Mercurial > repos > kaymccoy > aggregate_fitnesses
changeset 0:0890f73e463c draft
Uploaded
| author | kaymccoy |
|---|---|
| date | Thu, 11 Aug 2016 18:08:23 -0400 |
| parents | |
| children | a66c287c1864 |
| files | aggregate.py |
| diffstat | 1 files changed, 488 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/aggregate.py Thu Aug 11 18:08:23 2016 -0400 @@ -0,0 +1,488 @@ +# 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 + + + + + + + + + + +##### 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.max_weight = 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 += 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"]) + else: + output.append([gene, average, num, sd, se, "\n"]) + with open(arguments.summary, "wb") as csvfile: + writer = csv.writer(csvfile) + writer.writerows(output) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +# +# ~MMM=:DMMM?, +NMMO=,:~I8MMMMM8+, , ~I8MMMMMN87~?8NNMMN8: +NMND~ +MN= ,$MMMI ?M8, ,OM8, :MN+ =MM? ,MMDNMMD ,+DM8I, ,,:::~~~:::::::::: +# IMMMNMM8I ,I8MM87~::+$8NNMMMMOI+=~~:, ,,:~=?$DNMMMMMMDOZI7ZDMMMD8I , , $M8+?8MM8I , 7MI +MN= ZMN, 8MD MMN8MMM, :$ONM8I+=:, ,,,::::~~~~~=====~: +# , ,DMNN7: , ,OMMN7==~::~=?8NNMMMMMMNNMMMMMMMMMMMN8OO8ODNMMMMMD~ , IMMNMN~ ,OM+ ,NM$ ,NMO, :MM$ , ,:::,,::::,, $MNMMNM, ,,, :?ONMMNN8?~,, , 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MN~ +NM: +# IMM8~ =MD, ?N= :,, ,+77?=+, $8MM7::OMMMMMMZ+ I+, ?NI , :MMM8$ NNI, 7M$ +# ,MMN $MD 7M+ ~ODNZ~, :7MMN? , $MMMN= 7MNMN: +8+ ,D8~ =MMDD8Z= =NMD OM7, +# OMMD~ IMN: =8O:,~+$OO? :IZ+: :ZMMNM8= =DMMI ~8MM8= , ,, :8M?,$MM, ::, DM, =Z~ ,I8DDDN8$DMN~ MN ,=Z8DNDZZ= MD: , +# ,,,,,,,, ?8NNMM8$I????++=+8MMMD$77$ZDMMNMMMDNOZ~ :IZ8DOI~, =MMMNMNM8I:, ,7M8I +MND7 ~MNDDM8~ 7MI MM7 :8MDDM7 8N= ~ID$, ,:Z$?:,,,=ONNMNMD= ?M$ :: +MI ,,,,,,::~~:::,,,,,,, ,+OMO:, , +# ,,,::~===~~:, ,~+I$88DNNNNNNNNNDNDD8O$?~:, ,7DNMNDMMMN~,ZMND$8NNDZ=, $M? ?NNN7 =MO OMD$MM= MMMINMO $M7 DN: 8MMD: ?D~ :O8ZDMMMMD=, ,ZN$ ,INNMD= :NO ,:+8MMMMMMMMDI:,::,, +#=~~~::~~::, ~?78MMMDNMM? :+I$DD87?=, OMNDO$7$OI: 7N+ ?MNMN~ MMMNM$ 7M$ 7M$ +NMMO, ?N~ ~8+ ~7NMMNNOI= ~?ONZ+ ~ZNND,$M8 ~MO $MMMMMMMMMM7:::~~~~~~=+++===~~~:::,, +# ,~ONMMNO~, :?8NNMN8?~ZNMMMMMMMMMNMMNDOI=~NM? =MMM+ NMMMO 8M$ ,MM, $MMMN =D, ,?N~ , ?NMMMNNNDDO8DDD887, ,$ND= :O$NMZ IMO ,IMMMMN$, 7NO, ,,,,,:,,, +# ,~IDMNN8OI, :~+$DMMMMMMMN87+=~~~~?ZDNNMM, ?NM? :NMN :MM7 ~MN ~DMMMN O~ :DD, ,MMMMNNOI:,, , ,=ZODD8D? :MMM: NMZ +8NM8, ,, :~ ,,~:, +# ,INNMMNNO+ :=78NNZIIONMNNDMMN ,7D~ :MN~ MNMM?=MM $? OD, ,IMD ,:8MNMNNNNNNNNMMNM7: :8~ ZND OMN7, :NNMMMMN :8MMMMMNZ=: +# :O88NMMN$=~:, ?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