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view Tryp_T.py @ 29:b3d2d0a771e1 draft
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author | johnheap |
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date | Fri, 07 Jun 2019 11:06:29 -0400 |
parents | 5e346d75ccf3 |
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""" * Copyright 2018 University of Liverpool * Author: John Heap, Computational Biology Facility, UoL * Based on original scripts of Sara Silva Pereira, Institute of Infection and Global Health, UoL * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * """ import subprocess import pandas as pd import re import os import sys import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt pList = ['P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'P9', 'P10', 'P11', 'P12', 'P13', 'P14', 'P15'] quietString = "" #"">> Vap_log.txt 2>&1" def transcriptMapping(inputname, strain, forwardFN,reverseFN): #where is our Reference data - dir_path = os.path.dirname(os.path.realpath(__file__)) refName = dir_path+"/data/Reference/Tc148" #default if strain == "Tc148": refName = dir_path+"/data/Reference/Tc148" if strain == "IL3000": refName = dir_path+"/data/Reference/IL3000" #argString = "bowtie2 -x Refe4rence/IL3000 -1 data/"+forwardFN+" -2 data/"+reverseFN+" -S "+inputname+".sam" #>log.txt #argString = "bowtie2 -x Reference/Tc148 -1 data/"+forwardFN+" -2 data/"+reverseFN+" -S "+inputname+".sam" #>log.txt argString = "bowtie2 -x "+refName+" -1 "+forwardFN+" -2 "+reverseFN+" -S "+inputname+".sam"+quietString #>log.txt #print(argString) returncode = subprocess.call(argString, shell=True) def processSamFiles(inputname): #debug use a mapping sam file we have already found #dir_path = os.path.dirname(os.path.realpath(__file__)) #bugName = dir_path+"/data/T_Test" #defasult cur_path = os.getcwd() samName = cur_path+"/"+inputname #argString = "samtools view -bS "+bugName+" > "+inputname+".bam" argString = "samtools view -bS "+inputname+".sam > "+samName+".bam"+quietString #print(argString) returncode = subprocess.call(argString, shell=True) #argString = "samtools sort "+bugName+" -o "+inputname+".sorted" argString = "samtools sort "+samName+".bam -o "+samName+".sorted"+quietString #print("argstring = "+argString) returncode = subprocess.call(argString, shell=True) #argString = "samtools index "+bugName+".sorted "+inputname+".sorted.bai" argString = "samtools index "+samName+".sorted "+samName+".sorted.bai"+quietString #print("argstring = " + argString) returncode = subprocess.call(argString, shell=True) def transcriptAbundance(inputname, strain): dir_path = os.path.dirname(os.path.realpath(__file__)) refName = dir_path + "/data/Reference/ORFAnnotation.gtf" # defasult if strain == "Tc148": refName = dir_path + "/data/Reference/ORFAnnotation.gtf" if strain == "IL3000": refName = dir_path + "/data/Reference/IL3000.gtf" #argString = "cufflinks -G Reference/IL3000.gtf -o "+inputname+".cuff -u -p 8 "+inputname+".sorted" #argString = "cufflinks -G Reference/ORFAnnotation.gtf -o "+inputname+".cuff -u -p 8 "+inputname+".sorted" argString = "cufflinks -q -G "+refName+" -o "+inputname+".cuff -u -p 8 "+inputname+".sorted"+quietString returncode = subprocess.call(argString, shell = True) def convertToFasta(inputName, strain): #equivalent to Sara's awk scripte dir_path = os.path.dirname(os.path.realpath(__file__)) refName = dir_path + "/data/Reference/ORFAnnotation.gtf" # default if strain == "Tc148": refName = dir_path + "/data/Reference/148_prot.fasta" if strain == "IL3000": refName = dir_path + "/data/Reference/IL3000_prot.fasta" cuff_df = pd.read_csv(inputName+".cuff/genes.fpkm_tracking", sep='\t') cuff_df = cuff_df[(cuff_df['FPKM'] > 0)] cuff_df.to_csv("cuffTest.csv") gene_id_List = cuff_df['gene_id'].tolist() #print(gene_id_List) #print ("Found from 8880="+str(found)) # need to load in IL3000_prot.fasta # for each line with >TcIL3000_1_1940 # search within cuff_df[gene_id] for match # add it to the outfile. (need to save it as used by hmmer later number = 0 all = 0 with open(inputName+"_6frame.fas", 'w') as outfile: ref = open(refName,'r') #ref = open(r"Reference/IL3000_prot.fasta",'r') n = 0 line = ref.readline() while line: if line[0] == '>': all = all+1 ln = line[1:] #remove > ln = ln.rstrip() #remove /n /r etc #print (ln) if ln in gene_id_List: number = number+1 outfile.write(line) line = ref.readline() if line: while line[0] != '>': outfile.write(line) line=ref.readline() if not line: break; else: line = ref.readline() else: line =ref.readline() ref.close() print(str(len(gene_id_List))+":"+str(number)+" from "+str(all)) return cuff_df def HMMerMotifSearch(name, strain, cuff_df): motifs = ['1', '2a', '2b', '3', '4a', '4b', '4c', '5', '6', '7', '8a', '8b', '9a', '9b', '9c', '10a', '10b', '11a', '11b', '12', '13a', '13b', '13c', '13d', '14', '15a', '15b', '15c'] dir_path = os.path.dirname(os.path.realpath(__file__)) phylopath = dir_path + "/data/Motifs/Phylotype" lineCounts = [] compoundList = [] for m in motifs: argString = "hmmsearch "+phylopath + m + ".hmm " + name + "_6frame.fas > Phy" + m + ".out" print(argString) subprocess.call(argString, shell=True) hmmResult = open("Phy" + m + ".out", 'r') regex = r"Tc148[0-9]{1,8}" if strain == "Tc148": regex = r"Tc148[0-9]{1,8}" if strain == "IL3000": regex = r"TcIL3000_[0-9]{1,4}_[0-9]{1,5}" n = 0 outList = [] for line in hmmResult: m = re.search(regex, line) if m: outList.append(""+m.group()) n += 1 if re.search(r"inclusion", line): print("inclusion threshold reached") break compoundList.append(outList) lineCounts.append(n) hmmResult.close() #print(lineCounts) #print(cuff_df) concatGroups = [1, 2, 1, 3, 1, 1, 1, 2, 3, 2, 2, 1, 4, 1, 3] countList = [] weightList = [] countIndex = 0 totalCount = 0 totalWeigth = 0 for c in concatGroups: a = [] weight = [] for n in range(0, c): a = a + compoundList.pop(0) t = set(a) countList.append(len(t)) wa = 0 for w in t: wt = cuff_df.loc[cuff_df['gene_id'] == w, 'FPKM'].iloc[0] #print(w) #print(wt) wa = wa+wt weightList.append(wa) totalWeigth+=wa totalCount += len(t) countList.append(totalCount) weightList.append(totalWeigth) #print(countList) #print("--------") #print(weightList) #print("--------") return countList,weightList def relativeFrequencyTable(countList, name, htmlresource): relFreqList = [] c = float(countList[15]) for i in range(0, 15): relFreqList.append(countList[i] / c) data = {'Phylotype': pList, 'Relative Frequency': relFreqList} relFreq_df = pd.DataFrame(data) j_fname = htmlresource+ "/" + name + "_t_relative_frequency.csv" relFreq_df.to_csv(j_fname) return relFreqList # 0-14 = p1-p15 counts [15] = total counts def weightedFrequencyTable(countList, name, htmlresource): relFreqList = [] c = float(countList[15]) for i in range(0, 15): relFreqList.append(countList[i] / c) data = {'Phylotype': pList, 'Weighted Frequency': relFreqList} relFreq_df = pd.DataFrame(data) j_fname = htmlresource+ "/" + name + "_t_weighted_frequency.csv" relFreq_df.to_csv(j_fname) return relFreqList # 0-14 = p1-p15 counts [15] = total counts def createStackedBar(name,freqList,strain,pdf,html_resource): palette = ["#0000ff", "#6495ed", "#00ffff", "#caff70", "#228b22", "#528b8b", "#00ff00", "#a52a2a", "#ff0000", "#ffff00", "#ffa500", "#ff1493", "#9400d3", "#bebebe", "#000000", "#ff00ff"] VAP_148 = [0.072, 0.032, 0.032, 0.004, 0.007, 0.005, 0.202, 0.004, 0.006, 0.014, 0.130, 0.133, 0.054, 0.039, 0.265] VAP_IL3000 = [0.073, 0.040, 0.049, 0.018, 0.060, 0.055, 0.054, 0.025, 0.012, 0.060, 0.142, 0.100, 0.061, 0.078, 0.172] cmap = plt.cm.get_cmap('tab20') palette = [cmap(i) for i in range(cmap.N)] if strain == "Tc148": VAPtable = VAP_148 VAPname='Tc148\nGenome VAP' if strain == "IL3000": VAPtable = VAP_IL3000 VAPname= 'IL3000\nGenome VAP' width = 0.35 # the width of the bars: can also be len(x) sequence plots = [] fpos = 0 vpos = 0 for p in range(0, 15): tp = plt.bar(0, freqList[p], width, color= palette[p], bottom = fpos) fpos +=freqList[p] tp = plt.bar(1, VAPtable[p], width, color= palette[p], bottom = vpos) vpos +=VAPtable[p] plots.append(tp) plt.xticks([0,1],[name,VAPname]) plt.legend(plots[::-1],['p15','p14','p13','p12','p11','p10','p9','p8','p7','p6','p5','p4','p3','p2','p1']) title = "Figure Legend: The transcriptomic Variant Antigen Profile of $\itTrypanosoma$ $\itcongolense$ estimated as phylotype " \ "proportion adjusted for transcript abundance and the reference genomic Variant Antigen Profile. " \ "\nData was produced with the 'Variant Antigen Profiler' (Silva Pereira et al., 2019)." #plt.title(title, wrap="True") #plt.text(-0.2, -0.05, title, va="top", transform=ax.transAxes, wrap="True") plt.text(-0.3, -0.15, title, va="top", wrap="True") plt.tight_layout(pad=1.5) plt.subplots_adjust(bottom = 0.3,top=0.99,left=0.125,right=0.9,hspace=0.2,wspace=0.2) plt.savefig(html_resource + "/stackedbar.png") if pdf == 'PDF_Yes': plt.savefig(html_resource + "/stackedbar.pdf") #plt.show() def createHTML(name,htmlfn,htmlresource,freqList,weightList): #assumes imgs are heatmap.png, dheatmap.png, vapPCA.png and already in htmlresource htmlString = r"<html><title>T.congolense VAP</title><body><div style='text-align:center'><h2><i>Trypanosoma congolense</i> Variant Antigen Profile</h2><h3>" htmlString += name htmlString += r"<br>Transcriptomic Analysis</h3></p>" htmlString += "<p style = 'margin-left:20%; margin-right:20%'>Table Legend: Variant Antigen Profiles of a transcriptome of <i>Trypanosoma congolense</i> estimated as phylotype proportion. " \ "Weighted frequency refers to the phylotype proportion based transcript abundance. " \ "Data was produced with the 'Variant Antigen Profiler' (Silva Pereira et al., 2019).</p> " htmlString += r"<style> table, th, tr, td {border: 1px solid black; border-collapse: collapse;}</style>" htmlString += r"<table style='width:50%;margin-left:25%;text-align:center'><tr><th>Phylotype</th><th>Relative Frequency</th><th>Weighted Frequency</th></tr>" tabString = "" # flush out table with correct values for i in range(0, 15): f = format(freqList[i], '.4f') w = format(weightList[i], '.4f') tabString += "<tr><td>phy" + str(i + 1) + "</td><td>" + f + "</td><td>" + w + "</td></tr>" htmlString += tabString + "</table><br><br><br><br><br>" htmlString += r"<p> <h3>Stacked Bar chart of Phylotype Frequency</h3> The 'weighted' relative frequency of each phylotype alongside the VAP of selected strain.</p>" imgString = r"<img src = 'stackedbar.png' alt='Stacked bar chart of phylotype variation' style='max-width:100%'><br><br>" htmlString += imgString # htmlString += r"<p><h3>The Deviation Heat Map and Dendogram</h3>The phylotype variation expressed as the deviation from your sample mean compared to the model dataset</p>" # imgString = r"<img src = 'dheatmap.png' alt='Deviation Heatmap' style='max-width:100%'><br><br>" # htmlString += imgString # htmlString += r"<p><h3>The Variation PCA plot</h3>PCA analysis corresponding to absolute variation. Colour coded according to location</p>" # imgString = r"<img src = 'vapPCA.png' alt='PCA Analysis' style='max-width:100%'><br><br>" # htmlString += imgString + r"</div></body></html>" with open(htmlfn, "w") as htmlfile: htmlfile.write(htmlString) #argdict = {'name':2, 'pdfexport': 3, 'strain': 4, 'forward': 5, 'reverse': 6, 'html_file': 7, 'html_resource': 8} def transcriptomicProcess(args,dict): transcriptMapping(args[dict['name']], args[dict['strain']], args[dict['forward']], args[dict['reverse']]) #uses bowtie processSamFiles(args[dict['name']]) #uses samtools transcriptAbundance(args[dict['name']],args[dict['strain']]) #uses cufflinks -> ?.cuff/*.* cuff_df = convertToFasta(args[dict['name']],args[dict['strain']]) countList, weightList = HMMerMotifSearch(args[dict['name']],args[dict['strain']], cuff_df) relFreqList = relativeFrequencyTable(countList,args[dict['name']],args[dict['html_resource']]) relWeightList = weightedFrequencyTable(weightList,args[dict['name']],args[dict['html_resource']]) createStackedBar(args[dict['name']],relWeightList, args[dict['strain']],args[dict['pdfexport']],args[dict['html_resource']]) createHTML(args[dict['name']],args[dict['html_file']],args[dict['html_resource']], relFreqList, relWeightList) if __name__ == "__main__": #print("Commencing Transcript Mapping") #transcriptMapping("T_Test", "Transcripts.1","Transcripts.2") #print("Processimg Sam Files") #processSamFiles("T_Test") #print("Assessing Transcript Abundance") #transcriptAbundance("T_Test") #print ("Converting to Fasta Subset") #cuff_df = convertToFasta("T_Test") #print("Commencing HMMer search") #countList, weightList = HMMerMotifSearch("T_Test",cuff_df) #relativeFrequencyTable(countList,'T_Test') #weightedFrequencyTable(weightList,'T_Test') relFreqList = [0.111842105,0.059210526,0.026315789,0.013157895, 0.006578947,0.013157895,0.032894737,0.019736842, 0.039473684,0.046052632,0.217105263,0.065789474, 0.151315789,0.059210526,0.138157895] relWeightList = [0.07532571,0.05900545,0.009601452,0.042357532,0.01236219,0.001675663,0.04109726, 0.097464248,0.057491666,0.05826875,0.279457473,0.070004772,0.065329007,0.085361298,0.045197529] createStackedBar('T_Test',relWeightList, 'Tc148','PDF_Yes','results') createHTML("t_test","results/t_test.html","results",relFreqList,relWeightList)