annotate sequence_overview.r @ 0:c33d93683a09 draft

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1 library(reshape2)
2 
3 args <- commandArgs(trailingOnly = TRUE)
4 
5 before.unique.file = args[1]
6 merged.file = args[2]
7 outputdir = args[3]
8 gene.classes = unlist(strsplit(args[4], ","))
9 hotspot.analysis.sum.file = args[5]
10 NToverview.file = paste(outputdir, "ntoverview.txt", sep="/")
11 NTsum.file = paste(outputdir, "ntsum.txt", sep="/")
12 main.html = "index.html"
13 
14 setwd(outputdir)
15 
16 before.unique = read.table(before.unique.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="")
17 merged = read.table(merged.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="")
18 hotspot.analysis.sum = read.table(hotspot.analysis.sum.file, header=F, sep=",", fill=T, stringsAsFactors=F, quote="")
19 
20 #before.unique = before.unique[!grepl("unmatched", before.unique$best_match),]
21 
22 before.unique$seq_conc = paste(before.unique$CDR1.IMGT.seq, before.unique$FR2.IMGT.seq, before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq)
23 
24 IDs = before.unique[,c("Sequence.ID", "seq_conc", "best_match", "Functionality")]
25 IDs$best_match = as.character(IDs$best_match)
26 
27 #dat = data.frame(data.table(dat)[, list(freq=.N), by=c("best_match", "seq_conc")])
28 
29 dat = data.frame(table(before.unique$seq_conc))
30 #dat = data.frame(table(merged$seq_conc, merged$Functionality))
31 
32 #dat = dat[dat$Freq > 1,]
33 
34 #names(dat) = c("seq_conc", "Functionality", "Freq")
35 names(dat) = c("seq_conc", "Freq")
36 
37 dat$seq_conc = factor(dat$seq_conc)
38 
39 dat = dat[order(as.character(dat$seq_conc)),]
40 
41 #writing html from R...
42 get.bg.color = function(val){
43 	if(val %in% c("TRUE", "FALSE", "T", "F")){ #if its a logical value, give the background a green/red color
44 		return(ifelse(val,"#eafaf1","#f9ebea"))
45 	} else if (!is.na(as.numeric(val))) { #if its a numerical value, give it a grey tint if its >0
46 		return(ifelse(val > 0,"#eaecee","white"))
47 	} else {
48 		return("white")
49 	}
50 }
51 td = function(val) {
52   return(paste("<td bgcolor='", get.bg.color(val), "'>", val, "</td>", sep=""))
53 }
54 tr = function(val) { 
55 	return(paste(c("<tr>", sapply(val, td), "</tr>"), collapse="")) 
56 }
57 
58 make.link = function(id, clss, val) { 
59 	paste("<a href='", clss, "_", id, ".html'>", val, "</a>", sep="") 
60 }
61 tbl = function(df) {
62 	res = "<table border='1'>"
63 	for(i in 1:nrow(df)){ 
64 		res = paste(res, tr(df[i,]), sep="")
65 	}
66 	res = paste(res, "</table>")
67 }
68 
69 cat("<table border='1' class='pure-table pure-table-striped'>", file=main.html, append=F)
70 #cat("<caption>CDR1+FR2+CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T)
71 cat("<tr>", file=main.html, append=T)
72 cat("<th>Sequence</th><th>Functionality</th><th>ca1</th><th>ca2</th><th>cg1</th><th>cg2</th><th>cg3</th><th>cg4</th><th>cm</th><th>un</th>", file=main.html, append=T)
73 cat("<th>total CA</th><th>total CG</th><th>number of subclasses</th><th>present in both Ca and Cg</th><th>Ca1+Ca2</th>", file=main.html, append=T)
74 cat("<th>Cg1+Cg2</th><th>Cg1+Cg3</th><th>Cg1+Cg4</th><th>Cg2+Cg3</th><th>Cg2+Cg4</th><th>Cg3+Cg4</th>", file=main.html, append=T)
75 cat("<th>Cg1+Cg2+Cg3</th><th>Cg2+Cg3+Cg4</th><th>Cg1+Cg2+Cg4</th><th>Cg1+Cg3+Cg4</th><th>Cg1+Cg2+Cg3+Cg4</th>", file=main.html, append=T)
76 cat("</tr>", file=main.html, append=T)
77 
78 
79 
80 single.sequences=0 #sequence only found once, skipped
81 in.multiple=0 #same sequence across multiple subclasses
82 multiple.in.one=0 #same sequence multiple times in one subclass
83 unmatched=0 #all of the sequences are unmatched
84 some.unmatched=0 #one or more sequences in a clone are unmatched
85 matched=0 #should be the same als matched sequences
86 
87 sequence.id.page="by_id.html"
88 
89 for(i in 1:nrow(dat)){
90 	
91 	ca1 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGA1", IDs$best_match),]
92 	ca2 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGA2", IDs$best_match),]
93 	
94 	cg1 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG1", IDs$best_match),]
95 	cg2 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG2", IDs$best_match),]
96 	cg3 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG3", IDs$best_match),]
97 	cg4 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG4", IDs$best_match),]
98 	
99 	cm = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGM", IDs$best_match),]
00 	
01 	un = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^unmatched", IDs$best_match),]
02 	allc = rbind(ca1, ca2, cg1, cg2, cg3, cg4, cm, un)
03 	
04 	ca1.n = nrow(ca1)
05 	ca2.n = nrow(ca2)
06 	
07 	cg1.n = nrow(cg1)
08 	cg2.n = nrow(cg2)
09 	cg3.n = nrow(cg3)
10 	cg4.n = nrow(cg4)
11 	
12 	cm.n = nrow(cm)
13 	
14 	un.n = nrow(un)
15 	
16 	classes = c(ca1.n, ca2.n, cg1.n, cg2.n, cg3.n, cg4.n, cm.n, un.n)
17 	
18 	classes.sum = sum(classes)
19 	
20 	if(classes.sum == 1){
21 		single.sequences = single.sequences + 1
22 		next
23 	}
24 	
25 	if(un.n == classes.sum){
26 		unmatched = unmatched + 1
27 		next
28 	}
29 	
30 	in.classes = sum(classes > 0)
31 	
32 	matched = matched + in.classes #count in how many subclasses the sequence occurs.
33 	
34 	if(any(classes  == classes.sum)){
35 		multiple.in.one = multiple.in.one + 1
36 	} else if (un.n > 0) {
37 		some.unmatched = some.unmatched + 1
38 	} else {
39 		in.multiple = in.multiple + 1
40 	}
41 	
42 	id = as.numeric(dat[i,"seq_conc"])
43 	
44 	functionality = paste(unique(allc[,"Functionality"]), collapse=",")
45 	
46 	by.id.row = c()
47 	
48 	if(ca1.n > 0){
49 		cat(tbl(ca1), file=paste("IGA1_", id, ".html", sep=""))
50 	}
51 
52 	if(ca2.n > 0){
53 		cat(tbl(ca2), file=paste("IGA2_", id, ".html", sep=""))
54 	}
55 
56 	if(cg1.n > 0){
57 		cat(tbl(cg1), file=paste("IGG1_", id, ".html", sep=""))
58 	}
59 
60 	if(cg2.n > 0){
61 		cat(tbl(cg2), file=paste("IGG2_", id, ".html", sep=""))
62 	}
63 
64 	if(cg3.n > 0){
65 		cat(tbl(cg3), file=paste("IGG3_", id, ".html", sep=""))
66 	}
67 
68 	if(cg4.n > 0){
69 		cat(tbl(cg4), file=paste("IGG4_", id, ".html", sep=""))
70 	}
71 
72 	if(cm.n > 0){
73 		cat(tbl(cm), file=paste("IGM_", id, ".html", sep=""))
74 	}
75 
76 	if(un.n > 0){
77 		cat(tbl(un), file=paste("un_", id, ".html", sep=""))
78 	}
79 	
80 	ca1.html = make.link(id, "IGA1", ca1.n)
81 	ca2.html = make.link(id, "IGA2", ca2.n)
82 	
83 	cg1.html = make.link(id, "IGG1", cg1.n)
84 	cg2.html = make.link(id, "IGG2", cg2.n)
85 	cg3.html = make.link(id, "IGG3", cg3.n)
86 	cg4.html = make.link(id, "IGG4", cg4.n)
87 	
88 	cm.html = make.link(id, "IGM", cm.n)
89 	
90 	un.html = make.link(id, "un", un.n)
91 	
92 	#extra columns
93 	ca.n = ca1.n + ca2.n
94 	
95 	cg.n = cg1.n + cg2.n + cg3.n + cg4.n
96 	
97 	#in.classes
98 	
99 	in.ca.cg = (ca.n > 0 & cg.n > 0)
00 	
01 	in.ca1.ca2 = (ca1.n > 0 & ca2.n > 0)
02 	
03 	in.cg1.cg2 = (cg1.n > 0 & cg2.n > 0)
04 	in.cg1.cg3 = (cg1.n > 0 & cg3.n > 0)
05 	in.cg1.cg4 = (cg1.n > 0 & cg4.n > 0)
06 	in.cg2.cg3 = (cg2.n > 0 & cg3.n > 0)
07 	in.cg2.cg4 = (cg2.n > 0 & cg4.n > 0)
08 	in.cg3.cg4 = (cg3.n > 0 & cg4.n > 0)
09 	
10 	in.cg1.cg2.cg3 = (cg1.n > 0 & cg2.n > 0 & cg3.n > 0)
11 	in.cg2.cg3.cg4 = (cg2.n > 0 & cg3.n > 0 & cg4.n > 0)
12 	in.cg1.cg2.cg4 = (cg1.n > 0 & cg2.n > 0 & cg4.n > 0)
13 	in.cg1.cg3.cg4 = (cg1.n > 0 & cg3.n > 0 & cg4.n > 0)
14 	
15 	in.cg.all = (cg1.n > 0 & cg2.n > 0 & cg3.n > 0 & cg4.n > 0)
16 	
17 	
18 	
19 	
20 	#rw = c(as.character(dat[i,"seq_conc"]), functionality, ca1.html, ca2.html, cg1.html, cg2.html, cg3.html, cg4.html, cm.html, un.html)
21 	rw = c(as.character(dat[i,"seq_conc"]), functionality, ca1.html, ca2.html, cg1.html, cg2.html, cg3.html, cg4.html, cm.html, un.html)
22 	rw = c(rw, ca.n, cg.n, in.classes, in.ca.cg, in.ca1.ca2, in.cg1.cg2, in.cg1.cg3, in.cg1.cg4, in.cg2.cg3, in.cg2.cg4, in.cg3.cg4, in.cg1.cg2.cg3, in.cg2.cg3.cg4, in.cg1.cg2.cg4, in.cg1.cg3.cg4, in.cg.all)
23 
24 	cat(tr(rw), file=main.html, append=T)
25 	
26 	
27 	for(i in 1:nrow(allc)){ #generate html by id
28 		html = make.link(id, allc[i,"best_match"], allc[i,"Sequence.ID"])
29 		cat(paste(html, "<br />"), file=sequence.id.page, append=T)
30 	}
31 }
32 
33 cat("</table>", file=main.html, append=T)
34 
35 print(paste("Single sequences:", single.sequences))
36 print(paste("Sequences in multiple subclasses:", in.multiple))
37 print(paste("Multiple sequences in one subclass:", multiple.in.one))
38 print(paste("Matched with unmatched:", some.unmatched))
39 print(paste("Count that should match 'matched' sequences:", matched))
40 
41 #ACGT overview
42 
43 NToverview = merged[!grepl("^unmatched", merged$best_match),]
44 
45 NToverview$seq = paste(NToverview$CDR1.IMGT.seq, NToverview$FR2.IMGT.seq, NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq, sep="_")
46 
47 NToverview$A = nchar(gsub("[^Aa]", "", NToverview$seq))
48 NToverview$C = nchar(gsub("[^Cc]", "", NToverview$seq))
49 NToverview$G = nchar(gsub("[^Gg]", "", NToverview$seq))
50 NToverview$T = nchar(gsub("[^Tt]", "", NToverview$seq))
51 
52 #Nsum = data.frame(Sequence.ID="-", best_match="Sum", seq="-", A = sum(NToverview$A), C = sum(NToverview$C), G = sum(NToverview$G), T = sum(NToverview$T))
53 
54 #NToverview = rbind(NToverview, NTsum)
55 
56 NTresult = data.frame(nt=c("A", "C", "T", "G"))
57 
58 for(clazz in gene.classes){
59 	NToverview.sub = NToverview[grepl(paste("^", clazz, sep=""), NToverview$best_match),]
60 	new.col.x = c(sum(NToverview.sub$A), sum(NToverview.sub$C), sum(NToverview.sub$T), sum(NToverview.sub$G))
61 	new.col.y = sum(new.col.x)
62 	new.col.z = round(new.col.x / new.col.y * 100, 2)
63 	
64 	tmp = names(NTresult)
65 	NTresult = cbind(NTresult, data.frame(new.col.x, new.col.y, new.col.z))
66 	names(NTresult) = c(tmp, paste(clazz, c("x", "y", "z"), sep=""))
67 }
68 
69 write.table(NToverview[,c("Sequence.ID", "best_match", "seq", "A", "C", "G", "T")], NToverview.file, quote=F, sep="\t", row.names=F, col.names=T)
70 
71 NToverview = NToverview[!grepl("unmatched", NToverview$best_match),]
72 
73 new.col.x = c(sum(NToverview$A), sum(NToverview$C), sum(NToverview$T), sum(NToverview$G))
74 new.col.y = sum(new.col.x)
75 new.col.z = round(new.col.x / new.col.y * 100, 2)
76 
77 tmp = names(NTresult)
78 NTresult = cbind(NTresult, data.frame(new.col.x, new.col.y, new.col.z))
79 names(NTresult) = c(tmp, paste("all", c("x", "y", "z"), sep=""))
80 
81 names(hotspot.analysis.sum) = names(NTresult)
82 
83 hotspot.analysis.sum = rbind(hotspot.analysis.sum, NTresult)
84 
85 write.table(hotspot.analysis.sum, hotspot.analysis.sum.file, quote=F, sep=",", row.names=F, col.names=F, na="0")
86 
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