### R code from vignette source 'metaseqr-pdf.Rnw'
### Encoding: UTF-8

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### code chunk number 1: init-init
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library(metaseqR)


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### code chunk number 2: init-metaseqr (eval = FALSE)
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## library(metaseqR)
## help(metaseqr) # or
## help(metaseqr.main)


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### code chunk number 3: help-1 (eval = FALSE)
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## help(hg18.exon.data)
## help(mm9.gene.data)


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### code chunk number 4: data-1
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data("mm9.gene.data",package="metaseqR")


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### code chunk number 5: head-1
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head(mm9.gene.counts)


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### code chunk number 6: random-1
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sample.list.mm9


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### code chunk number 7: random-2
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libsize.list.mm9


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### code chunk number 8: example-1
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library(metaseqR)
data("mm9.gene.data",package="metaseqR")
result <- metaseqr(
       counts=mm9.gene.counts,
       sample.list=sample.list.mm9,
       contrast=c("e14.5_vs_adult_8_weeks"),
       libsize.list=libsize.list.mm9,
       annotation="download",
       org="mm9",
       count.type="gene",
       normalization="edger",
       statistics="edger",
       pcut=0.05,
       fig.format=c("png","pdf"),
       export.what=c("annotation","p.value","meta.p.value",
          "adj.meta.p.value","fold.change"),
       export.scale=c("natural","log2"),
       export.values="normalized",
       export.stats=c("mean","sd","cv"),
       export.where="~/metaseqr_test",
       restrict.cores=0.8,
       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","mm9")
       ),
       out.list=TRUE
)


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### code chunk number 9: head-2
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head(result[["data"]][["e14.5_vs_adult_8_weeks"]])


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### code chunk number 10: example-2
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library(metaseqR)
data("mm9.gene.data",package="metaseqR")
result <- metaseqr(
       counts=mm9.gene.counts,
       sample.list=sample.list.mm9,
       contrast=c("e14.5_vs_adult_8_weeks"),
       libsize.list=libsize.list.mm9,
       annotation="download",
       org="mm9",
       count.type="gene",
       when.apply.filter="prenorm",
       normalization="edaseq",
       statistics=c("deseq","edger"),
       meta.p="fisher",
       qc.plots=c(
             "mds","biodetection","countsbio","saturation","readnoise","filtered",
             "correl","pairwise","boxplot","gcbias","lengthbias","meandiff",
             "meanvar","rnacomp","deheatmap","volcano","biodist","venn"
       ),
       fig.format=c("png","pdf"),
       preset="medium.normal",
       export.where="~/metaseqr_test2",
       out.list=TRUE
)


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### code chunk number 11: example-3 (eval = FALSE)
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## library(metaseqR)
## data("mm9.gene.data",package="metaseqR")
## result <- metaseqr(
##        counts=mm9.gene.counts,
##        sample.list=sample.list.mm9,
##        contrast=c("e14.5_vs_adult_8_weeks"),
##        libsize.list=libsize.list.mm9,
##        annotation="download",
##        org="mm9",
##        count.type="gene",
##        normalization="edaseq",
##        statistics=c("deseq","edger"),
##        meta.p="fisher",
##        fig.format=c("png","pdf"),
##        preset="medium.normal",
##        out.list=TRUE
## )


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### code chunk number 12: example-4 (eval = FALSE)
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## # A full example pipeline with exon counts
## data("hg19.exon.data",package="metaseqR")
## metaseqr(
##        counts=hg19.exon.counts,
##        sample.list=sample.list.hg19,
##        contrast=c("normal_vs_paracancerous","normal_vs_cancerous",
##           "normal_vs_paracancerous_vs_cancerous"),
##        libsize.list=libsize.list.hg19,
##        id.col=4,
##        annotation="download",
##        org="hg19",
##        count.type="exon",
##        normalization="edaseq",
##        statistics="deseq",
##        pcut=0.05,
##        qc.plots=c(
##              "mds","biodetection","countsbio","saturation","rnacomp","pairwise",
##              "boxplot","gcbias","lengthbias","meandiff","meanvar","correl",
##              "deheatmap","volcano","biodist","filtered"
##        ),
##        fig.format=c("png","pdf"),
##        export.what=c("annotation","p.value","adj.p.value","fold.change","stats","counts"),
##        export.scale=c("natural","log2","log10","vst"),
##        export.values=c("raw","normalized"),
##        export.stats=c("mean","median","sd","mad","cv","rcv"),
##        restrict.cores=0.8,
##        gene.filters=list(
##              length=list(
##                     length=500
##              ),
##              avg.reads=list(
##                     average.per.bp=100,
##                     quantile=0.25
##              ),
##              expression=list(
##                     median=TRUE,
##                     mean=FALSE
##              ),
##              biotype=get.defaults("biotype.filter","hg19")
##        )
## )


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### code chunk number 13: example-5 (eval = FALSE)
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## # A full example pipeline with exon counts
## data("hg19.exon.data",package="metaseqR")
## metaseqr(
##        counts=hg19.exon.counts,
##        sample.list=sample.list.hg19,
##        contrast=c("normal_vs_paracancerous","normal_vs_cancerous",
##           "normal_vs_paracancerous_vs_cancerous"),
##        libsize.list=libsize.list.hg19,
##        id.col=4,
##        annotation="download",
##        org="hg19",
##        count.type="exon",
##        normalization="edaseq",
##        statistics="deseq",
##        preset="medium.normal",
##        restrict.cores=0.8
## )


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### code chunk number 14: example-6
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data("mm9.gene.data",package="metaseqR")
multic <- check.parallel(0.8)
weights <- estimate.aufc.weights(
    counts=as.matrix(mm9.gene.counts[,9:12]),
    normalization="edaseq",
    statistics=c("edger","limma"),
    nsim=1,N=10,ndeg=c(2,2),top=4,model.org="mm9",
    seed=42,multic=multic,libsize.gt=1e+5
)


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### code chunk number 15: head-3
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weights


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### code chunk number 16: help-2 (eval = FALSE)
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## help(stat.edgeR)


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### code chunk number 17: help-3 (eval = FALSE)
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## help(metaseqr)


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### code chunk number 18: session-info
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sessionInfo()