ln -s /data/images/proton2/run374/tophat_033/sort_uniq.bam SK3R1-Tbctrl2.bam
ln -s /data/images/proton2/run374/tophat_034/sort_uniq.bam SK3R2-Tbctrl3.bam
ln -s /data/images/proton2/run374/tophat_035/sort_uniq.bam SK3R3-Tbctrl4.bam
ln -s /data/images/proton2/run373/tophat_010/sort_uniq.bam SK3R10-Tdil22-9b.bam
ln -s /data/images/proton2/run373/tophat_011/sort_uniq.bam SK3R11-Tdil22-10b.bam
ln -s /data/images/proton2/run373/tophat_012/sort_uniq.bam SK3R12-Tdil22-11b.bam
ln -s /data/images/proton2/run373/tophat_016/sort_uniq.bam SK3R16-Lbil22-7b.bam
ln -s /data/images/proton2/run373/tophat_018/sort_uniq.bam SK3R18-Lbil22-9b.bam
ln -s /data/images/proton2/run373/tophat_019/sort_uniq.bam SK3R19-Lcctrl3b.bam
ln -s /data/images/proton2/run373/tophat_004/sort_uniq.bam SK3R4-Tbil22-7b.bam
ln -s /data/images/proton2/run373/tophat_006/sort_uniq.bam SK3R6-Tbil22-12b.bam
ln -s /data/images/proton2/run371/tophat_013/sort_uniq.bam SK3R13-Lbctrl4.bam
ln -s /data/images/proton2/run371/tophat_014/sort_uniq.bam SK3R14-Lbctrl5.bam
ln -s /data/images/proton2/run371/tophat_015/sort_uniq.bam SK3R15-Lbctrl6.bam
ln -s /data/images/proton2/run371/tophat_016/sort_uniq.bam SK3R16-Lbil22-7.bam
ln -s /data/images/proton2/run371/tophat_017/sort_uniq.bam SK3R17-Lbil22-8.bam
ln -s /data/images/proton2/run371/tophat_018/sort_uniq.bam SK3R18-Lbil22-9.bam
ln -s /data/images/proton2/run371/tophat_019/sort_uniq.bam SK3R19-Lcctrl3.bam
ln -s /data/images/proton2/run371/tophat_020/sort_uniq.bam SK3R20-Lcctrl4.bam
ln -s /data/images/proton2/run371/tophat_021/sort_uniq.bam SK3R21-Lcctrl6.bam
ln -s /data/images/proton2/run371/tophat_022/sort_uniq.bam SK3R22-Lcil22-8.bam
ln -s /data/images/proton2/run371/tophat_023/sort_uniq.bam SK3R23-Lcil22-11.bam
ln -s /data/images/proton2/run371/tophat_024/sort_uniq.bam SK3R24-Lcil22-12.bam
ln -s /data/images/proton2/run370/tophat_010/sort_uniq.bam SK3R10-Tdil22-9.bam
ln -s /data/images/proton2/run370/tophat_011/sort_uniq.bam SK3R11-Tdil22-10.bam
ln -s /data/images/proton2/run370/tophat_012/sort_uniq.bam SK3R12-Tdil22-11.bam
ln -s /data/images/proton2/run370/tophat_004/sort_uniq.bam SK3R4-Tbil22-7.bam
ln -s /data/images/proton2/run370/tophat_005/sort_uniq.bam SK3R5-Tbil22-11.bam
ln -s /data/images/proton2/run370/tophat_006/sort_uniq.bam SK3R6-Tbil22-12.bam
ln -s /data/images/proton2/run370/tophat_007/sort_uniq.bam SK3R7-Tdctrl2.bam
ln -s /data/images/proton2/run370/tophat_008/sort_uniq.bam SK3R8-Tdctrl3.bam
ln -s /data/images/proton2/run370/tophat_009/sort_uniq.bam SK3R9-Tdctrl6.bam

samtools merge SK3R4-Tbil22-7m.bam SK3R4-Tbil22-7.bam SK3R4-Tbil22-7b.bam
samtools merge SK3R6-Tbil22-12m.bam SK3R6-Tbil22-12.bam SK3R6-Tbil22-12b.bam
samtools merge SK3R11-Tdil22-10m.bam SK3R11-Tdil22-10.bam SK3R11-Tdil22-10b.bam
samtools merge SK3R10-Tdil22-9m.bam SK3R10-Tdil22-9.bam SK3R10-Tdil22-9b.bam
samtools merge SK3R12-Tdil22-11m.bam SK3R12-Tdil22-11.bam SK3R12-Tdil22-11b.bam

samtools merge SK3R16-Lbil22-7m.bam SK3R16-Lbil22-7.bam SK3R16-Lbil22-7b.bam
samtools merge SK3R18-Lbil22-9m.bam SK3R18-Lbil22-9.bam SK3R18-Lbil22-9b.bam
samtools merge SK3R19-Lcctrl3m.bam SK3R19-Lcctrl3.bam SK3R19-Lcctrl3b.bam

for i in *bam
 do
 echo $i
 samtools flagstat $i | grep total
 done

require(metaseqR)

the.path <- "/data/images/proton2/run374/SKobold"
the.contrasts.1 <- c(	"Lbctrl_vs_Lbil22"	)

# 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("targets-LbctrlVSLcctrl.txt")
targets <- read.targets("targets-Lbil22VSLcil22.txt")
targets <- read.targets("targets-LcctrlVSLcil22.txt")
targets <- read.targets("targets-TbctrlVSTbil22.txt")
targets <- read.targets("targets-TbctrlVSTdctrl.txt")
targets <- read.targets("targets-Tbil22VSTdil22.txt")
targets <- read.targets("targets-TdctrlVSTdil22.txt")

targets <- read.targets("targets-LbctrlVSLbil22.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)


# Run metaseqR
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 = 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_quantseq_LbctrlVSLbil22"),
    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","stats",
#        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"
)

targets <- read.targets("targets-LbctrlVSLcctrl.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="Lbctrl_vs_Lcctrl",
    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 = 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_quantseq_LbctrlVSLcctrl"),
    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","stats",
#        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"
)

targets <- read.targets("targets-Lbil22VSLcil22.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="Lbil22_vs_Lcil22",
    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 = 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_quantseq_Lbil22VSLcil22"),
    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","stats",
#        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"
)

targets <- read.targets("targets-LcctrlVSLcil22.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="Lcctrl_vs_Lcil22",
    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 = 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_quantseq_LcctrlVSLcil22"),
    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","stats",
#        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"
)

targets <- read.targets("targets-TbctrlVSTbil22.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="Tbctrl_vs_Tbil22",
    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 = 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_quantseq_TbctrlVSTbil22"),
    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","stats",
#        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"
)

targets <- read.targets("targets-TbctrlVSTdctrl.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="Tbctrl_vs_Tdctrl",
    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 = 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_quantseq_TbctrlVSTdctrl"),
    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","stats",
#        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"
)

targets <- read.targets("targets-Tbil22VSTdil22.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="Tbil22_vs_Tdil22",
    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 = 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_quantseq_Tbil22VSTdil22"),
    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","stats",
#        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"
)

targets <- read.targets("targets-TdctrlVSTdil22.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="Tdctrl_vs_Tdil22",
    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 = 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_quantseq_TdctrlVSTdil22"),
    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","stats",
#        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"
)