import os
import pandas as pd
from pathlib import Path
import numpy as np
[docs]def run(RunnerObj):
'''
Function to run SCRIBE algorithm.
To see all the inputs runScribe.R script takes, run:
docker run scribe:base /bin/sh -c "Rscript runScribe.R -h"
'''
inputPath = "data"+str(RunnerObj.inputDir).split(str(Path.cwd()))[1]+"/SCRIBE/"
# required inputs
delay = str(RunnerObj.params['delay'])
method = str(RunnerObj.params['method'])
low = str(RunnerObj.params['lowerDetectionLimit'])
fam = str(RunnerObj.params['expressionFamily'])
# optional inputs
log = str(RunnerObj.params['log'])
ignorePT = str(RunnerObj.params['ignorePT'])
# make output dirs if they do not exist:
outDir = "outputs/"+str(RunnerObj.inputDir).split("inputs/")[1]+"/SCRIBE/"
os.makedirs(outDir, exist_ok = True)
# Build the command to run Scribe
PTData = pd.read_csv(RunnerObj.inputDir.joinpath(RunnerObj.cellData),
header = 0, index_col = 0)
colNames = PTData.columns
for idx in range(len(colNames)):
# Specify file names for inputs and outputs
exprName = "ExpressionData"+str(idx)+".csv"
cellName = "CellData"+str(idx)+".csv"
outFile = "outFile"+str(idx)+".csv"
timeFile = 'time'+str(idx)+".txt"
cmdToRun = ' '.join(['docker run --rm -v', str(Path.cwd())+':/data/ scribe:base /bin/sh -c \"time -v -o', "data/" + str(outDir) + timeFile, 'Rscript runScribe.R',
'-e',inputPath +exprName, '-c',inputPath + cellName,
'-g',inputPath + 'GeneData.csv', '-o data/'+outDir, '-d',delay, '-l', low,
'-m', method, '-x',fam, '--outFile '+outFile])
if str(RunnerObj.params['log']) == 'True':
cmdToRun += ' --log'
if str(RunnerObj.params['ignorePT']) == 'True':
cmdToRun += ' -i'
cmdToRun += '\"'
print(cmdToRun)
os.system(cmdToRun)
[docs]def parseOutput(RunnerObj):
'''
Function to parse outputs from SCRIBE.
'''
outDir = "outputs/"+str(RunnerObj.inputDir).split("inputs/")[1]+"/SCRIBE/"
PTData = pd.read_csv(RunnerObj.inputDir.joinpath(RunnerObj.cellData),
header = 0, index_col = 0)
colNames = PTData.columns
OutSubDF = [0]*len(colNames)
for idx in range(len(colNames)):
# Read output
outFile = 'outFile'+str(idx)+'.csv'
if not Path(outDir+outFile).exists():
# Quit if output file does not exist
print(outDir+outFile+' does not exist, skipping...')
return
OutSubDF[idx] = pd.read_csv(outDir+outFile, sep = ' ', header = None)
# megre the dataframe by taking the maximum value from each DF
# From here: https://stackoverflow.com/questions/20383647/pandas-selecting-by-label-sometimes-return-series-sometimes-returns-dataframe
outDF = pd.concat(OutSubDF)
outDF.columns= ['Gene1','Gene2','EdgeWeight']
# Group by rows code is from here:
# https://stackoverflow.com/questions/53114609/pandas-how-to-remove-duplicate-rows-but-keep-all-rows-with-max-value
res = outDF[outDF['EdgeWeight'] == outDF.groupby(['Gene1','Gene2'])['EdgeWeight'].transform('max')]
# Sort values in the dataframe
finalDF = res.sort_values('EdgeWeight',ascending=False)
finalDF.to_csv(outDir+'rankedEdges.csv',sep='\t', index = False)