#!/usr/bin/env Rscript
#+ echo=FALSE
devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.4/R/core_commons.R")
devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.4/R/ggplot_commons.R")


require(knitr)
require.auto(tidyr)
require(tidyr)
#if (!require("DT")) devtools::install_github("rstudio/DT") ## see http://rstudio.github.io/DT/

## setwd("/projects/bioinfo/holger/projects/krause_chipseq/macs2")

########################################################################################################################
#' # Called Peaks Overview


## https://github.com/taoliu/MACS/

readMacsPeaks <- function(peakFile){
   data.table::fread(peakFile, header=F) %>% as.df() %>%  mutate(file=basename(peakFile))
}

#5th column: -10*log10pvalue of peak region" (see http://seqanswers.com/forums/showthread.php?t=18674)
## for spec see https://github.com/taoliu/MACS/#output-files
n_peaks <- list.files(".", "*.narrowPeak.*", full.names=TRUE, recursive=T) %>%
    ldply(readMacsPeaks) %>%
    set_names(c("chromosome_name", "start_position", "end_position", "peak_id", "score", "strand", "fold_change", "pvalue", "qvalue", "sum_rel_to_start", "file")) %>%
    select(-sum_rel_to_start)


b_peaks <- list.files(".", "*.broadPeak.*", full.names=TRUE, recursive=T) %>%
    ldply(readMacsPeaks) %>%
    set_names(c("chromosome_name", "start_position", "end_position", "peak_id", "score", "strand", "fold_change", "pvalue", "qvalue", "file"))

peaks <- rbind_list(n_peaks, b_peaks)

peaks %<>% separate(file, c("sample", "peak_type", "ext"), sep="[.]") %>% select(-ext)

splitProtTime <- . %>% separate(sample, c("protein", "timpoint"), sep="_", remove=F)
#peaks %>% splitProtTime %>% head

peaks %<>% mutate(peak_width=end_position-start_position)

save(peaks, file="peaks.RData")
# peaks <- local(get(load("peaks.RData")))


peaks %>%
#    sample_frac(0.3) %>%
    ggplot(aes(peak_width)) +
        geom_histogram() +
        facet_grid(sample ~ peak_type)  +
        # + geom_vline(yintercept=0, color="blue")
        ggtitle("peak width distribution") +
        xlim(0,1000)

peaks %>%
#    sample_frac(0.3) %>%
    ggplot(aes(sample, peak_width)) +
        geom_jitter(alpha=0.05, data=sample_frac(peaks, 0.1)) +
        geom_violin(alpha=0.8) +
        facet_wrap(~ peak_type)  +
        # + geom_vline(yintercept=0, color="blue")
        ggtitle("peak width distribution") +
        ylim(0,1000) +
        rotXlab()


peaks %>%
#    sample_frac(0.3) %>%
    ggplot(aes(sample, score)) +
        geom_jitter(alpha=0.05, data=sample_frac(peaks, 0.1)) +
        geom_violin(alpha=0.8) +
        facet_wrap(~ peak_type)  +
        # + geom_vline(yintercept=0, color="blue")
        ggtitle("peak score distribution") +
        ylim(0,100) +
        rotXlab()


# note: max: score ~ 10*qvalue


#peaks %>% group_by(sample, type) %>% summarize(num_peaks=n(), mean_width=median(end_position-start_position))
peaks %>% ggplot(aes(sample, fill=peak_type)) + geom_bar(position="dodge") + ggtitle("peak counts")


peaks %>%  sample_frac(0.1) %>%
    group_by(sample) %>%
    arrange(-score) %>%
    mutate(x=row_number()/n()) %>%
#    sample_frac(0.3) %>%
    ggplot(aes(x, qvalue, color=sample)) +
        geom_line() +
        facet_wrap(~ peak_type) +
        xlim(0, 0.2) + ylim(0,50)


########################################################################################################################
#' # ChippeakAnno

# see [docs](http://www.bioconductor.org/packages/release/bioc/vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.pdf)

#+ eval=FALSE, echo=FALSE
## DEBUG start
if(F){
#http://www.bioconductor.org/packages/release/bioc/vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.pdf
require(ChIPpeakAnno)
data(TSS.zebrafish.Zv9)

## https://www.biostars.org/p/115101/
## http://master.bioconductor.org/help/course-materials/2009/SeattleNov09/IRanges/IRangesOverview.R

peakSet <- peaks %>% sample_n(10000) %>% filter(sample=="H3HA_Dome", peak_type=="narrow_peaks")
peakRanges <-with(peakSet,  RangedData(ranges = IRanges(start_position, end_position), strand = ".", space = chromosome_name))

### other feature types
#Anno_exon = getAnnotation(mart, featureType=c("Exon"))
#Anno_TSS = getAnnotation(mart, featureType=c("TSS"))
#Anno_miRNA = getAnnotation(mart, featureType=c("miRNA"))
#annotatedPeak_exon = annotatePeakInBatch(pr, AnnotationData=Anno_exon)

annotatedPeak = annotatePeakInBatch (peakRanges, AnnotationData = TSS.zebrafish.Zv9)

annotatedPeak %>% as.df()%>% ggplot(aes(insideFeature))  + geom_bar()

}
## DEBUG end

#+
require(ChIPpeakAnno)
data(TSS.zebrafish.Zv9)


## apply csa to all sets
annotatePeaks <- function(somePeaks, subsample=min(nrow(peaks), 10000)){
    peakRanges <- somePeaks %>%
        sample_n(subsample) %$%
        RangedData(ranges = IRanges(start_position, end_position), strand = ".", space = chromosome_name)

    annotatePeakInBatch (peakRanges, AnnotationData = TSS.zebrafish.Zv9) %>% as.df()
}

annoPeaks <- peaks %>% group_by(sample, peak_type) %>% do(annotatePeaks(.))

save(annoPeaks, file="annoPeaks.RData")
# annoPeaks <- local(get(load("annoPeaks.RData")))


#' CSA adds ovelap category, nearest gene id and distance to TSS
#+ results='asis'
annoPeaks %>% ungroup() %>% sample_n(10) %>% kable()

#+
#with(peaks, as.data.frame(table(sample, peak_type)))
#peaks %$% as.data.frame(table(sample, peak_type))

annoPeaks %>%
#    as.data.frame(table(sample, peak_type, insideFeature)) %>%
    ggplot(aes(sample, fill=insideFeature)) + geom_bar() + facet_wrap(~peak_type)