.89
ZMIZ1 7 105 19 836 2.93 0.02338 3.13247 2.93 0.02338 3.13247 2.93 0.99 133.12
ZNF227 1 111 1 854 7.69 0.22 29.26 7.69 0.22 29.26 7.69 0.99 132.22
ZNF668 1 111 4 851 1.92 0.46 61.69 1.92 0.46 61.69 1.92 0.89 119.90
PDS5B 0 112 13 842 0.00 0.38 51.31 0.00 1.00 134.00 0.00 0.20 26.76
STAG1 0 112 12 843 0.00 0.38 50.85 0.00 1.00 134.00 0.00 0.23 30.32
STAG2 0 112 13 842 0.00 0.38 51.31 0.00 1.00 134.00 0.00 0.20 26.76
WAPAL 0 112 2 853 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.78 104.74
PDS5A 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
RAD21 1 111 5 850 1.53 0.52 70.11 1.53 0.52 70.11 1.53 0.85 114.41
SMC1A 0 112 11 844 0.00 0.63 83.94 0.00 1.00 134.00 0.00 0.26 34.34
KDM6A 3 109 13 842 1.78 0.42 55.93 1.78 0.28 37.54 1.78 0.90 120.14
MLL2 1 111 22 833 0.34 0.50 67.62 0.34 0.94 126.37 0.34 0.23 31.22
MLL3 8 104 59 796 1.04 0.84 113.19 1.04 0.52 70.13 1.04 0.63 84.46
E2F3 4 108 18 837 1.72 0.31 41.45 1.72 0.24 32.80 1.72 0.90 120.57
N4BP2 0 112 12 843 0.00 0.38 50.85 0.00 1.00 134.00 0.00 0.23 30.32
PROSER1 0 112 7 848 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.42 56.45
ASXL1 1 111 5 850 1.53 0.52 70.11 1.53 0.52 70.11 1.53 0.85 114.41
ASXL2 2 110 14 841 1.09 0.71 94.77 1.09 0.57 76.38 1.09 0.72 96.46
BAP1 0 112 7 848 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.42 56.45
ANKRD17 2 110 22 833 0.69 1.00 134.00 0.69 0.79 105.56 0.69 0.46 61.72
FOXK1 1 111 0 855 inf 0.12 15.52 inf 0.12 15.52 inf 1.00 134.00
FOXK2 1 111 3 852 2.56 0.39 52.17 2.56 0.39 52.17 2.56 0.93 124.86
KDM1B 1 111 8 847 0.95 1.00 134.00 0.95 0.67 89.96 0.95 0.72 96.45
BRAF 2 110 11 844 1.40 0.66 87.78 1.40 0.46 61.02 1.40 0.82 109.45
KRAS 7 105 26 829 2.13 0.09 12.41 2.13 0.08 10.26 2.13 0.97 130.11
PIK3CA 29 83 302 553 0.64 0.06 7.54 0.64 0.98 131.71 0.64 0.02888 3.87038
ARHGAP4 1 111 5 850 1.53 0.52 70.11 1.53 0.52 70.11 1.53 0.85 114.41
EIF2AK1 2 110 5 850 3.09 0.19 25.43 3.09 0.19 25.43 3.09 0.96 128.99
FAM118B 2 110 6 849 2.57 0.23 31.45 2.57 0.23 31.45 2.57 0.95 126.65
NRAS 0 112 3 852 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.69 92.59
PLCE1 1 111 15 840 0.50 1.00 134.00 0.50 0.86 115.61 0.50 0.43 57.62
RASGRF2 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
RASGRP2 1 111 6 849 1.27 0.58 77.55 1.27 0.58 77.55 1.27 0.81 108.57
RGL1 0 112 2 853 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.78 104.74
VARS2 0 112 1 854 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.88 118.48
B2M 1 111 4 851 1.92 0.46 61.69 1.92 0.46 61.69 1.92 0.89 119.90
CD1D 0 112 6 849 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.48 63.89
HLA-A 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
HLA-B 1 111 7 848 1.09 1.00 134.00 1.09 0.63 84.14 1.09 0.77 102.55
P4HTM 1 111 4 851 1.92 0.46 61.69 1.92 0.46 61.69 1.92 0.89 119.90
EGFR 7 105 17 838 3.29 0.01498 2.00720 3.29 0.01498 2.00720 3.29 1.00 133.49
ERBB2 19 93 116 739 1.30 0.31 41.96 1.30 0.20 26.90 1.30 0.87 116.27
AREG 3 109 9 846 2.59 0.15 20.53 2.59 0.15 20.53 2.59 0.96 128.58
ELF3 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
ERBB4 1 111 22 833 0.34 0.50 67.62 0.34 0.94 126.37 0.34 0.23 31.22
LRIG1 1 111 4 851 1.92 0.46 61.69 1.92 0.46 61.69 1.92 0.89 119.90
OSMR 1 111 4 851 1.92 0.46 61.69 1.92 0.46 61.69 1.92 0.89 119.90
BOC 1 111 9 846 0.85 1.00 134.00 0.85 0.71 95.11 0.85 0.67 90.38
CDON 2 110 7 848 2.20 0.28 37.55 2.20 0.28 37.55 2.20 0.92 123.89
KEAP1 0 112 3 852 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.69 92.59
NFE2L2 2 110 4 851 3.87 0.15 19.59 3.87 0.15 19.59 3.87 0.98 130.88
CHD6 3 109 17 838 1.36 0.50 66.60 1.36 0.41 55.47 1.36 0.81 108.13
PTMA 0 112 1 854 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.88 118.48
WAC 0 112 9 846 0.00 0.61 81.59 0.00 1.00 134.00 0.00 0.33 44.04
MYD88 0 112 1 854 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.88 118.48
SPOP 0 112 4 851 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.61 81.83
CBFB 1 111 22 833 0.34 0.50 67.62 0.34 0.94 126.37 0.34 0.23 31.22
RUNX1 1 111 31 824 0.24 0.16 21.94 0.24 0.98 131.56 0.24 0.10 13.03
ELF4 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
FBXW7 2 110 15 840 1.02 1.00 134.00 1.02 0.60 80.96 1.02 0.69 91.97
NOTCH1 3 109 9 846 2.59 0.15 20.53 2.59 0.15 20.53 2.59 0.96 128.58
CCNE1 7 105 38 817 1.43 0.35 46.47 1.43 0.26 34.59 1.43 0.86 115.44
DCLRE1C 2 110 6 849 2.57 0.23 31.45 2.57 0.23 31.45 2.57 0.95 126.65
DLL1 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
DTX4 1 111 7 848 1.09 1.00 134.00 1.09 0.63 84.14 1.09 0.77 102.55
FAM101A 1 111 4 851 1.92 0.46 61.69 1.92 0.46 61.69 1.92 0.89 119.90
FOXM1 1 111 6 849 1.27 0.58 77.55 1.27 0.58 77.55 1.27 0.81 108.57
JAG1 2 110 11 844 1.40 0.66 87.78 1.40 0.46 61.02 1.40 0.82 109.45
KLF5 0 112 0 855 nan 1.00 134.00 nan 1.00 134.00 nan 1.00 134.00
LFNG 0 112 1 854 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.88 118.48
LPP 0 112 6 849 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.48 63.89
MAML1 0 112 3 852 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.69 92.59
MMS22L 0 112 5 850 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.54 72.31
NOTCH3 3 109 11 844 2.11 0.21 28.75 2.11 0.21 28.75 2.11 0.93 124.87
NOTCH4 0 112 10 845 0.00 0.62 82.51 0.00 1.00 134.00 0.00 0.29 38.89
SHPRH 0 112 11 844 0.00 0.63 83.94 0.00 1.00 134.00 0.00 0.26 34.34
ARID1A 2 110 25 830 0.60 0.76 101.83 0.60 0.84 112.63 0.60 0.38 50.54
PBRM1 1 111 6 849 1.27 0.58 77.55 1.27 0.58 77.55 1.27 0.81 108.57
ADNP 1 111 14 841 0.54 1.00 134.00 0.54 0.84 113.16 0.54 0.47 62.48
ARID1B 3 109 13 842 1.78 0.42 55.93 1.78 0.28 37.54 1.78 0.90 120.14
ARID2 1 111 10 845 0.76 1.00 134.00 0.76 0.74 99.66 0.76 0.63 84.40
SMARCB1 1 111 2 853 3.84 0.31 41.41 3.84 0.31 41.41 3.84 0.96 129.06
SMARCA4 0 112 7 848 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.42 56.45
NCAPD3 2 110 9 846 1.71 0.37 49.60 1.71 0.37 49.60 1.71 0.87 117.25
SMC4 0 112 8 847 0.00 0.61 81.32 0.00 1.00 134.00 0.00 0.37 49.86
NCAPG2 0 112 3 852 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.69 92.59
NCAPH2 0 112 3 852 0.00 1.00 134.00 0.00 1.00 134.00 0.00 0.69 92.59
SMC2 1 111 4 851 1.92 0.46 61.69 1.92 0.46 61.69 1.92 0.89 119.90
NCAPD2 2 110 3 852 5.16 0.11 14.10 5.16 0.11 14.10 5.16 0.99 132.30
CLASP1 1 111 8 847 0.95 1.00 134.00 0.95 0.67 89.96 0.95 0.72 96.45
CLASP2 2 110 6 849 2.57 0.23 31.45 2.57 0.23 31.45 2.57 0.95 126.65
CLIP2 1 111 8 847 0.95 1.00 134.00 0.95 0.67 89.96 0.95 0.72 96.45

Which pathways are affected in MYC positive samples (Vogelstein)

This time use pathway definitions of Vogelstein et al.

Note that MYC has been deleted from pathway: Cell Cycle / Apoptosis



In [8]:

    
chromatin_modification_pathway=["ARID1A","ARID1B","ARID2","ASXL1","ATRX","CREBBP","DAXX","DNMT1","DNMT3A","EP300","EZH2","H3H3A","HIST1H3B","IDH1","IDH2","KDM5C","KDM6A","MEN1","MLL2","MLL3","NCOR1","PAX5","PBRM1","PRDM1","SETD2","SETBP1","SMARCA4","SMARCB1","SPOP","TET2","WT1","NCOA3"]
cell_cycle_apoptosis_pathway=["ABL1","BCL2","CARD1","CASP8","CDC73","CDKN2A","CYLD","DAXX","FUBP1","MED12","MYD88","NFE2L2","NPM2","PPP2R1A","RB1","TNFAIP3","TRAF7","TP53","CCND1","CDKN2C","MDM2","MDM4","MYCL1","MYCN","SKP2"]
tgf_beta_pathway=["ACVR1B","EP300","FOXL2","GATA1","GATA2","GNAS","MED12","SMAD2","SMAD4"]
PI3K_pathway=["AKT1","ALK","B2M","CBL","CEBPA","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","KIT","MET","PDGFRA","PIK2CA","PIK3R1","PTEN","RET","TSC1","TSHR","VHL","NCOA3"]
RAS_pathway=["ALK","B2M","BRAF","CBL","CEBPA","CIC","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","HRAS","KIT","KRAS","MAP2K1","MAP3K1","MET","NF1","NRAS","PDGFRA","PTPN11","RET","VHL"]
APC_pathway=["AXIN1","CDH1","CTNNB1","EP300","FAM123B","GNAS","HNF1A","NF2","RNF43","SOX9"]
transcription_regulation_pathway=["AR","BCOR","CREBBP","GATA3","KLF4","PHF6","RUNX1","SF3B1","SRSF2","U2AF1","IKZF1","LMO1"]
dna_damage_pathway=["ATM","BAP1","BRCA1","BRCA2","MLH1","MSH2","MSH6","STAG2","TP53"]
mapkinase_pathway=["B2M","CEBPA","GNA11","GNAQ","MAP3K1","TNFAIP3","TSHR","MAP2K4","NKX2-1"]
STAT_pathway=["CLRF","FGFR2","FGFR3","FLT3","JAK1","JAK2","JAK3","STAT","MPL","SOCS1","VHL"]
NOTCH_pathway=["EP300","FBXW7","GATA1","GATA2","NOTCH1","NOTCH2"]
wnt_pathway=["KLF4"]
hedgehog_pathway=["PTCH1","SMO","SPOP"]
mTOR_pathway=["STK11"]
replicationpathway=["SETBP1"]

pathways={"Chromatin Modification": chromatin_modification_pathway, "Cell Cycle\\Apoptosis":cell_cycle_apoptosis_pathway, 
        "TGF beta":tgf_beta_pathway,"PI3K":PI3K_pathway,"RAS":RAS_pathway, "APC":APC_pathway,  
        "Transcription Regulation":transcription_regulation_pathway, 
        "DNA Damage Repair":dna_damage_pathway, "MAP Kinase":mapkinase_pathway,
        "STAT":STAT_pathway, "NOTCH":NOTCH_pathway, 
        "WNT":wnt_pathway, "Hedgehog":hedgehog_pathway, 
        "mTOR":mTOR_pathway, "Replication":replicationpathway}

MYC_pos_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_pos_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,  
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0, 
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_affected[pathway] += 1
        elif not pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_unaffected[pathway] += 1
        elif pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_affected[pathway] += 1
        elif not pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.0005: 
      s += "<td>"+"{:.10f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.10f}".format(pvalue_greater * 15)+"</td>"
   elif pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 15)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 15)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[8]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAT 13 48 133 774 1.58 0.19 2.89 1.58 0.11 1.71 1.58 0.94 14.08
Chromatin Modification 28 33 268 639 2.02 0.00952 0.14282 2.02 0.00668 0.10020 2.02 1.00 14.95
PI3K 34 27 308 599 2.45 0.00082 0.01230 2.45 0.00061 0.00919 2.45 1.00 15.00
Transcription Regulation 12 49 175 732 1.02 1.00 15.00 1.02 0.53 7.89 1.02 0.61 9.08
NOTCH 5 56 58 849 1.31 0.59 8.82 1.31 0.36 5.47 1.31 0.80 12.02
DNA Damage Repair 42 19 311 596 4.24 0.00000 0.00000 4.24 0.0000001131 0.0000016969 4.24 1.00 15.00
TGF beta 3 58 70 837 0.62 0.62 9.23 0.62 0.86 12.85 0.62 0.31 4.61
MAP Kinase 10 51 141 766 1.07 0.86 12.83 1.07 0.49 7.31 1.07 0.65 9.78
Cell Cycle\Apoptosis 47 14 478 429 3.01 0.00018 0.00263 3.01 0.0001258500 0.0018877493 3.01 1.00 15.00
Replication 0 61 13 894 0.00 1.00 15.00 0.00 1.00 15.00 0.00 0.43 6.40
mTOR 1 60 1 906 15.10 0.12 1.83 15.10 0.12 1.83 15.10 1.00 14.94
RAS 29 32 362 545 1.36 0.28 4.21 1.36 0.15 2.24 1.36 0.90 13.56
WNT 0 61 2 905 0.00 1.00 15.00 0.00 1.00 15.00 0.00 0.88 13.17
APC 8 53 156 751 0.73 0.48 7.26 0.73 0.84 12.62 0.73 0.27 3.98
Hedgehog 2 59 27 880 1.10 0.70 10.56 1.10 0.56 8.35 1.10 0.73 10.89

Remove TP53 from Pathways: Cell Cycle/Apoptosis and DNA Damaga Control



In [9]:

    
chromatin_modification_pathway=["ARID1A","ARID1B","ARID2","ASXL1","ATRX","CREBBP","DAXX","DNMT1","DNMT3A","EP300","EZH2","H3H3A","HIST1H3B","IDH1","IDH2","KDM5C","KDM6A","MEN1","MLL2","MLL3","NCOR1","PAX5","PBRM1","PRDM1","SETD2","SETBP1","SMARCA4","SMARCB1","SPOP","TET2","WT1","NCOA3"]
cell_cycle_apoptosis_pathway=["ABL1","BCL2","CARD1","CASP8","CDC73","CDKN2A","CYLD","DAXX","FUBP1","MED12","MYD88","NFE2L2","NPM2","PPP2R1A","RB1","TNFAIP3","TRAF7","CCND1","CDKN2C","MDM2","MDM4","MYCL1","MYCN","SKP2"]
tgf_beta_pathway=["ACVR1B","EP300","FOXL2","GATA1","GATA2","GNAS","MED12","SMAD2","SMAD4"]
PI3K_pathway=["AKT1","ALK","B2M","CBL","CEBPA","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","KIT","MET","PDGFRA","PIK2CA","PIK3R1","PTEN","RET","TSC1","TSHR","VHL","NCOA3"]
RAS_pathway=["ALK","B2M","BRAF","CBL","CEBPA","CIC","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","HRAS","KIT","KRAS","MAP2K1","MAP3K1","MET","NF1","NRAS","PDGFRA","PTPN11","RET","VHL"]
APC_pathway=["AXIN1","CDH1","CTNNB1","EP300","FAM123B","GNAS","HNF1A","NF2","RNF43","SOX9"]
transcription_regulation_pathway=["AR","BCOR","CREBBP","GATA3","KLF4","PHF6","RUNX1","SF3B1","SRSF2","U2AF1","IKZF1","LMO1"]
dna_damage_pathway=["ATM","BAP1","BRCA1","BRCA2","MLH1","MSH2","MSH6","STAG2"]
mapkinase_pathway=["B2M","CEBPA","GNA11","GNAQ","MAP3K1","TNFAIP3","TSHR","MAP2K4","NKX2-1"]
STAT_pathway=["CLRF","FGFR2","FGFR3","FLT3","JAK1","JAK2","JAK3","STAT","MPL","SOCS1","VHL"]
NOTCH_pathway=["EP300","FBXW7","GATA1","GATA2","NOTCH1","NOTCH2"]
wnt_pathway=["KLF4"]
hedgehog_pathway=["PTCH1","SMO","SPOP"]
mTOR_pathway=["STK11"]
replicationpathway=["SETBP1"]

pathways={"Chromatin Modification": chromatin_modification_pathway, "Cell Cycle\\Apoptosis":cell_cycle_apoptosis_pathway, 
        "TGF beta":tgf_beta_pathway,"PI3K":PI3K_pathway,"RAS":RAS_pathway, "APC":APC_pathway,  
        "Transcription Regulation":transcription_regulation_pathway, 
        "DNA Damage Repair":dna_damage_pathway, "MAP Kinase":mapkinase_pathway,
        "STAT":STAT_pathway, "NOTCH":NOTCH_pathway, 
        "WNT":wnt_pathway, "Hedgehog":hedgehog_pathway, 
        "mTOR":mTOR_pathway, "Replication":replicationpathway}

MYC_pos_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_pos_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,  
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0, 
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_affected[pathway] += 1
        elif not pathway_affected and sample.checkFocalGeneAmp("MYC"):
            MYC_pos_pathway_unaffected[pathway] += 1
        elif pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_affected[pathway] += 1
        elif not pathway_affected and not sample.checkFocalGeneAmp("MYC"):
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 15)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 15)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[9]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAT 13 48 133 774 1.58 0.19 2.89 1.58 0.11 1.71 1.58 0.94 14.08
Chromatin Modification 28 33 268 639 2.02 0.00952 0.14282 2.02 0.00668 0.10020 2.02 1.00 14.95
PI3K 34 27 308 599 2.45 0.00082 0.01230 2.45 0.00061 0.00919 2.45 1.00 15.00
Transcription Regulation 12 49 175 732 1.02 1.00 15.00 1.02 0.53 7.89 1.02 0.61 9.08
NOTCH 5 56 58 849 1.31 0.59 8.82 1.31 0.36 5.47 1.31 0.80 12.02
DNA Damage Repair 9 52 74 833 1.95 0.09 1.40 1.95 0.07 1.03 1.95 0.97 14.56
TGF beta 3 58 70 837 0.62 0.62 9.23 0.62 0.86 12.85 0.62 0.31 4.61
MAP Kinase 10 51 141 766 1.07 0.86 12.83 1.07 0.49 7.31 1.07 0.65 9.78
Cell Cycle\Apoptosis 26 35 317 590 1.38 0.27 4.02 1.38 0.14 2.12 1.38 0.91 13.66
Replication 0 61 13 894 0.00 1.00 15.00 0.00 1.00 15.00 0.00 0.43 6.40
mTOR 1 60 1 906 15.10 0.12 1.83 15.10 0.12 1.83 15.10 1.00 14.94
RAS 29 32 362 545 1.36 0.28 4.21 1.36 0.15 2.24 1.36 0.90 13.56
WNT 0 61 2 905 0.00 1.00 15.00 0.00 1.00 15.00 0.00 0.88 13.17
APC 8 53 156 751 0.73 0.48 7.26 0.73 0.84 12.62 0.73 0.27 3.98
Hedgehog 2 59 27 880 1.10 0.70 10.56 1.10 0.56 8.35 1.10 0.73 10.89

Which pathways are affected in MYC copynumber > 4 samples (Vogelstein)



In [10]:

    
chromatin_modification_pathway=["ARID1A","ARID1B","ARID2","ASXL1","ATRX","CREBBP","DAXX","DNMT1","DNMT3A","EP300","EZH2","H3H3A","HIST1H3B","IDH1","IDH2","KDM5C","KDM6A","MEN1","MLL2","MLL3","NCOR1","PAX5","PBRM1","PRDM1","SETD2","SETBP1","SMARCA4","SMARCB1","SPOP","TET2","WT1","NCOA3"]
cell_cycle_apoptosis_pathway=["ABL1","BCL2","CARD1","CASP8","CDC73","CDKN2A","CYLD","DAXX","FUBP1","MED12","MYD88","NFE2L2","NPM2","PPP2R1A","RB1","TNFAIP3","TRAF7","TP53","CCND1","CDKN2C","MDM2","MDM4","MYCL1","MYCN","SKP2"]
tgf_beta_pathway=["ACVR1B","EP300","FOXL2","GATA1","GATA2","GNAS","MED12","SMAD2","SMAD4"]
PI3K_pathway=["AKT1","ALK","B2M","CBL","CEBPA","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","KIT","MET","PDGFRA","PIK2CA","PIK3R1","PTEN","RET","TSC1","TSHR","VHL","NCOA3"]
RAS_pathway=["ALK","B2M","BRAF","CBL","CEBPA","CIC","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","HRAS","KIT","KRAS","MAP2K1","MAP3K1","MET","NF1","NRAS","PDGFRA","PTPN11","RET","VHL"]
APC_pathway=["AXIN1","CDH1","CTNNB1","EP300","FAM123B","GNAS","HNF1A","NF2","RNF43","SOX9"]
transcription_regulation_pathway=["AR","BCOR","CREBBP","GATA3","KLF4","PHF6","RUNX1","SF3B1","SRSF2","U2AF1","IKZF1","LMO1"]
dna_damage_pathway=["ATM","BAP1","BRCA1","BRCA2","MLH1","MSH2","MSH6","STAG2","TP53"]
mapkinase_pathway=["B2M","CEBPA","GNA11","GNAQ","MAP3K1","TNFAIP3","TSHR","MAP2K4","NKX2-1"]
STAT_pathway=["CLRF","FGFR2","FGFR3","FLT3","JAK1","JAK2","JAK3","STAT","MPL","SOCS1","VHL"]
NOTCH_pathway=["EP300","FBXW7","GATA1","GATA2","NOTCH1","NOTCH2"]
wnt_pathway=["KLF4"]
hedgehog_pathway=["PTCH1","SMO","SPOP"]
mTOR_pathway=["STK11"]
replicationpathway=["SETBP1"]

pathways={"Chromatin Modification": chromatin_modification_pathway, "Cell Cycle\\Apoptosis":cell_cycle_apoptosis_pathway, 
        "TGF beta":tgf_beta_pathway,"PI3K":PI3K_pathway,"RAS":RAS_pathway, "APC":APC_pathway,  
        "Transcription Regulation":transcription_regulation_pathway, 
        "DNA Damage Repair":dna_damage_pathway, "MAP Kinase":mapkinase_pathway,
        "STAT":STAT_pathway, "NOTCH":NOTCH_pathway, 
        "WNT":wnt_pathway, "Hedgehog":hedgehog_pathway, 
        "mTOR":mTOR_pathway, "Replication":replicationpathway}

MYC_pos_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_pos_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,  
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0, 
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    copynumber = "n/a"
    log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
    if (log2 != None):
        copynumber = 2.0 * 2.0 ** log2
    else:
        copynumber="n/a"
    if copynumber == "n/a":
        continue
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if pathway_affected and copynumber > 4:
            MYC_pos_pathway_affected[pathway] += 1
        elif not pathway_affected and copynumber > 4:
            MYC_pos_pathway_unaffected[pathway] += 1
        elif pathway_affected and not copynumber > 4:
            MYC_neg_pathway_affected[pathway] += 1
        elif not pathway_affected and not copynumber > 4:
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.0005: 
      s += "<td>"+"{:.10f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.10f}".format(pvalue_greater * 15)+"</td>"
   elif pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 15)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 15)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[10]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAT 21 91 125 730 1.35 0.26 3.92 1.35 0.16 2.35 1.35 0.90 13.48
Chromatin Modification 47 65 249 606 1.76 0.00857 0.12859 1.76 0.00451 0.06767 1.76 1.00 14.96
PI3K 49 63 293 562 1.49 0.06 0.87 1.49 0.03201 0.48010 1.49 0.98 14.70
Transcription Regulation 18 94 168 687 0.78 0.44 6.66 0.78 0.85 12.74 0.78 0.22 3.32
NOTCH 10 102 53 802 1.48 0.31 4.58 1.48 0.18 2.73 1.48 0.90 13.50
DNA Damage Repair 68 44 284 571 3.11 0.00000 0.00000 3.11 0.0000000228 0.0000003415 3.11 1.00 15.00
TGF beta 11 101 62 793 1.39 0.34 5.11 1.39 0.21 3.20 1.39 0.87 13.12
MAP Kinase 18 94 133 722 1.04 0.89 13.35 1.04 0.49 7.33 1.04 0.62 9.28
Cell Cycle\Apoptosis 87 25 437 418 3.33 0.00000 0.00000 3.33 0.0000000420 0.0000006296 3.33 1.00 15.00
Replication 1 111 12 843 0.63 1.00 15.00 0.63 0.80 12.00 0.63 0.54 8.17
mTOR 1 111 1 854 7.69 0.22 3.28 7.69 0.22 3.28 7.69 0.99 14.80
RAS 52 60 338 517 1.33 0.18 2.74 1.33 0.10 1.47 1.33 0.93 13.99
WNT 0 112 2 853 0.00 1.00 15.00 0.00 1.00 15.00 0.00 0.78 11.72
APC 15 97 149 706 0.73 0.35 5.23 0.73 0.89 13.32 0.73 0.18 2.63
Hedgehog 4 108 25 830 1.23 0.77 11.48 1.23 0.44 6.57 1.23 0.76 11.43

Remove TP53 from pathways Cell Cycle / Apoptosis and DNA Damage repair



In [11]:

    
chromatin_modification_pathway=["ARID1A","ARID1B","ARID2","ASXL1","ATRX","CREBBP","DAXX","DNMT1","DNMT3A","EP300","EZH2","H3H3A","HIST1H3B","IDH1","IDH2","KDM5C","KDM6A","MEN1","MLL2","MLL3","NCOR1","PAX5","PBRM1","PRDM1","SETD2","SETBP1","SMARCA4","SMARCB1","SPOP","TET2","WT1","NCOA3"]
cell_cycle_apoptosis_pathway=["ABL1","BCL2","CARD1","CASP8","CDC73","CDKN2A","CYLD","DAXX","FUBP1","MED12","MYD88","NFE2L2","NPM2","PPP2R1A","RB1","TNFAIP3","TRAF7","CCND1","CDKN2C","MDM2","MDM4","MYCL1","MYCN","SKP2"]
tgf_beta_pathway=["ACVR1B","EP300","FOXL2","GATA1","GATA2","GNAS","MED12","SMAD2","SMAD4"]
PI3K_pathway=["AKT1","ALK","B2M","CBL","CEBPA","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","KIT","MET","PDGFRA","PIK2CA","PIK3R1","PTEN","RET","TSC1","TSHR","VHL","NCOA3"]
RAS_pathway=["ALK","B2M","BRAF","CBL","CEBPA","CIC","CSF1R","EGFR","ERBB2","FGFR2","FGFR3","FLT3","GNA11","GNAQ","GNAS","HRAS","KIT","KRAS","MAP2K1","MAP3K1","MET","NF1","NRAS","PDGFRA","PTPN11","RET","VHL"]
APC_pathway=["AXIN1","CDH1","CTNNB1","EP300","FAM123B","GNAS","HNF1A","NF2","RNF43","SOX9"]
transcription_regulation_pathway=["AR","BCOR","CREBBP","GATA3","KLF4","PHF6","RUNX1","SF3B1","SRSF2","U2AF1","IKZF1","LMO1"]
dna_damage_pathway=["ATM","BAP1","BRCA1","BRCA2","MLH1","MSH2","MSH6","STAG2"]
mapkinase_pathway=["B2M","CEBPA","GNA11","GNAQ","MAP3K1","TNFAIP3","TSHR","MAP2K4","NKX2-1"]
STAT_pathway=["CLRF","FGFR2","FGFR3","FLT3","JAK1","JAK2","JAK3","STAT","MPL","SOCS1","VHL"]
NOTCH_pathway=["EP300","FBXW7","GATA1","GATA2","NOTCH1","NOTCH2"]
wnt_pathway=["KLF4"]
hedgehog_pathway=["PTCH1","SMO","SPOP"]
mTOR_pathway=["STK11"]
replicationpathway=["SETBP1"]

pathways={"Chromatin Modification": chromatin_modification_pathway, "Cell Cycle\\Apoptosis":cell_cycle_apoptosis_pathway, 
        "TGF beta":tgf_beta_pathway,"PI3K":PI3K_pathway,"RAS":RAS_pathway, "APC":APC_pathway,  
        "Transcription Regulation":transcription_regulation_pathway, 
        "DNA Damage Repair":dna_damage_pathway, "MAP Kinase":mapkinase_pathway,
        "STAT":STAT_pathway, "NOTCH":NOTCH_pathway, 
        "WNT":wnt_pathway, "Hedgehog":hedgehog_pathway, 
        "mTOR":mTOR_pathway, "Replication":replicationpathway}

MYC_pos_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_pos_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_affected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0,  
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}
MYC_neg_pathway_unaffected={"Chromatin Modification": 0, "Cell Cycle\\Apoptosis":0, "TGF beta":0,"PI3K":0,"RAS":0, "APC":0, 
                          "Transcription Regulation":0, "DNA Damage Repair":0, "MAP Kinase":0,"STAT":0, "NOTCH":0, "WNT":0, "Hedgehog":0,
                          "mTOR":0, "Replication":0}


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    copynumber = "n/a"
    log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
    if (log2 != None):
        copynumber = 2.0 * 2.0 ** log2
    else:
        copynumber="n/a"
    if copynumber == "n/a":
        continue
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if pathway_affected and copynumber > 4:
            MYC_pos_pathway_affected[pathway] += 1
        elif not pathway_affected and copynumber > 4:
            MYC_pos_pathway_unaffected[pathway] += 1
        elif pathway_affected and not copynumber > 4:
            MYC_neg_pathway_affected[pathway] += 1
        elif not pathway_affected and not copynumber > 4:
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 15)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 15)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 15)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 15)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[11]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAT 21 91 125 730 1.35 0.26 3.92 1.35 0.16 2.35 1.35 0.90 13.48
Chromatin Modification 47 65 249 606 1.76 0.00857 0.12859 1.76 0.00451 0.06767 1.76 1.00 14.96
PI3K 49 63 293 562 1.49 0.06 0.87 1.49 0.03201 0.48010 1.49 0.98 14.70
Transcription Regulation 18 94 168 687 0.78 0.44 6.66 0.78 0.85 12.74 0.78 0.22 3.32
NOTCH 10 102 53 802 1.48 0.31 4.58 1.48 0.18 2.73 1.48 0.90 13.50
DNA Damage Repair 9 103 74 781 0.92 1.00 15.00 0.92 0.64 9.64 0.92 0.50 7.49
TGF beta 11 101 62 793 1.39 0.34 5.11 1.39 0.21 3.20 1.39 0.87 13.12
MAP Kinase 18 94 133 722 1.04 0.89 13.35 1.04 0.49 7.33 1.04 0.62 9.28
Cell Cycle\Apoptosis 50 62 293 562 1.55 0.03556 0.53343 1.55 0.02106 0.31585 1.55 0.99 14.81
Replication 1 111 12 843 0.63 1.00 15.00 0.63 0.80 12.00 0.63 0.54 8.17
mTOR 1 111 1 854 7.69 0.22 3.28 7.69 0.22 3.28 7.69 0.99 14.80
RAS 52 60 338 517 1.33 0.18 2.74 1.33 0.10 1.47 1.33 0.93 13.99
WNT 0 112 2 853 0.00 1.00 15.00 0.00 1.00 15.00 0.00 0.78 11.72
APC 15 97 149 706 0.73 0.35 5.23 0.73 0.89 13.32 0.73 0.18 2.63
Hedgehog 4 108 25 830 1.23 0.77 11.48 1.23 0.44 6.57 1.23 0.76 11.43

Which Genes are affected in MYC positive samples (Vogelstein)

This time use genes of pathway definitions of Vogelstein et al.

Note that MYC has been deleted from the gene list



In [12]:

    
genes=["ABL1","ACVR1B","AKT1","ALK","APC","AR","ARID1A","ARID1B","ARID2","ASXL1","ATM","ATRX","AXIN1","B2M","BAP1","BCL2","BCOR","BRAF",
       "BRCA1","BRCA2","CARD11","CASP8","CBL","CDC73","CDH1","CDKN2A","CEBPA","CIC","CREBBP","CRLF2","CSF1R","CTNNB1","CYLD","DAXX",
       "DNMT1","DNMT3A","EGFR","EP300","ERBB2","EZH2","FAM123B","FBXW7","FGFR2","FGFR3","FLT3","FOXL2","FUBP1","GATA1","GATA2","GATA3",
       "GNA11","GNAQ","GNAS","H3F3A","HIST1H3B","HNF1A","HRAS","IDH1","IDH2","JAK1","JAK2","JAK3","KDM5C","KDM6A","KIT","KLF4","KRAS",
       "MAP2K1","MAP3K1","MED12","MEN1","MET","MLH1","MLL2","MLL3","MPL","MSH2","MSH6","MYD88","NCOR1","NF1","NF2","NFE2L2","NOTCH1",
       "NOTCH2","NPM1","NRAS","PAX5","PBRM1","PDGFRA","PHF6","PIK3CA","PIK3R1","PPP2R1A","PRDM1","PTCH1","PTEN","PTPN11","RB1","RET",
       "RNF43","RUNX1","SETD2","SETBP1","SF3B1","SMAD2","SMAD4","SMARCA4","SMARCB1","SMO","SOCS1","SOX9","SPOP","SRSF2","STAG2","STK11",
       "TET2","TNFAIP3","TRAF7","TP53","TSC1","TSHR","U2AF1","VHL","WT1","CCND1","CDKN2C","IKZF1","LMO1","MAP2K4","MDM2","MDM4",
       "MYCL1","MYCN","NCOA3","NKX2-1","SKP2"]


s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Gene</th><th>MYC + Gene affected</th><th>MYC + Gene unaffected</th><th>MYC - Gene affected</th><th>MYC - Gene unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

gene_count = 0
for gene in genes:
    gene_count += 1
    MYC_pos_gene_affected = 0
    MYC_pos_gene_unaffected = 0
    MYC_neg_gene_affected = 0
    MYC_neg_gene_unaffected = 0

    for sample in samples.values():
        if not sample.somatic_mutation_data:
            continue
        if sample.checkFocalGeneAmp(gene) or gene in sample.genes_affected:
            if sample.checkFocalGeneAmp("MYC"):
                MYC_pos_gene_affected += 1
            else:
                MYC_neg_gene_affected += 1
        else:
            if sample.checkFocalGeneAmp("MYC"):
                MYC_pos_gene_unaffected += 1
            else:
                MYC_neg_gene_unaffected += 1

    oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]])
    oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='greater')
    oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='less')

    if (pvalue > 0.05 and pvalue_greater > 0.05 and pvalue_less > 0.05):
        continue
        
    s += "<tr><th>"+gene+"</th><td>"+str(MYC_pos_gene_affected)+"</td>"
    s += "<td>"+str(MYC_pos_gene_unaffected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_affected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_unaffected)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
    if pvalue < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue * 137)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue * 137)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
    if pvalue_greater < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_greater * 137)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_greater * 137)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
    if pvalue_less < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_less * 137)+"</td></tr>"
    else: 
       s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_less * 137)+"</td></tr>"

s += "</table>"
print "Genes: "+str(gene_count)
h = HTML(s);h









    



Genes: 137






    Out[12]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Gene MYC + Gene affected MYC + Gene unaffected MYC - Gene affected MYC - Gene unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
AXIN1 2 59 3 904 10.21 0.03454 4.73142 10.21 0.03454 4.73142 10.21 1.00 136.70
BRCA1 5 56 10 897 8.01 0.00155 0.21278 8.01 0.00155 0.21278 8.01 1.00 136.98
CEBPA 4 57 4 903 15.84 0.00083 0.11344 15.84 0.00083 0.11344 15.84 1.00 136.99
CYLD 3 58 8 899 5.81 0.02733 3.74448 5.81 0.02733 3.74448 5.81 1.00 136.54
ERBB2 15 46 120 787 2.14 0.02042 2.79781 2.14 0.01532 2.09915 2.14 0.99 136.13
PIK3CA 14 47 317 590 0.55 0.07 9.46 0.55 0.98 134.59 0.55 0.03528 4.83290
SOCS1 2 59 4 903 7.65 0.04972 6.81214 7.65 0.04972 6.81214 7.65 1.00 136.43
TP53 36 25 261 646 3.56 0.00000 0.00033 3.56 0.00000 0.00028 3.56 1.00 137.00
NCOA3 7 54 16 891 7.22 0.00031 0.04229 7.22 0.00031 0.04229 7.22 1.00 137.00

Which Genes are affected in MYC copynumber > 4 samples (Vogelstein)



In [13]:

    
genes=["ABL1","ACVR1B","AKT1","ALK","APC","AR","ARID1A","ARID1B","ARID2","ASXL1","ATM","ATRX","AXIN1","B2M","BAP1","BCL2","BCOR","BRAF",
       "BRCA1","BRCA2","CARD11","CASP8","CBL","CDC73","CDH1","CDKN2A","CEBPA","CIC","CREBBP","CRLF2","CSF1R","CTNNB1","CYLD","DAXX",
       "DNMT1","DNMT3A","EGFR","EP300","ERBB2","EZH2","FAM123B","FBXW7","FGFR2","FGFR3","FLT3","FOXL2","FUBP1","GATA1","GATA2","GATA3",
       "GNA11","GNAQ","GNAS","H3F3A","HIST1H3B","HNF1A","HRAS","IDH1","IDH2","JAK1","JAK2","JAK3","KDM5C","KDM6A","KIT","KLF4","KRAS",
       "MAP2K1","MAP3K1","MED12","MEN1","MET","MLH1","MLL2","MLL3","MPL","MSH2","MSH6","MYD88","NCOR1","NF1","NF2","NFE2L2","NOTCH1",
       "NOTCH2","NPM1","NRAS","PAX5","PBRM1","PDGFRA","PHF6","PIK3CA","PIK3R1","PPP2R1A","PRDM1","PTCH1","PTEN","PTPN11","RB1","RET",
       "RNF43","RUNX1","SETD2","SETBP1","SF3B1","SMAD2","SMAD4","SMARCA4","SMARCB1","SMO","SOCS1","SOX9","SPOP","SRSF2","STAG2","STK11",
       "TET2","TNFAIP3","TRAF7","TP53","TSC1","TSHR","U2AF1","VHL","WT1","CCND1","CDKN2C","IKZF1","LMO1","MAP2K4","MDM2","MDM4",
       "MYCL1","MYCN","NCOA3","NKX2-1","SKP2"]


s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Gene</th><th>MYC + Gene affected</th><th>MYC + Gene unaffected</th><th>MYC - Gene affected</th><th>MYC - Gene unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

gene_count = 0
for gene in genes:
    gene_count += 1
    MYC_pos_gene_affected = 0
    MYC_pos_gene_unaffected = 0
    MYC_neg_gene_affected = 0
    MYC_neg_gene_unaffected = 0

    for sample in samples.values():
        if not sample.somatic_mutation_data:
            continue
        copynumber = "n/a"
        log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
        if (log2 != None):
           copynumber = 2.0 * 2.0 ** log2
        else:
           copynumber="n/a"
        if copynumber == "n/a":
           continue
        if sample.checkFocalGeneAmp(gene) or gene in sample.genes_affected:
            if copynumber > 4:
                MYC_pos_gene_affected += 1
            else:
                MYC_neg_gene_affected += 1
        else:
            if copynumber > 4:
                MYC_pos_gene_unaffected += 1
            else:
                MYC_neg_gene_unaffected += 1

    oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]])
    oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='greater')
    oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='less')

    if (pvalue > 0.05 and pvalue_greater > 0.05 and pvalue_less > 0.05):
        continue
        
    s += "<tr><th>"+gene+"</th><td>"+str(MYC_pos_gene_affected)+"</td>"
    s += "<td>"+str(MYC_pos_gene_unaffected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_affected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_unaffected)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
    if pvalue < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue * 137)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue * 137)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
    if pvalue_greater < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_greater * 137)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_greater * 137)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
    if pvalue_less < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_less * 137)+"</td></tr>"
    else: 
       s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_less * 137)+"</td></tr>"

s += "</table>"
print "Genes: "+str(gene_count)
h = HTML(s);h









    



Genes: 137






    Out[13]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Gene MYC + Gene affected MYC + Gene unaffected MYC - Gene affected MYC - Gene unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
AXIN1 3 109 2 853 11.74 0.01271 1.74168 11.74 0.01271 1.74168 11.74 1.00 136.89
CDH1 7 105 105 750 0.48 0.06 8.29 0.48 0.98 134.91 0.48 0.03617 4.95570
CYLD 6 106 5 850 9.62 0.00060 0.08254 9.62 0.00060 0.08254 9.62 1.00 136.99
EGFR 7 105 17 838 3.29 0.01498 2.05213 3.29 0.01498 2.05213 3.29 1.00 136.48
FGFR3 6 106 15 840 3.17 0.02661 3.64589 3.17 0.02661 3.64589 3.17 0.99 136.07
PIK3CA 29 83 302 553 0.64 0.06 7.71 0.64 0.98 134.66 0.64 0.02888 3.95703
SOCS1 3 109 3 852 7.82 0.02329 3.19118 7.82 0.02329 3.19118 7.82 1.00 136.71
TP53 63 49 233 622 3.43 0.00000 0.00000 3.43 0.00000 0.00000 3.43 1.00 137.00
MDM2 10 102 38 817 2.11 0.06 8.12 2.11 0.04154 5.69056 2.11 0.98 134.71
NCOA3 8 104 15 840 4.31 0.00278 0.38135 4.31 0.00278 0.38135 4.31 1.00 136.92

Which pathways are affected in MYC CN>4 but CN<6 samples



In [14]:

    
TP53_pathway=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK", "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668"]
STAG1_pathway=["PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A"]
MLL_pathway=["KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1"]
BAP1_pathway=["ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B"]
KRAS_pathway=["BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2"]
HLA_pathway=["B2M", "CD1D","HLA-A", "HLA-B", "P4HTM"]
HER2_pathway=["EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR"]
CDON_pathway=["BOC", "CDON"]
KEAP1_pathway=["KEAP1", "NFE2L2","CHD6", "PTMA", "WAC"]
SPOP_pathway=["MYD88", "SPOP"]
RUNX1_pathway=["CBFB", "RUNX1","ELF4"]
NOTCH_pathway=["FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH"]
ARID_pathway=["ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2"]
SMARC_pathway=["SMARCB1", "SMARCA4"]
SMC_pathway=["NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2"]
CLASP_pathway=["CLASP1","CLASP2","CLIP2"]

pathways={"TP53_pathway": TP53_pathway, "STAG1_pathway":STAG1_pathway, "MLL_pathway":MLL_pathway,"BAP1_pathway":BAP1_pathway,"KRAS_pathway":KRAS_pathway, "HLA_pathway":HLA_pathway,  "HER2_pathway":HER2_pathway, "CDON_pathway":CDON_pathway, "KEAP1_pathway":KEAP1_pathway,"SPOP_pathway":SPOP_pathway, "RUNX1_pathway":RUNX1_pathway, "NOTCH_pathway":NOTCH_pathway, "ARID_pathway":ARID_pathway, "SMARC_pathway":SMARC_pathway, "SMC_pathway":SMC_pathway,"CLASP_pathway":CLASP_pathway }

MYC_pos_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_pos_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    copynumber = "n/a"
    log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
    if (log2 != None):
        copynumber = 2.0 * 2.0 ** log2
    else:
        copynumber="n/a"
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if copynumber != "n/a" and pathway_affected and copynumber > 4 and copynumber < 6:
            MYC_pos_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and  copynumber > 4 and copynumber < 6:
            MYC_pos_pathway_unaffected[pathway] += 1
        elif copynumber != "n/a" and pathway_affected and not  copynumber > 4:
            MYC_neg_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and not  copynumber > 4:
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 16)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 16)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[14]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAG1_pathway 0 93 55 800 0.00 0.00438 0.07014 0.00 1.00 16.00 0.00 0.00286 0.04578
KRAS_pathway 33 60 351 504 0.79 0.32 5.10 0.79 0.88 14.01 0.79 0.18 2.83
CLASP_pathway 4 89 21 834 1.78 0.30 4.75 1.78 0.22 3.58 1.78 0.91 14.57
SMARC_pathway 1 92 8 847 1.15 0.61 9.71 1.15 0.61 9.71 1.15 0.78 12.51
SPOP_pathway 0 93 5 850 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.60 9.54
RUNX1_pathway 2 91 57 798 0.31 0.11 1.77 0.31 0.99 15.77 0.31 0.06 0.91
MLL_pathway 14 79 113 742 1.16 0.63 10.09 1.16 0.36 5.74 1.16 0.75 11.99
TP53_pathway 75 18 502 353 2.93 0.00003 0.00044 2.93 0.00002 0.00025 2.93 1.00 16.00
ARID_pathway 6 87 61 794 0.90 1.00 16.00 0.90 0.66 10.58 0.90 0.51 8.12
KEAP1_pathway 4 89 29 826 1.28 0.56 8.90 1.28 0.41 6.54 1.28 0.78 12.55
SMC_pathway 5 88 24 831 1.97 0.19 3.11 1.97 0.15 2.34 1.97 0.94 15.10
NOTCH_pathway 17 76 123 732 1.33 0.35 5.67 1.33 0.19 3.12 1.33 0.88 14.00
BAP1_pathway 7 86 55 800 1.18 0.66 10.54 1.18 0.41 6.52 1.18 0.74 11.91
CDON_pathway 3 90 15 840 1.87 0.41 6.54 1.87 0.26 4.09 1.87 0.91 14.54
HLA_pathway 3 90 25 830 1.11 0.75 12.00 1.11 0.53 8.47 1.11 0.71 11.33
HER2_pathway 25 68 156 699 1.65 0.05 0.82 1.65 0.03394 0.54298 1.65 0.98 15.70

Which pathways are affected in MYC CN>6 samples



In [15]:

    
TP53_pathway=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK", "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668"]
STAG1_pathway=["PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A"]
MLL_pathway=["KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1"]
BAP1_pathway=["ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B"]
KRAS_pathway=["BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2"]
HLA_pathway=["B2M", "CD1D","HLA-A", "HLA-B", "P4HTM"]
HER2_pathway=["EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR"]
CDON_pathway=["BOC", "CDON"]
KEAP1_pathway=["KEAP1", "NFE2L2","CHD6", "PTMA", "WAC"]
SPOP_pathway=["MYD88", "SPOP"]
RUNX1_pathway=["CBFB", "RUNX1","ELF4"]
NOTCH_pathway=["FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH"]
ARID_pathway=["ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2"]
SMARC_pathway=["SMARCB1", "SMARCA4"]
SMC_pathway=["NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2"]
CLASP_pathway=["CLASP1","CLASP2","CLIP2"]

pathways={"TP53_pathway": TP53_pathway, "STAG1_pathway":STAG1_pathway, "MLL_pathway":MLL_pathway,"BAP1_pathway":BAP1_pathway,"KRAS_pathway":KRAS_pathway, "HLA_pathway":HLA_pathway,  "HER2_pathway":HER2_pathway, "CDON_pathway":CDON_pathway, "KEAP1_pathway":KEAP1_pathway,"SPOP_pathway":SPOP_pathway, "RUNX1_pathway":RUNX1_pathway, "NOTCH_pathway":NOTCH_pathway, "ARID_pathway":ARID_pathway, "SMARC_pathway":SMARC_pathway, "SMC_pathway":SMC_pathway,"CLASP_pathway":CLASP_pathway }

MYC_pos_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_pos_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_affected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }
MYC_neg_pathway_unaffected={"TP53_pathway": 0, "STAG1_pathway":0, "MLL_pathway":0,"BAP1_pathway":0,"KRAS_pathway":0, "HLA_pathway":0,  "HER2_pathway":0, "CDON_pathway":0, "KEAP1_pathway":0,"SPOP_pathway":0, "RUNX1_pathway":0, "NOTCH_pathway":0, "ARID_pathway":0, "SMARC_pathway":0, "SMC_pathway":0,"CLASP_pathway":0 }


for sample in samples.values():
    if not sample.somatic_mutation_data:
        continue
    copynumber = "n/a"
    log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
    if (log2 != None):
        copynumber = 2.0 * 2.0 ** log2
    else:
        copynumber="n/a"
    for pathway in pathways.keys():
        pathway_affected = False
        for gene in pathways[pathway]:
            if gene in sample.genes_affected or sample.checkFocalGeneAmp(gene):
                pathway_affected = True
                break
        if copynumber != "n/a" and pathway_affected and copynumber > 6:
            MYC_pos_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and  copynumber > 6:
            MYC_pos_pathway_unaffected[pathway] += 1
        elif copynumber != "n/a" and pathway_affected and not  copynumber > 6:
            MYC_neg_pathway_affected[pathway] += 1
        elif copynumber != "n/a" and not pathway_affected and not  copynumber > 6:
            MYC_neg_pathway_unaffected[pathway] += 1

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Pathway</th><th>MYC + pathway affected</th><th>MYC + pathway unaffected</th><th>MYC - pathway affected</th><th>MYC - pathway unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

for pathway in pathways.keys():
   oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]])
   oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='greater')
   oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_pathway_affected[pathway], MYC_pos_pathway_unaffected[pathway]], [MYC_neg_pathway_affected[pathway], MYC_neg_pathway_unaffected[pathway]]], alternative='less')
   s += "<tr><th>"+pathway+"</th><td>"+str(MYC_pos_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_pos_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_affected[pathway])+"</td>"
   s += "<td>"+str(MYC_neg_pathway_unaffected[pathway])+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
   if pvalue < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
   if pvalue_greater < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_greater * 16)+"</td>"
   else:
      s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_greater * 16)+"</td>"
   s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
   if pvalue_less < 0.05: 
      s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.5f}".format(pvalue_less * 16)+"</td></tr>"
   else: 
      s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
      s += "<td>"+"{:.2f}".format(pvalue_less * 16)+"</td></tr>"

s += "</table>"
h = HTML(s);h









    Out[15]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Pathway MYC + pathway affected MYC + pathway unaffected MYC - pathway affected MYC - pathway unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
STAG1_pathway 1 18 55 893 0.90 1.00 16.00 0.90 0.68 10.91 0.90 0.70 11.16
KRAS_pathway 9 10 384 564 1.32 0.64 10.22 1.32 0.35 5.64 1.32 0.80 12.80
CLASP_pathway 0 19 25 923 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.61 9.68
SMARC_pathway 0 19 9 939 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.84 13.37
SPOP_pathway 0 19 5 943 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.91 14.49
RUNX1_pathway 0 19 59 889 0.00 0.62 9.98 0.00 1.00 16.00 0.00 0.30 4.78
MLL_pathway 2 17 127 821 0.76 1.00 16.00 0.76 0.74 11.91 0.76 0.52 8.39
TP53_pathway 15 4 577 371 2.41 0.15 2.44 2.41 0.08 1.33 2.41 0.97 15.55
ARID_pathway 2 17 67 881 1.55 0.64 10.24 1.55 0.40 6.37 1.55 0.85 13.63
KEAP1_pathway 1 18 33 915 1.54 0.50 7.95 1.54 0.50 7.95 1.54 0.86 13.74
SMC_pathway 0 19 29 919 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.56 8.92
NOTCH_pathway 2 17 140 808 0.68 1.00 16.00 0.68 0.79 12.70 0.68 0.46 7.28
BAP1_pathway 1 18 62 886 0.79 1.00 16.00 0.79 0.73 11.61 0.79 0.65 10.33
CDON_pathway 0 19 18 930 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.70 11.16
HLA_pathway 0 19 28 920 0.00 1.00 16.00 0.00 1.00 16.00 0.00 0.57 9.11
HER2_pathway 4 15 181 767 1.13 0.77 12.34 1.13 0.51 8.12 1.13 0.71 11.35

Which genes of the Leiserson Pathway are affected at MYC copynumber > 4 but copynumber < 6 samples



In [16]:

    
genes=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK",
       "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668",
       "PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A",
       "KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1",
       "ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B",
       "BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2",
       "B2M", "CD1D","HLA-A", "HLA-B", "P4HTM",
       "EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR",
       "BOC", "CDON",
       "KEAP1", "NFE2L2","CHD6", "PTMA", "WAC",
       "MYD88", "SPOP",
       "CBFB", "RUNX1","ELF4",
       "FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH",
       "ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2",
       "SMARCB1", "SMARCA4",
       "NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2",
       "CLASP1","CLASP2","CLIP2"]

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Gene</th><th>MYC + Gene affected</th><th>MYC + Gene unaffected</th><th>MYC - Gene affected</th><th>MYC - Gene unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

gene_count = 0
for gene in genes:
    gene_count += 1
    MYC_pos_gene_affected = 0
    MYC_pos_gene_unaffected = 0
    MYC_neg_gene_affected = 0
    MYC_neg_gene_unaffected = 0

    for sample in samples.values():
        if not sample.somatic_mutation_data:
            continue
        copynumber = "n/a"
        log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
        if (log2 != None):
            copynumber = 2.0 * 2.0 ** log2
        else:
            copynumber="n/a"
        if sample.checkFocalGeneAmp(gene) or gene in sample.genes_affected:
            if copynumber != "n/a" and copynumber > 4 and copynumber < 6:
                MYC_pos_gene_affected += 1
            elif  copynumber != "n/a" and not copynumber > 4:
                MYC_neg_gene_affected += 1
        else:
            if copynumber != "n/a" and copynumber > 4 and copynumber < 6:
                MYC_pos_gene_unaffected += 1
            elif  copynumber != "n/a" and not copynumber > 4:
                MYC_neg_gene_unaffected += 1

    oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]])
    oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='greater')
    oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='less')

    if (pvalue > 0.05 and pvalue_greater > 0.05 and pvalue_less > 0.05):
        continue
        
    s += "<tr><th>"+gene+"</th><td>"+str(MYC_pos_gene_affected)+"</td>"
    s += "<td>"+str(MYC_pos_gene_unaffected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_affected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_unaffected)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
    if pvalue < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
    if pvalue_greater < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_greater * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_greater * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
    if pvalue_less < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_less * 134)+"</td></tr>"
    else: 
       s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_less * 134)+"</td></tr>"

s += "</table>"
print "Genes: "+str(gene_count)
h = HTML(s);h









    



Genes: 134






    Out[16]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Gene MYC + Gene affected MYC + Gene unaffected MYC - Gene affected MYC - Gene unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
TP53 52 41 233 622 3.39 0.00000 0.00001 3.39 0.00000 0.00001 3.39 1.00 134.00
CDKN2AIP 2 91 1 854 18.77 0.02676 3.58554 18.77 0.02676 3.58554 18.77 1.00 133.88
ZMIZ1 6 87 19 836 3.03 0.02885 3.86572 3.03 0.02885 3.86572 3.03 0.99 132.97
KRAS 7 86 26 829 2.60 0.03538 4.74100 2.60 0.03538 4.74100 2.60 0.99 132.50
PIK3CA 21 72 302 553 0.53 0.01521 2.03825 0.53 1.00 133.46 0.53 0.00810 1.08584
EGFR 7 86 17 838 4.01 0.00599 0.80333 4.01 0.00599 0.80333 4.01 1.00 133.83

Which genes of the Leiserson Pathway are affected at MYC copynumber > 6



In [17]:

    
genes=["CCND1","CDKN2A", "CUL9", "NPM1", "PTEN", "TP53","AGBL2", "ALS2CR8", "AMOTL1", "ANKRD12", "BRPF1", "CACHD1", "CARNS1", "CDKN2AIP", "CELSR3", "CHD8", "EPHA3", "HECW2", "IFT140", "IWS1", "MAGI2", "MAST3", "MDM4", "MLL5", "PLEKHA8", "PRDM2", "PRKRIR", "SCAPER", "SETD2", "SMG1", "SMG5", "SMG7", "SNRK",
       "SPTBN2", "STK11IP", "STRADA", "SWAP70", "TEP1", "TRIP12", "TTLL5", "UACA", "ZFP91", "ZMIZ1", "ZNF227", "ZNF668",
       "PDS5B", "STAG1", "STAG2", "WAPAL","PDS5A", "RAD21", "SMC1A",
       "KDM6A", "MLL2", "MLL3","E2F3", "N4BP2", "PROSER1",
       "ASXL1", "ASXL2", "BAP1","ANKRD17", "FOXK1", "FOXK2", "KDM1B",
       "BRAF", "KRAS", "PIK3CA","ARHGAP4", "EIF2AK1", "FAM118B", "NRAS", "PLCE1", "RASGRF2", "RASGRP2", "RGL1", "VARS2",
       "B2M", "CD1D","HLA-A", "HLA-B", "P4HTM",
       "EGFR", "ERBB2","AREG", "ELF3", "ERBB4", "LRIG1", "OSMR",
       "BOC", "CDON",
       "KEAP1", "NFE2L2","CHD6", "PTMA", "WAC",
       "MYD88", "SPOP",
       "CBFB", "RUNX1","ELF4",
       "FBXW7", "NOTCH1","CCNE1", "DCLRE1C", "DLL1", "DTX4", "FAM101A", "FOXM1", "JAG1", "KLF5", "LFNG", "LPP", "MAML1", "MMS22L", "NOTCH3", "NOTCH4", "SHPRH",
       "ARID1A", "PBRM1","ADNP", "ARID1B", "ARID2",
       "SMARCB1", "SMARCA4",
       "NCAPD3","SMC4","NCAPG2","NCAPH2","SMC2","NCAPD2",
       "CLASP1","CLASP2","CLIP2"]

s = "<table>"
s += "<th></th><th colspan=\"4\">Sample</th><th colspan=\"3\">Fisher's exact test (two-sided)</th><th colspan=\"3\">Fisher's exact test (one-sided greater)</th><th colspan=\"3\">Fisher's exact test (one-sided less)</th></tr>"
s += "<tr><th>Gene</th><th>MYC + Gene affected</th><th>MYC + Gene unaffected</th><th>MYC - Gene affected</th><th>MYC - Gene unaffected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th><th>Odd's ratio</th><th>p-value</th><th>p-value corrected</th></tr>"

gene_count = 0
for gene in genes:
    gene_count += 1
    MYC_pos_gene_affected = 0
    MYC_pos_gene_unaffected = 0
    MYC_neg_gene_affected = 0
    MYC_neg_gene_unaffected = 0

    for sample in samples.values():
        if not sample.somatic_mutation_data:
            continue
        copynumber = "n/a"
        log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
        if (log2 != None):
            copynumber = 2.0 * 2.0 ** log2
        else:
            copynumber="n/a"
        if sample.checkFocalGeneAmp(gene) or gene in sample.genes_affected:
            if copynumber != "n/a" and copynumber > 6:
                MYC_pos_gene_affected += 1
            elif  copynumber != "n/a" and not copynumber > 6:
                MYC_neg_gene_affected += 1
        else:
            if copynumber != "n/a" and copynumber > 6:
                MYC_pos_gene_unaffected += 1
            elif  copynumber != "n/a" and not copynumber > 6:
                MYC_neg_gene_unaffected += 1

    oddsratio, pvalue = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]])
    oddsratio_greater, pvalue_greater = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='greater')
    oddsratio_less, pvalue_less = scipy.stats.fisher_exact([[MYC_pos_gene_affected, MYC_pos_gene_unaffected], [MYC_neg_gene_affected, MYC_neg_gene_unaffected]], alternative='less')

    if (pvalue > 0.05 and pvalue_greater > 0.05 and pvalue_less > 0.05):
        continue
        
    s += "<tr><th>"+gene+"</th><td>"+str(MYC_pos_gene_affected)+"</td>"
    s += "<td>"+str(MYC_pos_gene_unaffected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_affected)+"</td>"
    s += "<td>"+str(MYC_neg_gene_unaffected)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio)+"</td>"
    if pvalue < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_greater)+"</td>"
    if pvalue_greater < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_greater * 134)+"</td>"
    else:
       s += "<td>"+"{:.2f}".format(pvalue_greater)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_greater * 134)+"</td>"
    s += "<td>"+"{:.2f}".format(oddsratio_less)+"</td>"
    if pvalue_less < 0.05: 
       s += "<td>"+"{:.5f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.5f}".format(pvalue_less * 134)+"</td></tr>"
    else: 
       s += "<td>"+"{:.2f}".format(pvalue_less)+"</td>"
       s += "<td>"+"{:.2f}".format(pvalue_less * 134)+"</td></tr>"

s += "</table>"
print "Genes: "+str(gene_count)
h = HTML(s);h









    



Genes: 134






    Out[17]:




Sample Fisher's exact test (two-sided) Fisher's exact test (one-sided greater) Fisher's exact test (one-sided less)
Gene MYC + Gene affected MYC + Gene unaffected MYC - Gene affected MYC - Gene unaffected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected Odd's ratio p-value p-value corrected
TP53 11 8 285 663 3.20 0.02022 2.70895 3.20 0.01164 1.56039 3.20 1.00 133.58
IFT140 2 17 7 941 15.82 0.01214 1.62627 15.82 0.01214 1.62627 15.82 1.00 133.93
MLL5 2 17 10 938 11.04 0.02148 2.87829 11.04 0.02148 2.87829 11.04 1.00 133.83
FOXK1 1 18 0 948 inf 0.01965 2.63289 inf 0.01965 2.63289 inf 1.00 134.00

	Sample				Fisher's exact test (two-sided)			Fisher's exact test (one-sided greater)			Fisher's exact test (one-sided less)
Pathway	MYC + pathway affected	MYC + pathway unaffected	MYC - pathway affected	MYC - pathway unaffected	Odd's ratio	p-value	p-value corrected	Odd's ratio	p-value	p-value corrected	Odd's ratio	p-value	p-value corrected
STAG1_pathway	2	59	54	853	0.54	0.57	9.15	0.54	0.88	14.13	0.54	0.30	4.77
KRAS_pathway	21	40	373	534	0.75	0.35	5.55	0.75	0.88	14.06	0.75	0.19	2.97
CLASP_pathway	4	57	21	886	2.96	0.07	1.07	2.96	0.07	1.07	2.96	0.98	15.73
SMARC_pathway	0	61	9	898	0.00	1.00	16.00	0.00	1.00	16.00	0.00	0.56	8.88
SPOP_pathway	0	61	5	902	0.00	1.00	16.00	0.00	1.00	16.00	0.00	0.72	11.55
RUNX1_pathway	1	60	58	849	0.24	0.17	2.72	0.24	0.98	15.70	0.24	0.10	1.59
MLL_pathway	14	47	115	792	2.05	0.03086	0.49383	2.05	0.02357	0.37711	2.05	0.99	15.84
TP53_pathway	49	12	544	363	2.72	0.00159	0.02544	2.72	0.00085	0.01354	2.72	1.00	16.00
ARID_pathway	3	58	66	841	0.66	0.80	12.72	0.66	0.83	13.25	0.66	0.35	5.63
KEAP1_pathway	4	57	30	877	2.05	0.16	2.57	2.05	0.16	2.57	2.05	0.94	15.09
SMC_pathway	2	59	27	880	1.10	0.70	11.26	1.10	0.56	8.91	1.10	0.73	11.61
NOTCH_pathway	12	49	130	777	1.46	0.26	4.19	1.46	0.17	2.70	1.46	0.90	14.47
BAP1_pathway	5	56	58	849	1.31	0.59	9.40	1.31	0.36	5.83	1.31	0.80	12.82
CDON_pathway	2	59	16	891	1.89	0.32	5.04	1.89	0.32	5.04	1.89	0.90	14.42
HLA_pathway	1	60	27	880	0.54	1.00	16.00	0.54	0.84	13.48	0.54	0.46	7.40
HER2_pathway	18	43	167	740	1.85	0.04239	0.67832	1.85	0.02890	0.46235	1.85	0.99	15.78

	Sample				Fisher's exact test (two-sided)			Fisher's exact test (one-sided greater)			Fisher's exact test (one-sided less)
Gene	MYC + Gene affected	MYC + Gene unaffected	MYC - Gene affected	MYC - Gene unaffected	Odd's ratio	p-value	p-value corrected	Odd's ratio	p-value	p-value corrected	Odd's ratio	p-value	p-value corrected
CCND1	10	51	182	725	0.78	0.62	82.90	0.78	0.80	107.69	0.78	0.31	40.95
CDKN2A	1	60	4	903	3.76	0.28	37.29	3.76	0.28	37.29	3.76	0.97	129.37
CUL9	1	60	8	899	1.87	0.44	59.60	1.87	0.44	59.60	1.87	0.89	119.84
NPM1	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
PTEN	2	59	33	874	0.90	1.00	134.00	0.90	0.66	88.65	0.90	0.62	82.92
TP53	36	25	261	646	3.56	0.00000	0.00032	3.56	0.00000	0.00027	3.56	1.00	134.00
AGBL2	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
ALS2CR8	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
AMOTL1	0	61	10	897	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.52	69.67
ANKRD12	0	61	20	887	0.00	0.63	84.58	0.00	1.00	134.00	0.00	0.27	35.97
BRPF1	0	61	6	901	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.68	90.58
CACHD1	1	60	6	901	2.50	0.37	49.16	2.50	0.37	49.16	2.50	0.93	125.04
CARNS1	2	59	44	863	0.66	0.76	102.23	0.66	0.80	107.49	0.66	0.43	58.24
CDKN2AIP	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
CELSR3	0	61	12	895	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.46	61.08
CHD8	0	61	15	892	0.00	0.62	82.66	0.00	1.00	134.00	0.00	0.37	50.11
EPHA3	3	58	22	885	2.08	0.20	27.46	2.08	0.20	27.46	2.08	0.93	125.08
HECW2	2	59	13	894	2.33	0.24	32.56	2.33	0.24	32.56	2.33	0.94	125.56
IFT140	2	59	7	900	4.36	0.11	14.16	4.36	0.11	14.16	4.36	0.98	131.96
IWS1	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
MAGI2	0	61	9	898	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.56	74.40
MAST3	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
MDM4	3	58	23	884	1.99	0.22	29.72	1.99	0.22	29.72	1.99	0.92	123.94
MLL5	2	59	10	897	3.04	0.17	23.02	3.04	0.17	23.02	3.04	0.97	129.33
PLEKHA8	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
PRDM2	1	60	7	900	2.14	0.41	54.55	2.14	0.41	54.55	2.14	0.91	122.53
PRKRIR	7	54	85	822	1.25	0.51	67.77	1.25	0.36	47.93	1.25	0.79	105.25
SCAPER	0	61	10	897	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.52	69.67
SETD2	2	59	15	892	2.02	0.29	39.02	2.02	0.29	39.02	2.02	0.91	122.47
SMG1	1	60	18	889	0.82	1.00	134.00	0.82	0.71	95.56	0.82	0.66	88.55
SMG5	0	61	8	899	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.59	79.45
SMG7	0	61	8	899	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.59	79.45
SNRK	2	59	5	902	6.12	0.07	8.96	6.12	0.07	8.96	6.12	0.99	133.07
SPTBN2	2	59	41	866	0.72	1.00	134.00	0.72	0.77	103.17	0.72	0.48	64.57
STK11IP	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
STRADA	0	61	7	900	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.63	84.84
SWAP70	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
TEP1	2	59	15	892	2.02	0.29	39.02	2.02	0.29	39.02	2.02	0.91	122.47
TRIP12	0	61	8	899	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.59	79.45
TTLL5	0	61	10	897	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.52	69.67
UACA	1	60	6	901	2.50	0.37	49.16	2.50	0.37	49.16	2.50	0.93	125.04
ZFP91	0	61	10	897	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.52	69.67
ZMIZ1	4	57	22	885	2.82	0.08	10.06	2.82	0.08	10.06	2.82	0.98	131.34
ZNF227	1	60	1	906	15.10	0.12	16.36	15.10	0.12	16.36	15.10	1.00	133.48
ZNF668	1	60	4	903	3.76	0.28	37.29	3.76	0.28	37.29	3.76	0.97	129.37
PDS5B	0	61	13	894	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.43	57.18
STAG1	1	60	11	896	1.36	0.54	72.92	1.36	0.54	72.92	1.36	0.83	110.98
STAG2	0	61	13	894	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.43	57.18
WAPAL	0	61	2	905	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.88	117.64
PDS5A	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
RAD21	1	60	5	902	3.01	0.32	43.42	3.01	0.32	43.42	3.01	0.95	127.34
SMC1A	0	61	11	896	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.49	65.24
KDM6A	2	59	14	893	2.16	0.27	35.79	2.16	0.27	35.79	2.16	0.93	124.07
MLL2	1	60	22	885	0.67	1.00	134.00	0.67	0.78	104.54	0.67	0.57	76.15
MLL3	7	54	60	847	1.83	0.18	24.70	1.83	0.12	16.17	1.83	0.95	126.96
E2F3	4	57	18	889	3.47	0.04435	5.94300	3.47	0.04435	5.94300	3.47	0.99	132.71
N4BP2	0	61	12	895	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.46	61.08
PROSER1	0	61	7	900	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.63	84.84
ASXL1	1	60	5	902	3.01	0.32	43.42	3.01	0.32	43.42	3.01	0.95	127.34
ASXL2	0	61	16	891	0.00	0.62	82.69	0.00	1.00	134.00	0.00	0.35	46.90
BAP1	0	61	7	900	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.63	84.84
ANKRD17	2	59	22	885	1.36	0.66	88.37	1.36	0.45	60.81	1.36	0.81	108.77
FOXK1	0	61	1	906	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.94	125.56
FOXK2	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
KDM1B	2	59	7	900	4.36	0.11	14.16	4.36	0.11	14.16	4.36	0.98	131.96
BRAF	1	60	12	895	1.24	0.57	76.82	1.24	0.57	76.82	1.24	0.80	107.84
KRAS	2	59	31	876	0.96	1.00	134.00	0.96	0.63	84.28	0.96	0.65	87.70
PIK3CA	14	47	317	590	0.55	0.07	9.26	0.55	0.98	131.65	0.55	0.03528	4.72707
ARHGAP4	0	61	6	901	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.68	90.58
EIF2AK1	1	60	6	901	2.50	0.37	49.16	2.50	0.37	49.16	2.50	0.93	125.04
FAM118B	2	59	6	901	5.09	0.09	11.47	5.09	0.09	11.47	5.09	0.99	132.58
NRAS	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
PLCE1	1	60	15	892	0.99	1.00	134.00	0.99	0.65	87.10	0.99	0.73	98.21
RASGRF2	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
RASGRP2	1	60	6	901	2.50	0.37	49.16	2.50	0.37	49.16	2.50	0.93	125.04
RGL1	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
VARS2	0	61	1	906	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.94	125.56
B2M	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
CD1D	1	60	5	902	3.01	0.32	43.42	3.01	0.32	43.42	3.01	0.95	127.34
HLA-A	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
HLA-B	0	61	8	899	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.59	79.45
P4HTM	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
EGFR	0	61	24	883	0.00	0.39	52.79	0.00	1.00	134.00	0.00	0.21	27.56
ERBB2	15	46	120	787	2.14	0.02042	2.73654	2.14	0.01532	2.05318	2.14	0.99	133.15
AREG	2	59	10	897	3.04	0.17	23.02	3.04	0.17	23.02	3.04	0.97	129.33
ELF3	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
ERBB4	2	59	21	886	1.43	0.65	87.30	1.43	0.43	57.85	1.43	0.83	110.94
LRIG1	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
OSMR	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
BOC	0	61	10	897	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.52	69.67
CDON	2	59	7	900	4.36	0.11	14.16	4.36	0.11	14.16	4.36	0.98	131.96
KEAP1	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
NFE2L2	1	60	5	902	3.01	0.32	43.42	3.01	0.32	43.42	3.01	0.95	127.34
CHD6	2	59	18	889	1.67	0.36	48.61	1.67	0.36	48.61	1.67	0.87	117.07
PTMA	0	61	1	906	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.94	125.56
WAC	1	60	8	899	1.87	0.44	59.60	1.87	0.44	59.60	1.87	0.89	119.84
MYD88	0	61	1	906	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.94	125.56
SPOP	0	61	4	903	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.77	103.24
CBFB	0	61	23	884	0.00	0.39	52.52	0.00	1.00	134.00	0.00	0.22	29.46
RUNX1	1	60	31	876	0.47	0.72	95.92	0.47	0.88	117.89	0.47	0.39	52.02
ELF4	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
FBXW7	2	59	15	892	2.02	0.29	39.02	2.02	0.29	39.02	2.02	0.91	122.47
NOTCH1	1	60	11	896	1.36	0.54	72.92	1.36	0.54	72.92	1.36	0.83	110.98
CCNE1	8	53	37	870	3.55	0.00528	0.70689	3.55	0.00528	0.70689	3.55	1.00	133.83
DCLRE1C	1	60	7	900	2.14	0.41	54.55	2.14	0.41	54.55	2.14	0.91	122.53
DLL1	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
DTX4	0	61	8	899	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.59	79.45
FAM101A	1	60	4	903	3.76	0.28	37.29	3.76	0.28	37.29	3.76	0.97	129.37
FOXM1	1	60	6	901	2.50	0.37	49.16	2.50	0.37	49.16	2.50	0.93	125.04
JAG1	0	61	13	894	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.43	57.18
KLF5	0	61	0	907	nan	1.00	134.00	nan	1.00	134.00	nan	1.00	134.00
LFNG	0	61	1	906	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.94	125.56
LPP	0	61	6	901	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.68	90.58
MAML1	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
MMS22L	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
NOTCH3	0	61	14	893	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.40	53.53
NOTCH4	0	61	10	897	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.52	69.67
SHPRH	0	61	11	896	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.49	65.24
ARID1A	0	61	27	880	0.00	0.41	54.58	0.00	1.00	134.00	0.00	0.17	22.55
PBRM1	1	60	6	901	2.50	0.37	49.16	2.50	0.37	49.16	2.50	0.93	125.04
ADNP	1	60	14	893	1.06	1.00	134.00	1.06	0.63	83.89	1.06	0.76	101.44
ARID1B	1	60	15	892	0.99	1.00	134.00	0.99	0.65	87.10	0.99	0.73	98.21
ARID2	0	61	11	896	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.49	65.24
SMARCB1	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
SMARCA4	0	61	7	900	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.63	84.84
NCAPD3	0	61	11	896	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.49	65.24
SMC4	0	61	8	899	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.59	79.45
NCAPG2	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
NCAPH2	0	61	3	904	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.82	110.21
SMC2	0	61	5	902	0.00	1.00	134.00	0.00	1.00	134.00	0.00	0.72	96.71
NCAPD2	2	59	3	904	10.21	0.03454	4.62781	10.21	0.03454	4.62781	10.21	1.00	133.71
CLASP1	1	60	8	899	1.87	0.44	59.60	1.87	0.44	59.60	1.87	0.89	119.84
CLASP2	1	60	7	900	2.14	0.41	54.55	2.14	0.41	54.55	2.14	0.91	122.53
CLIP2	2	59	7	900	4.36	0.11	14.16	4.36	0.11	14.16	4.36	0.98	131.96