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Snakefile
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Snakefile
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import pandas as pd
import os
import sys
sys.path.append(os.path.join(os.path.dirname(workflow.snakefile),'scripts'))
from common import effect_plot as EP
# The main entry point of your workflow.
# After configuring, running snakemake -n in a clone of this repository should successfully execute a dry-run of the workflow.
#configfile: "config.yaml"
Comparisons= config['Comparisons'].keys()
rule all:
input:
'data.tsv',
'metadata.tsv',
expand("Comparisons/{comparison}/{file}",
comparison=Comparisons,
file=['stats_aldex.tsv','aldex_plot.pdf','stats_relab.tsv'] ),
"Comparisons/cobined_stats_aldex.tsv",
"Comparisons/cobined_stats_relab.tsv"
#"Correlations/rho.tsv"
localrules: init
rule init:
input:
config['data'],
config['metadata']
output:
'data.tsv',
'metadata.tsv'
run:
D= pd.read_table(input[0],index_col=0)
metadata= pd.read_table(input[1],index_col=0)
subset= D.index.intersection(metadata.index)
metadata.loc[subset].to_csv(output[1],sep='\t')
D=D.loc[subset]
D= D.loc[:,D.mean()> config['min_count']]
D.to_csv(output[0],sep='\t')
rule aldex:
input:
data='data.tsv',
metadata='metadata.tsv'
output:
expand("Comparisons/{comparison}/stats_aldex.tsv",comparison=Comparisons )
log:
"logs/aldex2.txt"
params:
output_folder= "Comparisons"
script:
"scripts/Rscripts/aldex.R"
rule aldex_plot:
input:
"Comparisons/{comparison}/stats_aldex.tsv"
output:
"Comparisons/{comparison}/aldex_plot.pdf"
run:
import matplotlib
import matplotlib.pylab as plt
matplotlib.rcParams['pdf.fonttype']=42
S= pd.read_table(input[0])
EP.aldex_plot(S)
plt.suptitle(wildcards.comparison)
plt.savefig(output[0])
rule combine_stats:
input:
expand("Comparisons/{comparison}/stats_aldex.tsv",comparison=config['Comparisons'].keys() )
output:
"Comparisons/cobined_stats_aldex.tsv"
params:
comparisons = config['Comparisons'].keys()
run:
S={}
for i, comparison in enumerate(params.comparisons):
S[comparison] = pd.read_table(input[i])
S=pd.concat(S,axis=1,sort=True)
S.columns= S.columns.swaplevel()
S.sort_index(axis=1,inplace=True)
S.to_csv(output[0],sep='\t')
rule propr_rho:
input:
data='data.tsv',
#metadata='metadata.tsv'
output:
"Correlations/rho.tsv"
log:
"logs/propr_rho.txt"
params:
output_folder= lambda wc,output: os.path.dirname(output[0])
script:
"scripts/Rscripts/correlations.R"
rule relab_analysis:
input:
data='data.tsv',
metadata='metadata.tsv'
output:
expand("Comparisons/{comparison}/stats_relab.tsv",comparison=Comparisons )
params:
Comparisons= Comparisons,
output_folder= "Comparisons"
run:
import helper_scripts as hs
import numpy as np
def calculate_pseudo_count(data,a=0.65):
M= data.values
return M[np.nonzero(M)].min()*a
metadata= pd.read_table(input.metadata,index_col=0)
data= pd.read_table(input.data,index_col=0)
rel_data= (data.T/data.sum(1)).T
pseudo_count= calculate_pseudo_count(rel_data)
grouping_variable= metadata[config['grouping_variable']]
for comparison_name in params.Comparisons:
compared_groups = config['Comparisons'][comparison_name]
V= hs.Viewpoint(rel_data,grouping_variable,Pairwise_Sig='kruskal',
Selection_rows=grouping_variable.isin(compared_groups),
order=compared_groups )
S= V.Sumarize_Table(correction="Benjamini-Hochberg")
S[('log2FC','log2FC')]= np.log2(S[(compared_groups[1] ,'median')] +pseudo_count) - np.log2(S[(compared_groups[0] ,'median')] +pseudo_count)
S.to_csv(os.path.join(params.output_folder,comparison_name,'stats_relab.tsv'),sep='\t')
rule combine_relb_stats:
input:
expand("Comparisons/{comparison}/stats_relab.tsv",comparison=config['Comparisons'].keys() )
output:
"Comparisons/cobined_stats_relab.tsv"
params:
comparisons = config['Comparisons'].keys()
run:
S={}
for i, comparison in enumerate(params.comparisons):
d= pd.read_table(input[i],index_col=0,header=[0,1])
d= d.loc[:,['median_diff','P_values','P Benjamini-Hochberg','log2FC']]
d.columns= d.columns.droplevel(level=1)
S[comparison] = d
S=pd.concat(S,axis=1,sort=True)
S.columns= S.columns.swaplevel()
S.sort_index(axis=1,inplace=True)
S.to_csv(output[0],sep='\t')