In [1]:

    

%run ~/relmapping/annot/notebooks/__init__.ipynb


    

    




/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
  from pandas.core import datetools






    




os.getcwd(): /mnt/beegfs/scratch_copy/ahringer/jj374/lab/relmapping

In [2]:

    

df_genes = pd.read_csv('WS260_ce10/WS260_ce10.genes_by_CV.tsv', sep='\t').drop_duplicates(subset=['gene_id'])
print('%d gene records loaded' % (len(df_genes),))

fp_regl = 'annot/S2_regulatory_annotation/S2_regulatory_annotation.tsv'
df_regl = pd.read_csv(fp_regl, sep='\t')
print('%d regions loaded' % (len(df_regl),))


    

    




46742 gene records loaded
42245 regions loaded

In [3]:

    

l_ = list(df_regl.query('annot_fwd == "coding_promoter"')['promoter_gene_id_fwd'])\
   + list(df_regl.query('annot_rev == "coding_promoter"')['promoter_gene_id_rev'])
s_ = pd.Series(list(filter(lambda gene_id: gene_id != '.', l_)))
df_ = pd.DataFrame(s_.value_counts(), columns=['n_promoter'])
pr_ = df_genes.merge(df_, left_on='gene_id', right_index=True, how='left')['n_promoter']
df_genes['n_promoter'] = list(map(lambda n: n if n==n else 0, pr_))
df_genes.head()


    

    
Out[3]:







  

    

      

      
chrom
      
source
      
feature
      
start
      
end
      
score
      
strand
      
frame
      
gene_id
      
gene_source
      
...
      
Gerstein2014_L4
      
Gerstein2014_YA
      
Gerstein2014_CV
      
Latorre2015_oscillatory
      
Gerstein2014_max_rank
      
Gerstein2014_top15k_CV_rank
      
color
      
operon_id
      
operon_rank
      
n_promoter
    
  
  

    

      
0
      
chrI
      
WormBase
      
gene
      
3747
      
3909
      
.
      
-
      
.
      
WBGene00023193
      
WormBase
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
#56b4e9
      
.
      
0
      
0.0
    
    

      
1
      
chrI
      
WormBase
      
gene
      
4116
      
10230
      
.
      
-
      
.
      
WBGene00022277
      
WormBase
      
...
      
17.771486
      
29.094286
      
0.232
      
False
      
7674.0
      
4526.5
      
#e69f00
      
.
      
0
      
1.0
    
    

      
2
      
chrI
      
WormBase
      
gene
      
11495
      
16837
      
.
      
+
      
.
      
WBGene00022276
      
WormBase
      
...
      
134.416857
      
184.178286
      
0.517
      
False
      
453.0
      
7542.5
      
#0072b2
      
.
      
0
      
1.0
    
    

      
3
      
chrI
      
WormBase
      
gene
      
17487
      
26781
      
.
      
-
      
.
      
WBGene00022278
      
WormBase
      
...
      
19.295486
      
43.583714
      
0.136
      
False
      
5745.0
      
1928.5
      
#e69f00
      
.
      
0
      
1.0
    
    

      
4
      
chrI
      
WormBase
      
gene
      
22882
      
23600
      
.
      
-
      
.
      
WBGene00235381
      
WormBase
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
#56b4e9
      
.
      
0
      
0.0
    
  

5 rows × 27 columns

In [4]:

    

l_ = list(df_regl.query('annot == "putative_enhancer"')['associated_gene_id'])
s_ = pd.Series(list(itertools.chain(*list([l_i_.split(',') for l_i_ in filter(lambda gene_id: gene_id != '.', l_)]))))
df_ = pd.DataFrame(s_.value_counts(), columns=['n_putative_enhancer'])
pe_ = df_genes.merge(df_, left_on='gene_id', right_index=True, how='left')['n_putative_enhancer']
df_genes['n_putative_enhancer'] = list(map(lambda n: n if n==n else 0, pe_))
df_genes.head()


    

    
Out[4]:







  

    

      

      
chrom
      
source
      
feature
      
start
      
end
      
score
      
strand
      
frame
      
gene_id
      
gene_source
      
...
      
Gerstein2014_YA
      
Gerstein2014_CV
      
Latorre2015_oscillatory
      
Gerstein2014_max_rank
      
Gerstein2014_top15k_CV_rank
      
color
      
operon_id
      
operon_rank
      
n_promoter
      
n_putative_enhancer
    
  
  

    

      
0
      
chrI
      
WormBase
      
gene
      
3747
      
3909
      
.
      
-
      
.
      
WBGene00023193
      
WormBase
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
#56b4e9
      
.
      
0
      
0.0
      
0.0
    
    

      
1
      
chrI
      
WormBase
      
gene
      
4116
      
10230
      
.
      
-
      
.
      
WBGene00022277
      
WormBase
      
...
      
29.094286
      
0.232
      
False
      
7674.0
      
4526.5
      
#e69f00
      
.
      
0
      
1.0
      
1.0
    
    

      
2
      
chrI
      
WormBase
      
gene
      
11495
      
16837
      
.
      
+
      
.
      
WBGene00022276
      
WormBase
      
...
      
184.178286
      
0.517
      
False
      
453.0
      
7542.5
      
#0072b2
      
.
      
0
      
1.0
      
2.0
    
    

      
3
      
chrI
      
WormBase
      
gene
      
17487
      
26781
      
.
      
-
      
.
      
WBGene00022278
      
WormBase
      
...
      
43.583714
      
0.136
      
False
      
5745.0
      
1928.5
      
#e69f00
      
.
      
0
      
1.0
      
1.0
    
    

      
4
      
chrI
      
WormBase
      
gene
      
22882
      
23600
      
.
      
-
      
.
      
WBGene00235381
      
WormBase
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
#56b4e9
      
.
      
0
      
0.0
      
0.0
    
  

5 rows × 28 columns

In [5]:

    

df_genes['n_promoter'].value_counts()


    

    
Out[5]:





0.0    35546
1.0     8196
2.0     2063
3.0      628
4.0      215
5.0       67
6.0       19
7.0        7
8.0        1
Name: n_promoter, dtype: int64

In [6]:

    

df_genes['n_putative_enhancer'].value_counts()


    

    
Out[6]:





0.0     40074
1.0      3428
2.0      1361
3.0       626
4.0       375
5.0       218
6.0       138
7.0        94
8.0        88
9.0        66
10.0       46
11.0       42
12.0       39
13.0       28
14.0       17
19.0       13
17.0       13
16.0       12
15.0       12
18.0       10
20.0       10
21.0        6
24.0        4
23.0        4
22.0        4
25.0        3
26.0        3
28.0        2
27.0        1
33.0        1
36.0        1
34.0        1
38.0        1
31.0        1
Name: n_putative_enhancer, dtype: int64

In [8]:

    

plt.figure(figsize=(2,3))
plt.boxplot([
    df_genes.query('n_promoter == 1')['n_putative_enhancer'],
    df_genes.query('n_promoter == 2')['n_putative_enhancer'],
    df_genes.query('n_promoter >= 3')['n_putative_enhancer'],
],
    labels = ['1', '2', '>2'],
    sym='', # Do not show outliers -- by eye, <100 in each group...
    widths=0.7,
)
plt.xlabel('Number of promoters')
plt.ylabel('Number of enhancers')
plt.gca().set_ylim(-1, 17)
plt.yticks([0,5,10,15])
plt.savefig('annot_Apr27/Fig2C_npromoter_vs_nenhancer.pdf', bbox_inches='tight', transparent=True)


    

    




/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead
  return getattr(obj, method)(*args, **kwds)






    

In [ ]: