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In [1]:



    


cd ..



    


















    






/cellar/users/agross/TCGA_Code/DX/Notebooks

















In [2]:



    


import NotebookImport
from DX_screen import *



    


















    






importing IPython notebook from DX_screen









    






importing IPython notebook from Imports









    






/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/rpy/__init__.py:11: FutureWarning: The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2. 
See here for a guide on how to port your code to rpy2: http://pandas.pydata.org/pandas-docs/stable/r_interface.html
  FutureWarning)










    






importing IPython notebook from Global_Parameters









    






importing IPython notebook from /cellar/users/agross/anaconda2/lib/python2.7/site-packages/MethylTools/Probe_Annotations
















In [3]:



    


from metaPCNA import *



    


















    






importing IPython notebook from metaPCNA

GSEA

Gene level fraction upregulated statistic





In [4]:



    


gs2 = gene_sets.ix[dx_rna.index].fillna(0)
rr = screen_feature(dx_rna.frac, rev_kruskal, gs2.T, 
                    align=False)
fp = (1.*gene_sets.T * dx_rna.frac).T.dropna().replace(0, np.nan).mean().order()
fp.name = 'mean frac'



    











In [6]:



    


rr.join(fp).to_csv(FIGDIR + 'f_up_gene_sets.csv')



    











In [7]:



    


ff_u = filter_pathway_hits(rr.ix[ti(fp>.5)].p.order(), gs2)
ff_p = filter_pathway_hits(rr.ix[ti(fp<.5)].p.order(), gs2)
ff = ff_u.append(ff_p)

selected = rr.ix[ff[ff < .00001].index].join(fp)
selected.sort('p')



    


















    
Out[7]:










  
    

      
      H
      p
      q
      mean frac
    

    

      Gene_Set
      
      
      
      
    

  
  
    

      REACTOME_CELL_CYCLE
      343.67
      1.01e-76
      1.35e-73
      0.67
    

    

      REACTOME_METABOLISM_OF_PROTEINS
      145.97
      1.32e-33
      1.46e-31
      0.59
    

    

      REACTOME_PROCESSING_OF_CAPPED_INTRON_CONTAINING_PRE_MRNA
      126.77
      2.09e-29
      1.64e-27
      0.66
    

    

      REACTOME_SIGNALING_BY_GPCR
      70.43
      4.78e-17
      1.10e-15
      0.46
    

    

      REACTOME_BIOLOGICAL_OXIDATIONS
      54.94
      1.24e-13
      2.07e-12
      0.41
    

    

      REACTOME_TRNA_AMINOACYLATION
      53.07
      3.21e-13
      5.21e-12
      0.68
    

    

      KEGG_PPAR_SIGNALING_PATHWAY
      37.42
      9.50e-10
      1.17e-08
      0.39
    

    

      KEGG_RNA_DEGRADATION
      32.92
      9.59e-09
      1.02e-07
      0.61
    

    

      NABA_ECM_GLYCOPROTEINS
      31.19
      2.34e-08
      2.43e-07
      0.44
    

    

      REACTOME_METABOLISM_OF_NUCLEOTIDES
      20.31
      6.58e-06
      4.81e-05
      0.60
    

  




















In [8]:



    


selected.to_csv(FIGDIR + 'f_up_gene_sets_selected.csv')

Looking for subsets of the cell-cycle pathway





In [9]:



    


d = pd.DataFrame({g: gs2['REACTOME_CELL_CYCLE'] for g in gs2.columns})
a,b = odds_ratio_df(d.T>0, gs2.T>0)



    











In [10]:



    


dd = rr.ix[ti((a > 100) & (rr.q < 10e-15))].join(fp).sort(fp.name, ascending=False)
filter_pathway_hits(dd, gs2)



    


















    
Out[10]:










  
    

      
      H
      p
      q
      mean frac
    

  
  
    

      REACTOME_M_G1_TRANSITION
      135.57
      2.48e-31
      2.20e-29
      0.74
    

    

      REACTOME_DEPOSITION_OF_NEW_CENPA_CONTAINING_NUCLEOSOMES_AT_THE_CENTROMERE
      99.38
      2.09e-23
      1.11e-21
      0.73
    

  




















In [11]:



    


(combine(gs2['REACTOME_M_G1_TRANSITION']>0, 
            gs2['REACTOME_DEPOSITION_OF_NEW_CENPA_CONTAINING_NUCLEOSOMES_AT_THE_CENTROMERE']>0)
).value_counts()



    


















    
Out[11]:








neither    18366
first         72
second        54
dtype: int64

















In [12]:



    


f2 = fp.ix[ti(rr.q < .00001)]
ff_u = filter_pathway_hits(fp.ix[ti(f2>.5)].order()[::-1], gs2)
ff_p = filter_pathway_hits(fp.ix[ti(f2<.5)].order(), gs2)
ff = ff_u.append(ff_p)

selected = rr.ix[ff.index].join(f2)
selected.ix[(f2 - .5).abs().order().index[::-1]].dropna()



    


















    
Out[12]:










  
    

      
      H
      p
      q
      mean frac
    

  
  
    

      REACTOME_UNWINDING_OF_DNA
      28.61
      8.86e-08
      8.24e-07
      0.81
    

    

      REACTOME_EXTENSION_OF_TELOMERES
      55.78
      8.12e-14
      1.40e-12
      0.76
    

    

      BIOCARTA_PROTEASOME_PATHWAY
      56.05
      7.07e-14
      1.25e-12
      0.74
    

    

      REACTOME_DEPOSITION_OF_NEW_CENPA_CONTAINING_NUCLEOSOMES_AT_THE_CENTROMERE
      99.38
      2.09e-23
      1.11e-21
      0.73
    

    

      REACTOME_FANCONI_ANEMIA_PATHWAY
      30.87
      2.76e-08
      2.80e-07
      0.71
    

    

      PID_FOXM1_PATHWAY
      35.10
      3.13e-09
      3.59e-08
      0.70
    

    

      REACTOME_CYTOSOLIC_TRNA_AMINOACYLATION
      34.10
      5.24e-09
      5.91e-08
      0.70
    

    

      REACTOME_MRNA_SPLICING_MINOR_PATHWAY
      52.40
      4.53e-13
      7.17e-12
      0.69
    

    

      REACTOME_NEP_NS2_INTERACTS_WITH_THE_CELLULAR_EXPORT_MACHINERY
      33.27
      8.02e-09
      8.74e-08
      0.67
    

    

      KEGG_FATTY_ACID_METABOLISM
      47.69
      5.00e-12
      7.15e-11
      0.33
    

    

      REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE
      83.95
      5.07e-20
      1.82e-18
      0.63
    

    

      REACTOME_DEADENYLATION_DEPENDENT_MRNA_DECAY
      27.93
      1.26e-07
      1.15e-06
      0.63
    

    

      REACTOME_ASPARAGINE_N_LINKED_GLYCOSYLATION
      36.83
      1.29e-09
      1.55e-08
      0.61
    

    

      REACTOME_CYTOCHROME_P450_ARRANGED_BY_SUBSTRATE_TYPE
      25.97
      3.46e-07
      2.95e-06
      0.40
    

    

      REACTOME_G_ALPHA_S_SIGNALLING_EVENTS
      33.07
      8.90e-09
      9.54e-08
      0.42
    

    

      NABA_ECM_GLYCOPROTEINS
      31.19
      2.34e-08
      2.43e-07
      0.44
    

    

      NABA_SECRETED_FACTORS
      38.68
      4.98e-10
      6.37e-09
      0.45
    

  




















In [13]:



    


selected.to_csv(FIGDIR + 'f_up_gene_sets_selected_fc.csv')

Gene level proliferation statistic





In [14]:



    


gs2 = gene_sets.ix[pcna_corr.dropna().index].fillna(0)
rr = screen_feature(pcna_corr, rev_kruskal, gs2.T, 
                    align=False)
fp = (1.*gene_sets.T * pcna_corr).T.dropna().replace(0, np.nan).mean().order()
fp.name = 'mean score'



    











In [15]:



    


rr.join(fp).to_csv(FIGDIR + 'pcna_gene_sets.csv')



    











In [16]:



    


ff_u = filter_pathway_hits(rr.ix[ti(fp>0)].p.order(), gs2)
ff_p = filter_pathway_hits(rr.ix[ti(fp<0)].p.order(), gs2)
ff = ff_u.append(ff_p)
selected = rr.ix[ff[ff < .00001].index].join(fp)
selected.sort('p')



    


















    
Out[16]:










  
    

      
      H
      p
      q
      mean score
    

    

      Gene_Set
      
      
      
      
    

  
  
    

      REACTOME_CELL_CYCLE
      415.34
      2.52e-92
      3.35e-89
      4.18e-01
    

    

      REACTOME_GPCR_DOWNSTREAM_SIGNALING
      278.96
      1.27e-62
      3.37e-60
      2.18e-02
    

    

      NABA_MATRISOME_ASSOCIATED
      227.53
      2.06e-51
      3.04e-49
      2.02e-02
    

    

      REACTOME_PROCESSING_OF_CAPPED_INTRON_CONTAINING_PRE_MRNA
      216.76
      4.60e-49
      6.12e-47
      4.32e-01
    

    

      KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
      144.05
      3.47e-33
      2.56e-31
      -1.41e-02
    

    

      NABA_ECM_AFFILIATED
      82.47
      1.07e-19
      3.24e-18
      -1.56e-02
    

    

      NABA_CORE_MATRISOME
      79.20
      5.62e-19
      1.66e-17
      2.27e-02
    

    

      KEGG_COMPLEMENT_AND_COAGULATION_CASCADES
      71.09
      3.41e-17
      8.39e-16
      -1.02e-01
    

    

      KEGG_CELL_ADHESION_MOLECULES_CAMS
      65.29
      6.46e-16
      1.36e-14
      -1.69e-02
    

    

      KEGG_RNA_DEGRADATION
      62.89
      2.19e-15
      4.22e-14
      3.40e-01
    

    

      REACTOME_TRNA_AMINOACYLATION
      54.13
      1.88e-13
      2.77e-12
      3.62e-01
    

    

      REACTOME_PEPTIDE_CHAIN_ELONGATION
      40.34
      2.14e-10
      2.24e-09
      1.15e-02
    

    

      REACTOME_PHASE1_FUNCTIONALIZATION_OF_COMPOUNDS
      35.89
      2.08e-09
      1.98e-08
      -1.71e-02
    

    

      REACTOME_BIOLOGICAL_OXIDATIONS
      33.62
      6.70e-09
      6.11e-08
      2.95e-02
    

    

      REACTOME_MITOCHONDRIAL_PROTEIN_IMPORT
      33.46
      7.29e-09
      6.55e-08
      2.94e-01
    

    

      KEGG_BASAL_TRANSCRIPTION_FACTORS
      32.91
      9.67e-09
      8.58e-08
      3.24e-01
    

    

      REACTOME_GENERIC_TRANSCRIPTION_PATHWAY
      27.15
      1.88e-07
      1.39e-06
      1.80e-01
    

    

      REACTOME_INTERFERON_GAMMA_SIGNALING
      25.04
      5.61e-07
      3.87e-06
      3.69e-03
    

    

      KEGG_DILATED_CARDIOMYOPATHY
      22.09
      2.60e-06
      1.59e-05
      3.25e-02
    

    

      REACTOME_RESPIRATORY_ELECTRON_TRANSPORT
      19.97
      7.86e-06
      4.52e-05
      2.29e-01
    

  




















In [17]:



    


selected.to_csv(FIGDIR + 'pcna_gene_sets_selected.csv')



    











In [18]:



    


f2 = fp.ix[ti(rr.q < .0001)]
ff_u = filter_pathway_hits(fp.ix[ti(f2>0)].order()[::-1], gs2)
ff_p = filter_pathway_hits(fp.ix[ti(f2<0)].order(), gs2)
ff = ff_u.append(ff_p)

selected = rr.ix[ff.index].join(f2)
selected.sort('p')



    


















    
Out[18]:










  
    

      
      H
      p
      q
      mean score
    

  
  
    

      KEGG_OLFACTORY_TRANSDUCTION
      162.64
      3.00e-37
      3.07e-35
      2.21e-02
    

    

      REACTOME_MITOTIC_PROMETAPHASE
      142.12
      9.16e-33
      6.42e-31
      5.37e-01
    

    

      NABA_CORE_MATRISOME
      79.20
      5.62e-19
      1.66e-17
      2.27e-02
    

    

      REACTOME_EXTENSION_OF_TELOMERES
      61.80
      3.80e-15
      6.83e-14
      5.69e-01
    

    

      KEGG_GRAFT_VERSUS_HOST_DISEASE
      60.74
      6.51e-15
      1.15e-13
      -1.28e-01
    

    

      BIOCARTA_PROTEASOME_PATHWAY
      57.76
      2.96e-14
      4.63e-13
      4.42e-01
    

    

      REACTOME_PROCESSING_OF_CAPPED_INTRONLESS_PRE_MRNA
      45.54
      1.50e-11
      1.79e-10
      4.63e-01
    

    

      REACTOME_DEADENYLATION_DEPENDENT_MRNA_DECAY
      43.52
      4.19e-11
      4.85e-10
      3.41e-01
    

    

      PID_ATM_PATHWAY
      41.50
      1.18e-10
      1.29e-09
      4.11e-01
    

    

      REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES
      40.49
      1.97e-10
      2.08e-09
      -4.27e-02
    

    

      REACTOME_G0_AND_EARLY_G1
      40.11
      2.40e-10
      2.50e-09
      5.43e-01
    

    

      KEGG_JAK_STAT_SIGNALING_PATHWAY
      40.06
      2.46e-10
      2.54e-09
      3.33e-02
    

    

      REACTOME_RNA_POL_I_PROMOTER_OPENING
      38.13
      6.63e-10
      6.68e-09
      2.90e-01
    

    

      KEGG_RIBOSOME
      37.75
      8.03e-10
      8.03e-09
      1.52e-02
    

    

      NABA_ECM_REGULATORS
      35.94
      2.03e-09
      1.95e-08
      4.69e-02
    

    

      REACTOME_BIOLOGICAL_OXIDATIONS
      33.62
      6.70e-09
      6.11e-08
      2.95e-02
    

    

      REACTOME_MITOCHONDRIAL_PROTEIN_IMPORT
      33.46
      7.29e-09
      6.55e-08
      2.94e-01
    

    

      REACTOME_UNWINDING_OF_DNA
      32.07
      1.48e-08
      1.31e-07
      7.53e-01
    

    

      REACTOME_CYTOSOLIC_TRNA_AMINOACYLATION
      31.57
      1.92e-08
      1.63e-07
      3.66e-01
    

    

      REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT
      27.82
      1.33e-07
      9.98e-07
      -2.15e-01
    

    

      REACTOME_FORMATION_OF_FIBRIN_CLOT_CLOTTING_CASCADE
      26.98
      2.06e-07
      1.51e-06
      -7.95e-02
    

    

      KEGG_PPAR_SIGNALING_PATHWAY
      25.95
      3.51e-07
      2.45e-06
      2.41e-03
    

    

      REACTOME_RNA_POL_I_TRANSCRIPTION_INITIATION
      24.91
      6.01e-07
      4.10e-06
      3.41e-01
    

    

      REACTOME_BILE_ACID_AND_BILE_SALT_METABOLISM
      23.80
      1.07e-06
      7.03e-06
      -4.40e-02
    

    

      BIOCARTA_DC_PATHWAY
      22.97
      1.65e-06
      1.06e-05
      -6.99e-02
    

    

      KEGG_DILATED_CARDIOMYOPATHY
      22.09
      2.60e-06
      1.59e-05
      3.25e-02
    

    

      REACTOME_RESPIRATORY_ELECTRON_TRANSPORT
      19.97
      7.86e-06
      4.52e-05
      2.29e-01
    

    

      REACTOME_NITRIC_OXIDE_STIMULATES_GUANYLATE_CYCLASE
      18.78
      1.47e-05
      8.09e-05
      -4.35e-02
    

    

      REACTOME_GASTRIN_CREB_SIGNALLING_PATHWAY_VIA_PKC_AND_MAPK
      18.77
      1.47e-05
      8.09e-05
      6.64e-02
    

    

      KEGG_LINOLEIC_ACID_METABOLISM
      18.69
      1.54e-05
      8.38e-05
      -3.85e-02
    

  




















In [19]:



    


selected.to_csv(FIGDIR + 'pcna_gene_sets_selected_fc.csv')

Gene level detrended fraction upregulated statistic





In [31]:



    


gs2 = gene_sets.ix[f_win.dropna().index].fillna(0)
rr = screen_feature(f_win, rev_kruskal, gs2.T, 
                    align=False)
fp = (1.*gene_sets.T * f_win).T.dropna().replace(0, np.nan).mean().order()
fp.name = 'mean score'



    











In [35]:



    


(rr.q < .00001).value_counts()



    


















    
Out[35]:








False    1271
True       59
dtype: int64

















In [22]:



    


rr.join(fp).to_csv(FIGDIR + 'detrended_fup_sets.csv')



    











In [23]:



    


f2 = fp.ix[ti(rr.q < .0001)]
ff_u = filter_pathway_hits(rr.ix[ti(f2>0)].p.order(), gs2)
ff_p = filter_pathway_hits(rr.ix[ti(f2<0)].p.order(), gs2)
ff = ff_u.append(ff_p)

selected = rr.ix[ff[ff < .00001].index].join(fp)
selected.sort('p')



    


















    
Out[23]:










  
    

      
      H
      p
      q
      mean score
    

  
  
    

      REACTOME_TRANSLATION
      158.08
      2.97e-36
      3.86e-33
      0.13
    

    

      KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS
      82.42
      1.10e-19
      1.22e-17
      0.10
    

    

      KEGG_FATTY_ACID_METABOLISM
      42.00
      9.14e-11
      5.79e-09
      -0.13
    

    

      REACTOME_INTERFERON_ALPHA_BETA_SIGNALING
      41.59
      1.13e-10
      6.81e-09
      0.11
    

    

      KEGG_LYSOSOME
      38.06
      6.86e-10
      3.51e-08
      0.07
    

    

      REACTOME_GENERIC_TRANSCRIPTION_PATHWAY
      34.89
      3.48e-09
      1.36e-07
      -0.03
    

    

      REACTOME_VIF_MEDIATED_DEGRADATION_OF_APOBEC3G
      34.13
      5.16e-09
      1.96e-07
      0.09
    

    

      REACTOME_ASPARAGINE_N_LINKED_GLYCOSYLATION
      29.17
      6.63e-08
      1.76e-06
      0.07
    

    

      REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION
      21.82
      2.99e-06
      5.45e-05
      0.08
    

  




















In [24]:



    


selected.to_csv(FIGDIR + 'detrended_fup_sets_selected.csv')



    











In [25]:



    


f2 = fp.ix[ti(rr.q < .0001)]
ff_u = filter_pathway_hits(fp.ix[ti(f2>0)].order()[::-1], gs2)
ff_p = filter_pathway_hits(fp.ix[ti(f2<0)].order(), gs2)
ff = ff_u.append(ff_p)

selected = rr.ix[ff.index].join(f2)
selected.sort('p')



    


















    
Out[25]:










  
    

      
      H
      p
      q
      mean score
    

  
  
    

      KEGG_RIBOSOME
      144.04
      3.47e-33
      1.16e-30
      0.16
    

    

      REACTOME_PACKAGING_OF_TELOMERE_ENDS
      45.35
      1.65e-11
      1.15e-09
      0.12
    

    

      KEGG_FATTY_ACID_METABOLISM
      42.00
      9.14e-11
      5.79e-09
      -0.13
    

    

      KEGG_LYSOSOME
      38.06
      6.86e-10
      3.51e-08
      0.07
    

    

      KEGG_GRAFT_VERSUS_HOST_DISEASE
      35.52
      2.52e-09
      1.05e-07
      0.11
    

    

      REACTOME_GENERIC_TRANSCRIPTION_PATHWAY
      34.89
      3.48e-09
      1.36e-07
      -0.03
    

    

      KEGG_PROTEASOME
      31.14
      2.40e-08
      7.43e-07
      0.09
    

    

      REACTOME_N_GLYCAN_TRIMMING_IN_THE_ER_AND_CALNEXIN_CALRETICULIN_CYCLE
      24.76
      6.48e-07
      1.35e-05
      0.18
    

    

      REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION
      21.82
      2.99e-06
      5.45e-05
      0.08
    

  




















In [26]:



    


selected.to_csv(FIGDIR + 'detrended_fup_sets_selected_fc.csv')

Diggining into the different telomere gene subsets





In [27]:



    


[g for g in gs2 if 'TELO' in g]



    


















    
Out[27]:








['PID_TELOMERASE_PATHWAY',
 'REACTOME_EXTENSION_OF_TELOMERES',
 'REACTOME_PACKAGING_OF_TELOMERE_ENDS',
 'REACTOME_TELOMERE_MAINTENANCE']

















In [28]:



    


a = gs2['REACTOME_PACKAGING_OF_TELOMERE_ENDS']>0
b = gs2['REACTOME_EXTENSION_OF_TELOMERES']>0
a.name = 'end packaging'
b.name = 'extension'
cc = combine(a,b).replace('neither', np.nan).dropna()



    











In [29]:



    


fig, ax = subplots(figsize=(4,4))
series_scatter(dx_rna.frac, pcna_corr.ix[ti(cc == 'end packaging')],
               ax=ax, color=colors[0], ann=None, alpha=1, s=30)
series_scatter(dx_rna.frac, pcna_corr.ix[ti(cc == 'extension')],
               ax=ax, color=colors[1], ann=None, alpha=1, s=30)
ax.set_xlabel('Fraction Upregulated')
ax.set_ylabel('Proliferation Score')
ax.legend(['end packaging','extension'], loc='upper left', frameon=True)
prettify_ax(ax)
fig.tight_layout()
fig.savefig(FIGDIR + 'S3_Fig.pdf')



    


















    
























In [30]:



    


violin_plot_pandas(cc, f_win)

Content source: theandygross/TCGA_differential_expression

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