renamed files with shorter names and corrected paths for input and output files
This commit is contained in:
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4be0de97d7
commit
61fcd14b17
8 changed files with 244 additions and 164 deletions
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@ -1,6 +1,9 @@
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# run step0-step3a for mcsm pipeline
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#!/bin/bash
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# run all bash scripts for mcsm
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#./step0_check_duplicate_SNPs.sh
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#./step1_mCSMLig_curl_submit_store_outputurl.sh
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./step2_mCSM_LIG_batch_outputurls_results.sh
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./step3a_mCSM_LIG_regex_output_formatting.sh
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#./step1_lig_output_urls.sh
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./step2_lig_results.sh
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./step3a_results_format_interim.sh
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@ -24,7 +24,7 @@ infile_mut="/pnca_mis_SNPs_v2_unique.csv"
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infile_struc="/complex1_no_water.pdb"
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outpath="${inpath}${processed_path}"
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outfile="/mCSM_lig_complex1_result_url.txt"
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outfile="/complex1_result_url.txt"
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# create valid input and output filenames
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#filename="${HOME}/git/Data/pyrazinamide/input/processed/pnca_mis_SNPs_v2_unique.csv"
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@ -82,7 +82,7 @@ echo -e "${mutation} : processing entry ${COUNT}/$(wc -l < ${filename})..."
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# create output file with the added number of muts from file
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# after much thought, bad idea as less generic!
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#echo -e "${host}${result_url}" >> ../Results/$(wc -l < ${filename})_mCSM_lig_complex1_result_url.txt
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#echo -e "${host}${result_url}" >> ../Results/$(wc -l < ${filename})_complex1_result_url.txt
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echo -e "${host}${result_url}" >> ${outfilename}
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#echo -n '.'
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done < "${filename}"
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@ -35,10 +35,10 @@
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# specify variables for input and output paths and filenames
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inpath="${HOME}/git/Data/pyrazinamide/input"
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processed_path="/processed"
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infile="/mCSM_lig_complex1_result_url.txt"
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infile="/complex1_result_url.txt"
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outpath="${inpath}${processed_path}"
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outfile="/mCSM_lig_complex1_output_MASTER.txt"
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outfile="/complex1_output_MASTER.txt"
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# create valid input and output filenames
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filename="${inpath}${processed_path}${infile}"
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@ -27,12 +27,11 @@ inpath="${HOME}/git/Data/pyrazinamide/input"
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processed_path="/processed"
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# Create input file: copy and rename output file of step2
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oldfile="${inpath}${processed_path}/mCSM_lig_complex1_output_MASTER.txt"
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newfile="${inpath}${processed_path}/mCSM_lig_complex1_output_processed.txt"
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oldfile="${inpath}${processed_path}/complex1_output_MASTER.txt"
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newfile="${inpath}${processed_path}/complex1_output_processed.txt"
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cp $oldfile $newfile
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#infile="../Results/336_mCSM_lig_complex1_output_processed.txt"
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infile="/mCSM_lig_complex1_output_processed.txt"
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infile="/complex1_output_processed.txt"
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filename="${inpath}${processed_path}${infile}"
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echo Input filename is : ${filename}
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@ -1,29 +0,0 @@
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#!/usr/bin/python
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import pandas as pd
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from collections import defaultdict
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#file = r'../Results/322_mCSM_lig_complex1_output_processed.txt'
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outCols=[
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'PredictedAffinityChange',
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'Mutationinformation',
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'Wild-type',
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'Position',
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'Mutant-type',
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'Chain',
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'LigandID',
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'Distancetoligand',
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'DUETstabilitychange'
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]
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lines = [line.rstrip('\n') for line in open('../Results/336_mCSM_lig_complex1_output_processed.txt')]
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outputs = defaultdict(list)
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for item in lines:
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col, val = item.split(':')
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outputs[col].append(val)
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dfOut=pd.DataFrame(outputs)
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pd.DataFrame.to_csv(dfOut,'../Results/336_complex1_formatted_results.csv', columns=outCols)
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61
mcsm_analysis/pyrazinamide/scripts/mcsm/step3b_results_format_df.py
Executable file
61
mcsm_analysis/pyrazinamide/scripts/mcsm/step3b_results_format_df.py
Executable file
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@ -0,0 +1,61 @@
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#!/usr/bin/python
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###################
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# load libraries
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import os, sys
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import pandas as pd
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from collections import defaultdict
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####################
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#********************************************************************
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# TASK: Formatting results with nice colnames
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# step3a processed the mcsm results to remove all newlines and
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# brought data in a format where the delimiter ":" splits
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# data into a convenient format of "colname": "value".
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# this script formats the data and outputs a df with each row
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# as a mutation and its corresponding mcsm_values
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# Requirements:
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# input: output of step3a, file containing "..._output_processed.txt"
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# path: "Data/<drug>/input/processed/<filename>"
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# output: formatted .csv file
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# path: "Data/<drug>/input/processed/<filename>"
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#***********************************************************************
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# specify variables for input and output paths and filenames
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homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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basedir = "/git/Data/pyrazinamide/input"
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inpath = "/processed"
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in_filename = "/complex1_output_processed.txt"
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infile = homedir + basedir + inpath + in_filename
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print("Input file is:", infile)
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outpath = "/processed"
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out_filename = "/complex1_formatted_results.csv"
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outfile = homedir + basedir + outpath + out_filename
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print("Output file is:", outfile)
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# end of variable assignment for input and output files
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outCols=[
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'PredictedAffinityChange',
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'Mutationinformation',
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'Wild-type',
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'Position',
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'Mutant-type',
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'Chain',
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'LigandID',
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'Distancetoligand',
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'DUETstabilitychange'
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]
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lines = [line.rstrip('\n') for line in open(infile)]
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outputs = defaultdict(list)
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for item in lines:
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col, val = item.split(':')
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outputs[col].append(val)
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dfOut=pd.DataFrame(outputs)
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pd.DataFrame.to_csv(dfOut, outfile, columns=outCols)
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@ -1,22 +1,42 @@
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getwd()
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#setwd("~/Documents/git/LSHTM_analysis/mcsm_complex1/Results") # work
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setwd("~/git/LSHTM_analysis/mcsm_complex1/Results") # thinkpad
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#setwd("/Users/tanu/git/LSHTM_analysis/mcsm_complex1/Results") # mac
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#setwd("~/git/LSHTM_analysis/mcsm_complex1/Results")
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getwd()
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#=======================================================
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# TASK: read formatted_results_df.csv to complete
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# missing info, adding DUET categories, assigning
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# meaningful colnames, etc.
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# Requirements:
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# input: output of step3b, python processing,
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# path: Data/<drug>/input/processed/<filename>"
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# output: NO output as the next scripts refers to this
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# for yet more processing
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#=======================================================
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# specify variables for input and output paths and filenames
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homedir = "~"
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basedir = "/git/Data/pyrazinamide/input"
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inpath = "/processed"
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in_filename = "/complex1_formatted_results.csv"
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infile = paste0(homedir, basedir, inpath, in_filename)
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print(paste0("Input file is:", infile))
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#======================================================
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#TASK: To tidy the columns so you can generate figures
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#=======================================================
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####################
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#### read file #####: this will be the output from python script (csv file)
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####################
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data = read.csv("336_complex1_formatted_results.csv"
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data = read.csv(infile
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, header = T
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, stringsAsFactors = FALSE)
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dim(data)
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#335, 10
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str(data)
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# clear variables
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rm(homedir, basedir, inpath, in_filename, infile)
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###########################
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##### Data processing #####
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###########################
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@ -31,41 +51,38 @@ data$Mutationinformation = paste0(data$Wild.type, data$Position, data$Mutant.typ
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head(data$Mutationinformation)
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tail(data$Mutationinformation)
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#write.csv(data, 'test.csv')
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##########################################
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# Remove duplicate SNPs as a sanity check
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##########################################
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#very important
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# very important
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table(duplicated(data$Mutationinformation))
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#FALSE
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#335
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#extract duplicated entries
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# extract duplicated entries
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dups = data[duplicated(data$Mutationinformation),] #0
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#No of dups should match with the no. of TRUE in the above table
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# No of dups should match with the no. of TRUE in the above table
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#u_dups = unique(dups$Mutationinformation) #10
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sum( table(dups$Mutationinformation) ) #13
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rm(dups)
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sum( table(dups$Mutationinformation) )
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#***************************************************************
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#select non-duplicated SNPs and create a new df
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df = data[!duplicated(data$Mutationinformation),] #309, 10
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# select non-duplicated SNPs and create a new df
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df = data[!duplicated(data$Mutationinformation),]
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#***************************************************************
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#sanity check
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# sanity check
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u = unique(df$Mutationinformation)
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u2 = unique(data$Mutationinformation)
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table(u%in%u2)
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#TRUE
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#309
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#should all be 1, hence 309 1's
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# should all be 1
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sum(table(df$Mutationinformation) == 1)
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#sort df by Position
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#MANUAL CHECKPOINT:
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# sort df by Position
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# MANUAL CHECKPOINT:
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#foo <- df[order(df$Position),]
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#df <- df[order(df$Position),]
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# clear variables
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rm(u, u2, dups)
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####################
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@ -75,26 +92,28 @@ rm(u, u2, dups)
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#=======
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#STEP 1
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#========
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#make a copy of the PredictedAffinityColumn and call it Lig_outcome
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df$Lig_outcome = df$PredictedAffinityChange #335, 11
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# make a copy of the PredictedAffinityColumn and call it Lig_outcome
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df$Lig_outcome = df$PredictedAffinityChange
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#make Predicted...column numeric and outcome column categorical
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#make Predicted...column numeric and outcome column categorical
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head(df$PredictedAffinityChange)
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df$PredictedAffinityChange = gsub("log.*"
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, ""
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, df$PredictedAffinityChange)
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#sanity checks
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# sanity checks
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head(df$PredictedAffinityChange)
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#should be numeric, check and if not make it numeric
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is.numeric( df$PredictedAffinityChange )
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#change to numeric
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df$PredictedAffinityChange = as.numeric(df$PredictedAffinityChange)
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#should be TRUE
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# should be numeric, check and if not make it numeric
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is.numeric( df$PredictedAffinityChange )
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#change the column name to indicate units
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# change to numeric
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df$PredictedAffinityChange = as.numeric(df$PredictedAffinityChange)
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# should be TRUE
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is.numeric( df$PredictedAffinityChange )
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# change the column name to indicate units
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n = which(colnames(df) == "PredictedAffinityChange"); n
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colnames(df)[n] = "PredAffLog"
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colnames(df)[n]
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@ -102,38 +121,44 @@ colnames(df)[n]
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#========
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#STEP 2
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#========
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#make Lig_outcome column categorical showing effect of mutation
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# make Lig_outcome column categorical showing effect of mutation
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head(df$Lig_outcome)
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df$Lig_outcome = gsub("^.*-"
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, "",
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df$Lig_outcome)
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#sanity checks
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# sanity checks
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head(df$Lig_outcome)
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#should be factor, check and if not change it to factor
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# should be factor, check and if not change it to factor
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is.factor(df$Lig_outcome)
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#change to factor
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# change to factor
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df$Lig_outcome = as.factor(df$Lig_outcome)
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#should be TRUE
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# should be TRUE
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is.factor(df$Lig_outcome)
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#========
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#STEP 3
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#========
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#gsub
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# gsub
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head(df$Distancetoligand)
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df$Distancetoligand = gsub("Å"
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, ""
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, df$Distancetoligand)
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#sanity checks
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# sanity checks
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head(df$Distancetoligand)
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#should be numeric, check if not change it to numeric
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is.numeric(df$Distancetoligand)
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#change to numeric
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df$Distancetoligand = as.numeric(df$Distancetoligand)
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#should be TRUE
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# should be numeric, check if not change it to numeric
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is.numeric(df$Distancetoligand)
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#change the column name to indicate units
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# change to numeric
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df$Distancetoligand = as.numeric(df$Distancetoligand)
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# should be TRUE
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is.numeric(df$Distancetoligand)
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# change the column name to indicate units
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n = which(colnames(df) == "Distancetoligand")
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colnames(df)[n] <- "Dis_lig_Ang"
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colnames(df)[n]
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@ -146,16 +171,19 @@ head(df$DUETstabilitychange)
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df$DUETstabilitychange = gsub("Kcal/mol"
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, ""
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, df$DUETstabilitychange)
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#sanity checks
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# sanity checks
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head(df$DUETstabilitychange)
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#should be numeric, check if not change it to numeric
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is.numeric(df$DUETstabilitychange)
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#change to numeric
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df$DUETstabilitychange = as.numeric(df$DUETstabilitychange)
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#should be TRUE
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# should be numeric, check if not change it to numeric
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is.numeric(df$DUETstabilitychange)
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#change the column name to indicate units
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# change to numeric
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df$DUETstabilitychange = as.numeric(df$DUETstabilitychange)
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# should be TRUE
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is.numeric(df$DUETstabilitychange)
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# change the column name to indicate units
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n = which(colnames(df) == "DUETstabilitychange"); n
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colnames(df)[n] = "DUETStability_Kcalpermol"
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colnames(df)[n]
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@ -163,25 +191,20 @@ colnames(df)[n]
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#========
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#STEP 5
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#========
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#create yet another extra column: classification of DUET stability only
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# create yet another extra column: classification of DUET stability only
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df$DUET_outcome = ifelse(df$DUETStability_Kcalpermol >=0
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, "Stabilizing"
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, "Destabilizing") #335, 12
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, "Destabilizing") # spelling to be consistent with mcsm
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table(df$Lig_outcome)
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#Destabilizing Stabilizing
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#281 54
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table(df$DUET_outcome)
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#Destabilizing Stabilizing
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#288 47
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#==============================
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#FIXME
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#Insert a venn diagram
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#================================
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#========
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#STEP 6
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#========
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#========
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#STEP 7
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#========
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#create an extra column: maybe useful for some plots
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df$WildPos = paste0(df$Wild_type, df$Position) #335, 13
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# create an extra column: maybe useful for some plots
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df$WildPos = paste0(df$Wild_type, df$Position)
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#clear variables
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# clear variables
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rm(n, wt, mut)
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################ end of data cleaning
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getwd()
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#setwd("~/Documents/git/LSHTM_analysis/mcsm_complex1/Results") # work
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setwd("~/git/LSHTM_analysis/mcsm_complex1/Results") # thinkpad
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#setwd("/Users/tanu/git/LSHTM_analysis/mcsm_complex1/Results") # mac
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##################
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# load libraries
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library(compare)
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##################
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getwd()
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#=======================================================
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#TASK:read cleaned data and perform rescaling
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# TASK:read cleaned data and perform rescaling
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# of DUET stability scores
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# of Pred affinity
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#compare scaling methods with plots
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#output normalised file
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# compare scaling methods with plots
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# Requirements:
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# input: R script, step3c_results_cleaning.R
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# path: Data/<drug>/input/processed/<filename>"
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# output: NO output as the next scripts refers to this
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# for yet more processing
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# output normalised file
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#=======================================================
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# specify variables for input and output paths and filenames
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homedir = "~"
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currdir = "/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/mcsm"
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in_filename = "/step3c_results_cleaning.R"
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infile = paste0(homedir, currdir, in_filename)
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print(paste0("Input file is:", infile))
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# output file
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basedir = "/git/Data/pyrazinamide/input"
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outpath = "/processed"
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out_filename = "/mcsm_complex1_normalised.csv"
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outfile = paste0(homedir, basedir, outpath, out_filename)
|
||||
print(paste0("Output file is:", outfile))
|
||||
|
||||
####################
|
||||
#### read file #####: this will be the output of my R script that cleans the data columns
|
||||
####################
|
||||
source("../Scripts/step3c_data_cleaning.R")
|
||||
##This will outut two dataframes:
|
||||
##data: unclean data: 335, 10
|
||||
##df : cleaned df 335, 13
|
||||
## you can remove data if you want as you will not need it
|
||||
source(infile)
|
||||
|
||||
#This will outut two dataframes:
|
||||
# data: unclean data: 10 cols
|
||||
# df : cleaned df: 13 cols
|
||||
# you can remove data if you want as you will not need it
|
||||
rm(data)
|
||||
|
||||
colnames(df)
|
||||
|
|
@ -36,67 +58,60 @@ group = which(colnames(df) == "Lig_outcome"); group
|
|||
# This is because this makes it easier to see the results of rescaling for debugging
|
||||
head(df$PredAffLog)
|
||||
|
||||
#ORDER BY PredAff scrores: negative values at the top and positive at the bottoom
|
||||
# ORDER BY PredAff scrores: negative values at the top and positive at the bottoom
|
||||
df = df[order(df$PredAffLog),]
|
||||
head(df$PredAffLog)
|
||||
|
||||
#sanity checks
|
||||
head(df[,n]) #all negatives
|
||||
tail(df[,n]) #all positives
|
||||
# sanity checks
|
||||
head(df[,n]) # all negatives
|
||||
tail(df[,n]) # all positives
|
||||
|
||||
#sanity checks
|
||||
# sanity checks
|
||||
mean(df[,n])
|
||||
#-0.9526746
|
||||
|
||||
tapply(df[,n], df[,group], mean)
|
||||
#Destabilizing Stabilizing
|
||||
#-1.2112100 0.3926667
|
||||
|
||||
#===========================
|
||||
#Same as above: in 2 steps
|
||||
# Same as above: in 2 steps
|
||||
#===========================
|
||||
|
||||
#find range of your data
|
||||
my_min = min(df[,n]); my_min #-3.948
|
||||
my_max = max(df[,n]); my_max #2.23
|
||||
# find range of your data
|
||||
my_min = min(df[,n]); my_min #
|
||||
my_max = max(df[,n]); my_max #
|
||||
|
||||
#===============================================
|
||||
# WITHIN GROUP rescaling 2: method "ratio"
|
||||
# create column to store the rescaled values
|
||||
# Rescaling separately (Less dangerous)
|
||||
# =====> chosen one:as Nick prefers
|
||||
# =====> chosen one: preserves sign
|
||||
#===============================================
|
||||
df$ratioPredAff = ifelse(df[,n] < 0
|
||||
, df[,n]/abs(my_min)
|
||||
, df[,n]/my_max
|
||||
)#335 14
|
||||
#sanity checks
|
||||
)# 14 cols
|
||||
# sanity checks
|
||||
head(df$ratioPredAff)
|
||||
tail(df$ratioPredAff)
|
||||
|
||||
min(df$ratioPredAff); max(df$ratioPredAff)
|
||||
|
||||
tapply(df$ratioPredAff, df$Lig_outcome, min)
|
||||
#Destabilizing Stabilizing
|
||||
#-1.000000000 0.005381166
|
||||
|
||||
tapply(df$ratioPredAff, df$Lig_outcome, max)
|
||||
#Destabilizing Stabilizing
|
||||
#-0.001266464 1.000000000
|
||||
|
||||
#should be the same as below (281 and 54)
|
||||
# should be the same as below
|
||||
sum(df$ratioPredAff < 0); sum(df$ratioPredAff > 0)
|
||||
|
||||
table(df$Lig_outcome)
|
||||
#Destabilizing Stabilizing
|
||||
#281 54
|
||||
|
||||
#===============================================
|
||||
# Hist and density plots to compare the rescaling
|
||||
# methods: Base R
|
||||
#===============================================
|
||||
#uncomment as necessary
|
||||
# uncomment as necessary
|
||||
my_title = "Ligand_stability"
|
||||
#my_title = colnames(df[n])
|
||||
# my_title = colnames(df[n])
|
||||
|
||||
# Set the margin on all sides
|
||||
par(oma = c(3,2,3,0)
|
||||
|
|
@ -140,7 +155,7 @@ rm(my_min, my_max, my_title, n, group)
|
|||
#===================
|
||||
# 3b: DUET stability
|
||||
#===================
|
||||
dim(df) #335, 14
|
||||
dim(df) # 14 cols
|
||||
|
||||
n = which(colnames(df) == "DUETStability_Kcalpermol"); n # 10
|
||||
group = which(colnames(df) == "DUET_outcome"); group #12
|
||||
|
|
@ -151,63 +166,53 @@ group = which(colnames(df) == "DUET_outcome"); group #12
|
|||
# This is because this makes it easier to see the results of rescaling for debugging
|
||||
head(df$DUETStability_Kcalpermol)
|
||||
|
||||
#ORDER BY DUET scores: negative values at the top and positive at the bottom
|
||||
# ORDER BY DUET scores: negative values at the top and positive at the bottom
|
||||
df = df[order(df$DUETStability_Kcalpermol),]
|
||||
|
||||
#sanity checks
|
||||
head(df[,n]) #negatives
|
||||
tail(df[,n]) #positives
|
||||
# sanity checks
|
||||
head(df[,n]) # negatives
|
||||
tail(df[,n]) # positives
|
||||
|
||||
#sanity checks
|
||||
# sanity checks
|
||||
mean(df[,n])
|
||||
#[1] -1.173316
|
||||
|
||||
tapply(df[,n], df[,group], mean)
|
||||
#Destabilizing Stabilizing
|
||||
#-1.4297257 0.3978723
|
||||
|
||||
#===============================================
|
||||
# WITHIN GROUP rescaling 2: method "ratio"
|
||||
# create column to store the rescaled values
|
||||
# Rescaling separately (Less dangerous)
|
||||
# =====> chosen one:as Nick prefers
|
||||
# =====> chosen one: preserves sign
|
||||
#===============================================
|
||||
#find range of your data
|
||||
my_min = min(df[,n]); my_min #-3.87
|
||||
my_max = max(df[,n]); my_max #1.689
|
||||
# find range of your data
|
||||
my_min = min(df[,n]); my_min
|
||||
my_max = max(df[,n]); my_max
|
||||
|
||||
df$ratioDUET = ifelse(df[,n] < 0
|
||||
, df[,n]/abs(my_min)
|
||||
, df[,n]/my_max
|
||||
) #335, 15
|
||||
#sanity check
|
||||
) # 15 cols
|
||||
# sanity check
|
||||
head(df$ratioDUET)
|
||||
tail(df$ratioDUET)
|
||||
|
||||
min(df$ratioDUET); max(df$ratioDUET)
|
||||
|
||||
#sanity checks
|
||||
# sanity checks
|
||||
tapply(df$ratioDUET, df$DUET_outcome, min)
|
||||
#Destabilizing Stabilizing
|
||||
#-1.00000000 0.01065719
|
||||
|
||||
tapply(df$ratioDUET, df$DUET_outcome, max)
|
||||
#Destabilizing Stabilizing
|
||||
#-0.003875969 1.000000000
|
||||
|
||||
#should be the same as below (267 and 42)
|
||||
# should be the same as below (267 and 42)
|
||||
sum(df$ratioDUET < 0); sum(df$ratioDUET > 0)
|
||||
|
||||
table(df$DUET_outcome)
|
||||
#Destabilizing Stabilizing
|
||||
#288 47
|
||||
|
||||
#===============================================
|
||||
# Hist and density plots to compare the rescaling
|
||||
# methods: Base R
|
||||
#===============================================
|
||||
#uncomment as necessary
|
||||
|
||||
# uncomment as necessary
|
||||
my_title = "DUET_stability"
|
||||
#my_title = colnames(df[n])
|
||||
|
||||
|
|
@ -246,7 +251,25 @@ mtext(text = my_title
|
|||
, line = 0
|
||||
, outer = TRUE)
|
||||
|
||||
# reorder by column name
|
||||
#data <- data[c("A", "B", "C")]
|
||||
colnames(df)
|
||||
df2 = df[c("X", "Mutationinformation", "WildPos", "Position"
|
||||
, "Wild_type", "Mutant_type"
|
||||
, "DUETStability_Kcalpermol", "DUET_outcome"
|
||||
, "Dis_lig_Ang", "PredAffLog", "Lig_outcome"
|
||||
, "ratioDUET", "ratioPredAff"
|
||||
, "LigandID","Chain")]
|
||||
|
||||
# sanity check
|
||||
# should be True
|
||||
#compare(df, df2, allowAll = T)
|
||||
compare(df, df2, ignoreColOrder = T)
|
||||
#TRUE
|
||||
#reordered columns
|
||||
|
||||
#===================
|
||||
# write output as csv file
|
||||
#===================
|
||||
write.csv(df, "../Data/mcsm_complex1_normalised.csv", row.names = FALSE) #335, 15
|
||||
#write.csv(df, "../Data/mcsm_complex1_normalised.csv", row.names = FALSE)
|
||||
write.csv(df2, outfile, row.names = FALSE)
|
||||
Loading…
Add table
Add a link
Reference in a new issue