# script for UCSC tracks
awk -f /data/images/proton/make-stranded-sampled-mm10-tracks1.awk  reads.txt-VKmerged  > make-stranded-sampled-mm10-tracks.sh
source make-stranded-sampled-mm10-tracks.sh

# config for metaseqR
awk -f /data/images/proton/make-metaseqr-targets-from-reads1.awk reads.txt-VKmerged > targets.txt

library(metaseqR)

the.path <- "/mnt/max/b/genomics_facility/proton2/run433/wwwVKlab"

the.contrasts.1 <- c(
	"APCGFP_vs_APCunsort",
	"APCTOM_vs_APCunsort",
	"APCGFP_vs_APCTOM"
)

metaseqr(
    sample.list=file.path(the.path,"targets.txt"),
    contrast=the.contrasts.1,
    annotation="download",
    org="mm10",
    count.type="utr",
    normalization="deseq",
    statistics="deseq",
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433"),
    restrict.cores=0.5,
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias","meandiff",
        "meanvar","biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05,
    export.what=c("annotation","p.value","adj.p.value","fold.change",
		"counts","flags"),
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.counts.table=TRUE,
	report.top=0.05
)

the.contrasts.1 <- c(
	"APCGFP_vs_GIH"
)
metaseqr(
    sample.list=file.path(the.path,"targets2a.txt"),
    contrast=the.contrasts.1,
    annotation="download",
    org="mm10",
    count.type="utr",
    normalization="deseq",
    statistics="deseq",
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433-2a"),
    restrict.cores=0.5,
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias","meandiff",
        "meanvar","biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05,
    export.what=c("annotation","p.value","adj.p.value","fold.change",
		"counts","flags"),
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.counts.table=TRUE,
	report.top=0.05
)

the.contrasts.1 <- c(
	"APCTOM_vs_TI"
)

metaseqr(
    sample.list=file.path(the.path,"targets2b.txt"),
    contrast=the.contrasts.1,
    annotation="download",
    org="mm10",
    count.type="utr",
    normalization="deseq",
    statistics="deseq",
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433-2b"),
    restrict.cores=0.5,
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias","meandiff",
        "meanvar","biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05,
    export.what=c("annotation","p.value","adj.p.value","fold.change",
		"counts","flags"),
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.counts.table=TRUE,
	report.top=0.05
)


#@
the.contrasts.1 <- c(
	"APCTOM_vs_TI"
)
# Read transcript data from external file
transcript.data <- read.delim("/data/results/tools/rnaseq/metaseqr/mm10/transcript_data_mm10.txt.gz")
rownames(transcript.data) <- as.character(transcript.data$transcript_id)

# metaseqR related variables outside the pipeline
multic <- check.parallel(0.5) # If wish to use multiple cores
assign("VERBOSE",TRUE,envir=metaseqR:::meta.env)

# Read targets files
message("Reading targets file...")
targets <- read.targets("targets2b.txt")

# Do the counting based with bam files in the targets file
r2c.out <- read2count(targets,transcript.data,has.all.fields=TRUE,multic=multic)

# Create a counts table to be passed as "embedded" annotation
the.counts <- cbind(r2c.out$mergedann,r2c.out$counts)

metaseqr(
	counts=the.counts,
    sample.list=targets$samples,
    contrast=the.contrasts.1,
    annotation="embedded",
	gene.file="/data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz",
	id.col=4, # required with embedded annotation
	gc.col=5, # required with embedded annotation
	bt.col=8, # required with embedded annotation
	name.col=7, # required with embedded annotation
    org="custom",
    count.type="utr",
    normalization="deseq", # or whatever supported
    statistics="deseq",
#statistics = c("deseq", "edger", "limma", "nbpseq"),
#             meta.p = "pandora",
#statistics = c("deseq", "edger", "noiseq", "bayseq", "limma", "nbpseq"),
#             meta.p = "pandora",
#    statistics="deseq", # or whatever supported, more than one also
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433-2b"),
    restrict.cores=0.5, # fraction of available cores to use
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias",
        "biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),  # it's the default anyway
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05, # only for the truncated significant list output, all results are exported anyway
    export.what=c("annotation","p.value","adj.p.value","fold.change","meta.p.value","adj.meta.p.value","stats",
        "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.stats="mean"
)


the.contrasts.1 <- c(
	"APCunsort_vs_UI"
)
targets <- read.targets("targets2c.txt")

# Do the counting based with bam files in the targets file
r2c.out <- read2count(targets,transcript.data,has.all.fields=TRUE,multic=multic)

# Create a counts table to be passed as "embedded" annotation
the.counts <- cbind(r2c.out$mergedann,r2c.out$counts)

metaseqr(
	counts=the.counts,
    sample.list=targets$samples,
    contrast=the.contrasts.1,
    annotation="embedded",
	gene.file="/data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz",
	id.col=4, # required with embedded annotation
	gc.col=5, # required with embedded annotation
	bt.col=8, # required with embedded annotation
	name.col=7, # required with embedded annotation
    org="custom",
    count.type="utr",
    normalization="deseq", # or whatever supported
    statistics="deseq",
#statistics = c("deseq", "edger", "limma", "nbpseq"),
#             meta.p = "pandora",
#statistics = c("deseq", "edger", "noiseq", "bayseq", "limma", "nbpseq"),
#             meta.p = "pandora",
#    statistics="deseq", # or whatever supported, more than one also
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433-2c"),
    restrict.cores=0.5, # fraction of available cores to use
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias",
        "biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),  # it's the default anyway
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05, # only for the truncated significant list output, all results are exported anyway
    export.what=c("annotation","p.value","adj.p.value","fold.change","meta.p.value","adj.meta.p.value","stats",
        "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.stats="mean"
)


the.contrasts.1 <- c(
	"APCGFP_vs_GFP"
)
targets <- read.targets("targets3a.txt")

# Do the counting based with bam files in the targets file
r2c.out <- read2count(targets,transcript.data,has.all.fields=TRUE,multic=multic)

# Create a counts table to be passed as "embedded" annotation
the.counts <- cbind(r2c.out$mergedann,r2c.out$counts)

metaseqr(
	counts=the.counts,
    sample.list=targets$samples,
    contrast=the.contrasts.1,
    annotation="embedded",
	gene.file="/data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz",
	id.col=4, # required with embedded annotation
	gc.col=5, # required with embedded annotation
	bt.col=8, # required with embedded annotation
	name.col=7, # required with embedded annotation
    org="custom",
    count.type="utr",
    normalization="deseq", # or whatever supported
    statistics="deseq",
#statistics = c("deseq", "edger", "limma", "nbpseq"),
#             meta.p = "pandora",
#statistics = c("deseq", "edger", "noiseq", "bayseq", "limma", "nbpseq"),
#             meta.p = "pandora",
#    statistics="deseq", # or whatever supported, more than one also
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433-3a"),
    restrict.cores=0.5, # fraction of available cores to use
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias",
        "biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),  # it's the default anyway
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05, # only for the truncated significant list output, all results are exported anyway
    export.what=c("annotation","p.value","adj.p.value","fold.change","meta.p.value","adj.meta.p.value","stats",
        "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.stats="mean"
)




the.contrasts.1 <- c(
	"APCTOM_vs_TOM"
)
targets <- read.targets("targets3b.txt")

# Do the counting based with bam files in the targets file
r2c.out <- read2count(targets,transcript.data,has.all.fields=TRUE,multic=multic)

# Create a counts table to be passed as "embedded" annotation
the.counts <- cbind(r2c.out$mergedann,r2c.out$counts)

metaseqr(
	counts=the.counts,
    sample.list=targets$samples,
    contrast=the.contrasts.1,
    annotation="embedded",
	gene.file="/data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz",
	id.col=4, # required with embedded annotation
	gc.col=5, # required with embedded annotation
	bt.col=8, # required with embedded annotation
	name.col=7, # required with embedded annotation
    org="custom",
    count.type="utr",
    normalization="deseq", # or whatever supported
    statistics="deseq",
#statistics = c("deseq", "edger", "limma", "nbpseq"),
#             meta.p = "pandora",
#statistics = c("deseq", "edger", "noiseq", "bayseq", "limma", "nbpseq"),
#             meta.p = "pandora",
#    statistics="deseq", # or whatever supported, more than one also
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433-3b"),
    restrict.cores=0.5, # fraction of available cores to use
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias",
        "biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),  # it's the default anyway
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05, # only for the truncated significant list output, all results are exported anyway
    export.what=c("annotation","p.value","adj.p.value","fold.change","meta.p.value","adj.meta.p.value","stats",
        "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.stats="mean"
)


the.contrasts.1 <- c(
	"APCunsort_vs_Unsort"
)
targets <- read.targets("targets3c.txt")

# Do the counting based with bam files in the targets file
r2c.out <- read2count(targets,transcript.data,has.all.fields=TRUE,multic=multic)

# Create a counts table to be passed as "embedded" annotation
the.counts <- cbind(r2c.out$mergedann,r2c.out$counts)

metaseqr(
	counts=the.counts,
    sample.list=targets$samples,
    contrast=the.contrasts.1,
    annotation="embedded",
	gene.file="/data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz",
	id.col=4, # required with embedded annotation
	gc.col=5, # required with embedded annotation
	bt.col=8, # required with embedded annotation
	name.col=7, # required with embedded annotation
    org="custom",
    count.type="utr",
    normalization="deseq", # or whatever supported
    statistics="deseq",
#statistics = c("deseq", "edger", "limma", "nbpseq"),
#             meta.p = "pandora",
#statistics = c("deseq", "edger", "noiseq", "bayseq", "limma", "nbpseq"),
#             meta.p = "pandora",
#    statistics="deseq", # or whatever supported, more than one also
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_run433-3c"),
    restrict.cores=0.5, # fraction of available cores to use
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise","filtered",
        "correl","pairwise","boxplot","gcbias","lengthbias",
        "biodist","volcano","deheatmap"
    ),
    exon.filters=NULL,
    gene.filters=list(
       length=list(
            length=500
        ),
        avg.reads=list(
            average.per.bp=100,
            quantile=0.25
        ),
        expression=list(
            median=TRUE,
            mean=FALSE,
            quantile=NA,
            known=NA,
            custom=NA
        ),  # it's the default anyway
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05, # only for the truncated significant list output, all results are exported anyway
    export.what=c("annotation","p.value","adj.p.value","fold.change","meta.p.value","adj.meta.p.value","stats",
        "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added
    export.scale=c("log2","rpgm"),
    export.values="normalized",
    export.stats="mean"
)