| chr11first | R Documentation | 
For 4 samples on chr11 (hg19), this data set counts the first base pair of aligned reads at each genomic position. In contrast, chr11ChIPseq counts every base pair in each read (and each read is about 100bp, so that means there is some auto-correlation in chr11ChIPseq, but not in chr11first).
data("chr11first")A data frame with 23252 observations on the following 4 variables.
sample.ida factor with levels for each of 4 samples
chromStartinteger vector: base before, on chr11
chromEndinteger vector: last base on chr11
countinteger: aligned first base read counts
H3K4me3_TDH_immune chunk 5 in http://cbio.ensmp.fr/~thocking/chip-seq-chunk-db/ which in turn comes from http://epigenomesportal.ca/
data(chr11ChIPseq)
data(chr11first)
library(ggplot2)
ann.colors <-
  c(noPeaks="#f6f4bf",
    peakStart="#ffafaf",
    peakEnd="#ff4c4c",
    peaks="#a445ee")
both <- list(coverage=chr11ChIPseq$coverage, first=chr11first)
representations <- NULL
one.sample <- "McGill0322"
for(data.type in names(both)){
  one <- subset(both[[data.type]], sample.id==one.sample)
  representations <- rbind(representations, data.frame(data.type, one))
}
one.sample.regions <- subset(
  chr11ChIPseq$regions, sample.id==one.sample)
if(interactive() && require(ggplot2)){
ggplot()+
  scale_fill_manual("annotation", values=ann.colors,
                    breaks=names(ann.colors))+
  penaltyLearning::geom_tallrect(aes(xmin=chromStart/1e3, xmax=chromEnd/1e3,
                    fill=annotation),
                data=one.sample.regions, alpha=1/2)+
  theme_bw()+
  theme(panel.margin=grid::unit(0, "cm"))+
  facet_grid(data.type ~ ., scales="free")+
  geom_step(aes(chromStart/1e3, count), data=representations)+
  xlab("position on chr11 (kilo base pairs)")
}
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