LIBRARY=NCms10058_1
BAMFILES=../Moirai/NCms10058_1.CAGEscan_short-reads.20150625154711/properly_paired_rmdup/*bam
level1.py -o $LIBRARY.l1.gz -f 66 -F 516 $BAMFILES
level2.py -t 0 -o $LIBRARY.l2.gz $LIBRARY.l1.gz
function osc2bed {
zcat $1 |
grep -v \# |
sed 1d |
awk '{OFS="\t"}{print $2, $3, $4, "l1", "1000", $5}'
}
function bed2annot {
bedtools intersect -a $1 -b ../annotation/annot.bed -s -loj |
awk '{OFS="\t"}{print $1":"$2"-"$3$6,$10}' |
bedtools groupby -g 1 -c 2 -o collapse
}
function bed2symbols {
bedtools intersect -a $1 -b ../annotation/gencode.v14.annotation.genes.bed -s -loj |
awk '{OFS="\t"}{print $1":"$2"-"$3$6,$10}' |
bedtools groupby -g 1 -c 2 -o distinct
}
osc2bed $LIBRARY.l2.gz | tee $LIBRARY.l2.bed | bed2annot - > $LIBRARY.l2.annot
bed2symbols $LIBRARY.l2.bed > $LIBRARY.l2.genes
## Opening NCms10058_1.l1.gz
library(oscR) # See https://github.com/charles-plessy/oscR for oscR.
library(smallCAGEqc) # See https://github.com/charles-plessy/smallCAGEqc for smallCAGEqc.
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.0-10
library(ggplot2)
stopifnot(
packageVersion("oscR") >= "0.1.1"
, packageVersion("smallCAGEqc") > "0.10.0"
)
LIBRARY <- "NCms10058_1"
l2_NCki <- read.osc(paste(LIBRARY,'l2','gz',sep='.'), drop.coord=T, drop.norm=T)
colnames(l2_NCki) <- sub('raw.NCms10058_1.','NCki_',colnames(l2_NCki))
colSums(l2_NCki)
## NCki_HeLa_PS_A NCki_HeLa_PS_B NCki_HeLa_PS_C NCki_HeLa_RanN6_A NCki_HeLa_RanN6_B NCki_HeLa_RanN6_C
## 11800 13969 22764 14137 13556 10430
## NCki_THP1_PS_A NCki_THP1_PS_B NCki_THP1_PS_C NCki_THP1_RanN6_A NCki_THP1_RanN6_B NCki_THP1_RanN6_C
## 15157 15453 13092 8708 14536 17122
In all the 3 libraries used, one contain only few reads tags. The smallest one has 8,708 counts. In order to make meaningful comparisons, all of them are subsapled to 8700 counts.
set.seed(1)
l2.sub1 <- t(rrarefy(t(l2_NCki),min(8700)))
colSums(l2.sub1)
## NCki_HeLa_PS_A NCki_HeLa_PS_B NCki_HeLa_PS_C NCki_HeLa_RanN6_A NCki_HeLa_RanN6_B NCki_HeLa_RanN6_C
## 8700 8700 8700 8700 8700 8700
## NCki_THP1_PS_A NCki_THP1_PS_B NCki_THP1_PS_C NCki_THP1_RanN6_A NCki_THP1_RanN6_B NCki_THP1_RanN6_C
## 8700 8700 8700 8700 8700 8700
Load the QC data produced by the Moirai workflow with which the libraries were processed. Sort in the same way as the l1 and l2 tables, to allow for easy addition of columns.
libs <- loadMoiraiStats(multiplex = "NCms10058_1.multiplex.txt", summary = "../Moirai/NCms10058_1.CAGEscan_short-reads.20150625154711/text/summary.txt", pipeline = "CAGEscan_short-reads")
Count the number of unique L2 clusters per libraries after subsampling, and add
this to the QC table. Each subsampling will give a different result, but the
mean result can be calculated by using the rarefy
function at the same scale
as the subsampling.
libs["l2.sub1"] <- colSums(l2.sub1 > 0)
libs["l2.sub1.exp"] <- rarefy(t(l2_NCki), min(colSums(l2_NCki)))
Richness should also be calculated on the whole data.
libs["r100.l2"] <- rarefy(t(l2_NCki),100)
boxplot(data=libs, r100.l2 ~ group, ylim=c(80,100), las=1)
Differences of sampling will not bias distort the distribution of reads between annotations, so the non-subsampled library is used here.
annot.l2 <- read.table(paste(LIBRARY,'l2','annot',sep='.'), head=F, col.names=c('id', 'feature'), row.names=1)
annot.l2 <- hierarchAnnot(annot.l2)
rownames(libs) <- sub("HeLa", "NCki_HeLa", rownames(libs))
rownames(libs) <- sub("THP1", "NCki_THP1", rownames(libs))
libs <- cbind(libs, t(rowsum(l2_NCki, annot.l2[,'class'])))
libs$samplename <- sub('HeLa', 'NCki_HeLa', libs$samplename)
libs$samplename <- sub('THP1', 'NCki_THP1', libs$samplename)
genesymbols <- read.table(paste(LIBRARY,'l2','genes',sep='.'), col.names=c("cluster","symbol"), stringsAsFactors=FALSE)
rownames(genesymbols) <- genesymbols$cluster
g2 <- rowsum(l2_NCki, genesymbols$symbol)
countSymbols <- countSymbols(g2)
libs[colnames(l2_NCki),"genes"] <- (countSymbols)
Number of genes detected in sub-sample
l2.sub1 <- data.frame(l2.sub1)
g2.sub1 <- rowsum(l2.sub1, genesymbols$symbol)
countSymbols.sub1 <- countSymbols(g2.sub1)
libs[colnames(l2.sub1),"genes.sub1"] <- (countSymbols.sub1)
save the different tables produced for later analysis
write.table(l2_NCki, "l2_NCki_1.txt", sep = "\t", quote=FALSE)
write.table(l2.sub1, "l2.sub1_NCki_1.txt", sep = "\t", quote=FALSE)
write.table(g2.sub1, 'g2.sub1_NCki_1.txt', sep="\t", quote=F)
write.table(libs, 'libs_NCki_1.txt', sep="\t", quote=F)