library(metaseqR)
the.path <- "/data/images/proton2/run342/www"

the.contrasts.1 <- c(
	"Grp4aMinus_vs_Grp4aPlus",
	"Grp4fMinus_vs_Grp4fPlus"
)

metaseqr(
    sample.list=file.path(the.path,"targets.txt"),
    contrast=the.contrasts.1,
    annotation="download",
    org="mm10",
    refdb="ensembl",
    count.type="utr",
    normalization="deseq", # or whatever supported
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_pandora_run342a"),
    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(
"Grp4aMinus4fMinus_vs_Grp4aPlus",
"Grp4aMinus4fMinus_vs_Grp4fPlus",
"Grp4aMinus4fMinus_vs_Cplus1Plus"
)

metaseqr(
    sample.list=file.path(the.path,"targets.txt2"),
    contrast=the.contrasts.1,
    annotation="download",
    org="mm10",
    refdb="ensembl",
    count.type="utr",
    normalization="deseq",
statistics = c("deseq", "edger", "limma", "nbpseq"),
             meta.p = "pandora",
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_pandora_run342b"),
    restrict.cores=0.5,
    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
        ),
        biotype=get.defaults("biotype.filter","mm10")
    ),
    pcut=0.05,
    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",
    export.counts.table=TRUE,
	report.top=0.05
)

#hg19
the.contrasts.1 <- c(
"Cplus_vs_vectorhuh75"
)

metaseqr(
    sample.list=file.path(the.path,"targets.txt-hsa"),
    contrast=the.contrasts.1,
    annotation="download",
    org="hg19",
    count.type="utr",
    normalization="deseq",
statistics = c("deseq", "edger", "limma", "nbpseq"),
             meta.p = "pandora",
#    statistics="deseq",
    fig.format=c("png","pdf"),
    export.where=file.path(the.path,"metaseqr_pandora_run342d"),
    restrict.cores=0.5,
    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
        ),
        biotype=get.defaults("biotype.filter","hg19")
    ),
    pcut=0.05,
    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",
    export.counts.table=TRUE,
	report.top=0.05
)