Mercurial > repos > kaymccoy > aggregate_fitnesses
changeset 19:9928890d0220 draft
Deleted selected files
| author | kaymccoy |
|---|---|
| date | Fri, 12 Aug 2016 23:03:13 -0400 |
| parents | 13f7c0c33dcb |
| children | 9a358069d5bb |
| files | aggregate.py |
| diffstat | 1 files changed, 0 insertions(+), 488 deletions(-) [+] |
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line diff
--- a/aggregate.py Fri Aug 12 23:01:17 2016 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,488 +0,0 @@ -# A translation of aggregate.pl into python! For analysis of Tn-Seq. -# This script requires BioPython just like calc_fitness.py, so you need it installed along with its dependencies if you want to run these scripts on your own. -# How to install BioPython and a list of its dependencies can be found here: http://biopython.org/DIST/docs/install/Installation.html -# K. McCoy - - - - - - - - - -##### ARGUMENTS ##### - -def print_usage(): - print "Aggregate.py's usage is as follows:" + "\n\n" - print "\033[1m" + "Required" + "\033[0m" + "\n" - print "-o" + "\t\t" + "Output file for aggregated data." + "\n" - print "\n" - print "\033[1m" + "Optional" + "\033[0m" + "\n" - print "-c" + "\t\t" + "Check for missing genes in the data set - provide a reference genome in genbank format. Missing genes will be sent to stdout." + "\n" - print "-m" + "\t\t" + "Place a mark in an extra column for this set of genes. Provide a file with a list of genes seperated by newlines." + "\n" - print "-x" + "\t\t" + "Cutoff: Don't include fitness scores with average counts (c1+c2)/2 < x (default: 0)" + "\n" - print "-b" + "\t\t" + "Blanks: Exclude -b % of blank fitness scores (scores where c2 = 0) (default: 0 = 0%)" + "\n" - print "-f" + "\t\t" + "An in-between file carrying information on the blank count found from calc_fitness or consol_fitness; one of two ways to pass a blank count to this script" + "\n" - print "-w" + "\t\t" + "Use weighted algorithm to calculate averages, variance, sd, se" + "\n" - print "-l" + "\t\t" + "Weight ceiling: maximum value to use as a weight (default: 999,999)" + "\n" - print "\n" - print "All remainder arguements will be treated as fitness files (those files created by calc_fitness.py)" + "\n" - print "\n" - -import argparse -parser = argparse.ArgumentParser() -parser.add_argument("-o", action="store", dest="summary") -parser.add_argument("-c", action="store", dest="find_missing") -parser.add_argument("-m", action="store", dest="marked") -parser.add_argument("-x", action="store", dest="cutoff") -parser.add_argument("-b", action="store", dest="blank_pc") -parser.add_argument("-f", action="store", dest="blank_file") -parser.add_argument("-w", action="store", dest="weighted") -parser.add_argument("-l", action="store", dest="weight_ceiling") -parser.add_argument("fitnessfiles", nargs=argparse.REMAINDER) - -arguments = parser.parse_args() - -if not arguments.summary: - print "\n" + "You are missing a value for the -o flag. " - print_usage() - quit() - -if not arguments.fitnessfiles: - print "\n" + "You are missing fitness file(s); these should be entered immediately after all the flags. " - print_usage() - quit() - -# 999,999 is a trivial placeholder number - -if (not arguments.weight_ceiling): - arguments.weight_ceiling = 999999 - -# Cutoff exists to discard positions with a low number of counted transcripts, because their fitness may not be as accurate - for the same reasoning that studies with low sample sizes can be innacurate. - -if (not arguments.cutoff): - arguments.cutoff = 0 - -# Gets information from the txt output file of calc_fit / consol, if inputted - -if arguments.blank_file: - with open(arguments.blank_file) as file: - blank_pc = file.read().splitlines() - arguments.blank_pc = float(blank_pc[0].split()[1]) - -if (not arguments.blank_pc): - arguments.blank_pc = 0 - - - - - -##### SUBROUTINES ##### - -# A subroutine that calculates the average, variance, standard deviation (sd), and standard error (se) of a group of scores; for use when aggregating scores by gene later on - -import math -def average(scores): - sum = 0 - num = 0 - for i in scores: - sum += float(i) - num += 1 - average = sum/num - xminusxbars = 0 - for i in scores: - xminusxbars += (i - average)**2 - variance = xminusxbars/(num-1) - sd = math.sqrt(variance) - se = sd / math.sqrt(num) - return (average, variance, sd, se) - -# A subroutine that calculates the weighted average, variance, standard deviation (sd), and standard error (se) of a group of scores; the weights come from the number of reads each insertion location has -# For use when aggregating scores by gene later on, if the weighted argument is called - -def weighted_average(scores,weights): - sum = 0 - weighted_average = 0 - weighted_variance = 0 - top = 0 - bottom = 0 - i = 0 - while i < len(weights): - if not scores[i]: - scores[i] = 0.0 - top += float(weights[i])*float(scores[i]) - bottom += float(weights[i]) - i += 1 - if bottom == 0: - return 0 - weighted_average = top/bottom - top = 0 - bottom = 0 - i = 0 - while i < len(weights): - top += float(weights[i]) * (float(scores[i]) - weighted_average)**2 - bottom += float(weights[i]) - i += 1 - weighted_variance = top/bottom - weighted_stdev = math.sqrt(weighted_variance) - weighted_stder = weighted_stdev/math.sqrt(len(scores)) - return (weighted_average, weighted_variance, weighted_stdev, weighted_stder) - - - - - - - - - - -##### AGGREGATION / CALCULATIONS ##### - -#Reads the genes which should be marked in the final aggregate file into an array - -import os.path -if arguments.marked: - with open(arguments.marked) as file: - marked_set = file.read().splitlines() - -#Creates a dictionary of dictionaries to contain a summary of all genes and their fitness values -#The fitness values and weights match up, so that the weight of gene_summary[locus]["w"][2] would be gene_summary[locus]["s"][2] - -import csv -gene_summary = {} -for eachfile in arguments.fitnessfiles: - with open(eachfile) as csvfile: - lines = csv.reader(csvfile) - for line in lines: - locus = line[9] - w = line[12] - if w == 'nW': - continue - if not w: - w == 0 - c1 = float(line[2]) - c2 = float(line[3]) - avg = (c1+c2)/2 - if avg < float(arguments.cutoff): - continue - if avg > float(arguments.weight_ceiling): - avg = arguments.weight_ceiling - if locus not in gene_summary: - gene_summary[locus] = {"w" : [], "s": []} - gene_summary[locus]["w"].append(w) - gene_summary[locus]["s"].append(avg) - -#If finding any missing gene loci is requested in the arguments, starts out by loading all the known features from a genbank file - -from Bio import SeqIO -if (arguments.find_missing): - output = [["locus","mean","var","sd","se","gene","Total","Blank","Not Blank","Blank Removed","M\n"]] - handle = open(arguments.find_missing, "rU") - for record in SeqIO.parse(handle, "genbank"): - refname = record.id - features = record.features - handle.close() - -#Goes through the features to find which are genes - - for feature in features: - gene = "" - if feature.type == "gene": - locus = "".join(feature.qualifiers["locus_tag"]) - if "gene" in feature.qualifiers: - gene = "".join(feature.qualifiers["gene"]) - else: - continue - -#Goes through the fitness scores of insertions within each gene, and removes whatever % of blank fitness scores were requested along with their corresponding weights - - sum = 0 - num = 0 - avgsum = 0 - blank_ws = 0 - i = 0 - if locus in gene_summary.keys(): - for w in gene_summary[locus]["w"]: - if float(w) == 0: - blank_ws += 1 - else: - sum += float(w) - num += 1 - count = num + blank_ws - removed = 0 - to_remove = int(float(arguments.blank_pc)*count) - if blank_ws > 0: - i = 0 - while i < len(gene_summary[locus]["w"]): - w = gene_summary[locus]["w"][i] - if removed == to_remove: - break - if float(w) == 0: - del gene_summary[locus]["w"][i] - del gene_summary[locus]["s"][i] - removed += 1 - i -= 1 - i += 1 - -#If all the fitness values within a gene are empty, sets mean/var to 0.10 and Xs out sd/se; marks the gene if that's requested - - if num == 0: - if (arguments.marked and locus in marked_set): - output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "M", "\n"]) - else: - output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "\n"]) - -#Otherwise calls average() or weighted_average() to find the aggregate w / count / standard deviation / standard error of the insertions within each gene; marks the gene if that's requested - - else: - if not arguments.weighted: - (average, variance, stdev, stderr) = average(gene_summary[locus]["w"]) - else: - (average, variance, stdev, stderr) = weighted_average(gene_summary[locus]["w"],gene_summary[locus]["s"]) - if (arguments.marked and locus in marked_set): - output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "M", "\n"]) - else: - output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "\n"]) - -#If a gene doesn't have any insertions, sets mean/var to 0.10 and Xs out sd/se, plus leaves count through removed blank because there were no reads. - - else: - if (arguments.marked and locus in marked_set): - output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "M", "\n"]) - else: - output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "\n"]) - -#Writes the aggregated fitness file - - with open(arguments.summary, "wb") as csvfile: - writer = csv.writer(csvfile) - writer.writerows(output) - -#If finding missing genes is not requested, just finds the aggregate w / count / standard deviation / standard error of the insertions within each gene, and writes them to a file, plus marks the genes requested -#This is never called through Galaxy since finding missing genes is just better than not finding them. - -else: - output = [["Locus","W","Count","SD","SE","M\n"]] - for gene in gene_summary.keys(): - sum = 0 - num = 0 - average = 0 - if "w" not in gene_summary[gene]: - continue - for i in gene_summary[gene]["w"]: - sum += i - num += 1 - average = sum/num - xminusxbars = 0 - for i in w: - xminusxbars += (i-average)**2 - if num > 1: - sd = math.sqrt(xminusxbars/(num-1)) - se = sd / math.sqrt(num) - if (arguments.marked and locus in marked_set): - output.append([gene, average, num, sd, se, "M", "\n"]) - 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OD, ,IMD ,:8MNMNNNNNNNNMMNM7: :8~ ZND OMN7, :NNMMMMN :8MMMMMNZ=: -# :O88NMMN$=~:, ?MMN88NMMMD =MN? =, :NM +7$NO, =N8 , OMI, ,, 7M: ZDDND? MMO ,, +MM8?OMN: ,:: -# :$NMMMNMM8I: ,8MD, +MMN= ,=?77=:ZMMM7 ?MN ~$+ 7M7 ,DZ :M~ IMM8: ,8MN, , ,,,, :MMD,=DMM+,?O8= , , -# ,=I77$ONNNZ?+ONMZ =$D ,+=::+$8NNMNDNMD7?, ?MN :~~: ~D7 ,NI ,M7 ~?ZNZ?, 8MM~ =ZMMN~$MMMMMMMNMMD$ONMMMMD: -# ~DMM= ~I7=, =DMMMD7MMM, :+?=?O$: ~N: ~MN88D$: NMM=, ~MMMMMMMMMMMMOZMMMMMM7::8MMI -# , :DNNI :~IDMMND :::=?II?==~::ZN7=+I$ZZZ8DZ+~~: IMMM~ ~MMMNMMMMMI~:7NMMMMD7,: +NM8, -# :NMM7, ~MM+ ,,,:~==~~~: , OMMN: ?NNMMMMM8, ~NMNO, =MMMN?,, -# ,,,,,,,, ,$8MMO= ~M8 +MNO: ,MM~ , :: ,~, ,,:::,,,, -# ,::,,,, ~ONND+ OD+ ?NMD, ?? ,::::::,, -# :=+??=:,,,,, ~?$D87I: ~Z? =$DM$: ,::,,,,,,,, -# ,:~==~, :+=, $NNNNZ~ ,,:~~: , :DMMMI -# ,~~~~~:,,, ,,, ,~IMMMN8O+, ,:~?7$Z7~, :ZDMNNDNM8ZI -# ,,~~:,, , :$DNNMMN8?, , ,~7ZOO?: ,:$NMMMM? :7NMMD?, -# ,:~~:,,, $NMMMMMNMNO?~ ~?ODDD$=, :?8MMMMMD?~7NMMMD$~ :ONMMN? -# ,,,,,,,,, ?$DMMMMMMMMMMD$?=~, ,~7ZZODN87????I$ODNMDOZ$7I: ~$ZDMD7==~ =$ZDND$++~, -# ~?++=~~, ~+?II7ZNMMMMMM8$$$?~, ,~?II7Z8DDOZ$77II+: ~?IZDM8O$I~, :??$8MMNZZ7+, -# , , ,+D7 :=IZ8NMMMNNMNNO$= ,:?ZDNNMMMNMND8Z7=, =ZNMMMMMD?,, :ONMNMMMN?, -# ,MMMM7 ,,::~IONNMMNNDDD8OZI=, ,::::=+I$ONMNNNNDDDNNMMMMMMMMMD87: ,:~=ZNNNDOI, ~7$ZO8DDNNNNDD8O+:, -#~, +MMMMDI?~ ,~+Z8DNDNNMMMMDD$+:,,, , ,~?7O8DNNNNNNMMMMMNOOZI??++?IZ88$: ,,~ZDMMMMMMMMMMMMMMMMMMNNNNNMMMNND8$=,, -# ,~: NMMMMOZMM8: ,7DZ~ ,,~IONMMMMMMMMMNDZ+~: , ,=I8DNMMMMMNMNMMMMMDNMMMMNMMMNNNDDDDD8O8Z$7II+++IZDDMMNMN$,, -# :~: MMMMMNMMM+ :, +?DOI~ ,:~?7$$ZODDNMNN8Z7??+=~:, ,~=+?7I?=:,,,,:=?I7$$$$$$$ZZZOO8DDNNMMNNNNNMMMMNMZ+ -# ~=, MMMMMMMM8 ,NNNO , ,~?ZDNMNNNNNNND8O7?:,,, ,~=7DMMMMMMMMMZ~ -# ,:+~ ,MMMMMMMMO ~N8: ,,:~, ,:~:,,, ,:~, ,:~~=+++?7$O8DNNNNNMMN8$=, ~+$ZO8DNNMMNNNND8OOOZ$+:, :=+I8MMMMDOI -# ,=+ MMMMMMMMMDMZ, ~?: ,,,,, ,:~~~, ,NN?$NMMMMMMMMMMMN87~:,,,=+=:,,,,,,:?NMMNMMMMMMNNDNDI8MMM+ , -# +=, $MMMN?$NMN, ,+?+:, ,,:~~:, :=~, 8M= , :7ONNNO?~, , :$NNMMMMMMMMMMMMMN, -# ,+? ,NNO,, , :ODNNNZ: :?777I= IMO ,,=$Z7+~ ,?NNMMNZ: :7NMMMMMNMMMM8 , ,IDND~ -# ,=~, 7I :+I+~::,, ~?I~ , ,~=~==: ZMZ ?8I: :I$DND$?~ :?$8MMN$?: ?ZDMMMMMMMM7 +$NMMMMMI -# ~~ , ,=+=: ,== ::, , ZMM: ?NN8: ,7NMMNI, ,$NMMMO: =NMMMNMM+ I8NMMMM, -# ~= ,~:,,~~, ,:, ,,,, , ,, +MN8 ,~OMM8Z, :+ZMNDOI =IMMN8= ,=NNMO ?NMMMD -# ::, ,,,:: ,:, ,, =MMO~ , ?NMMMI, ,INMMMDI, ~DMMN+, NMO ?DNO~ -# ,~, ,,,::, ,, , ,, ,INMND+: :MMM+ ~ZDDMMMD?~:$MMMMMMMMMMM? -# ,, :~~:, ,:, ,, :ZMMMMNNMMMN, ,=ONMMMMNM$,,,,,, ,, -#Z, :, ,, ,~=~, ,, ,=?7$I= , :~~~, ,,,, -#MD= :: ,,,, , ,, ,,,, -#MNMI :: ,,, ,,,, -#7MMM? , ,~: :~:, ,:, -# OMMMO: ~NMD, :=: :+=: ,,, -# ~MMMN8: NMMM~ +?: ,:=++~ ,:,, -# $MMMMMNI,?MMMMZ ,DM7 ,=I= ~+~~:, ,:,,, -# +MMO~OMMMMMMMMD MMM7 , , =$~ ?OZ+ ,:, -# ~NMN: :+DNN7MMMNMMMDODMMN+ :+?, +77+~: :::, -# IMM+ ,NMMMMMMMMN?NM7 ~7+, , :+$I, , +8? :+~ -# =NMD, NMMMMMM8~ ?NN, =ND? ,=??: ,8NMMMM= :++ -# +DO, ~ZZ$ODI :8M~ :=I8NMNMMD+, ,:~~=: , +8MO$DMD+ ~===~: :~~: -# :$~ 7NMD$, ZMM8I? ,~=: , ZNN7,:MM8~,OMNMD8DMM= ++: -# =ZNOI: ?DND= ,?I~ ,,,: ,$ND+ ?NMNNNNN7+, 7MN, ,=+~ -# :ZI: :, ,=7: ::,, $NN= 7NMMMM7: :DMMMN8NMMDDMN7 ,::, -# ~?= ,,, ,ZM= DMMD8 ?MMMMNN7, ,I+ :~~, -# =+, ,,, ,, =Z8: +$~ -# :~ ,,~~, -# :, ::, -# ::, ,:::, -# ,, ,~ -# ,, ,,, -# -# \ No newline at end of file