Collapse technical replicates in a RangedSummarizedExperiment or DESeqDataSet

Description

Collapses the columns in object by summing within levels of a grouping factor groupby. The purpose of this function is to sum up read counts from technical replicates to create an object with a single column of read counts for each sample. Optionally renames the columns of returned object with the levels of the grouping factor. Note: this function is written very simply and can be easily altered to produce other behavior by examining the source code.

Usage

collapseReplicates(object, groupby, run, renameCols = TRUE)

Arguments

Value

the object with as many columns as levels in groupby. This object has assay/count data which is summed from the various columns which are grouped together, and the colData is subset using the first column for each group in groupby.

Examples

dds <- makeExampleDESeqDataSet(m=12)

# make data with two technical replicates for three samples
dds$sample <- factor(sample(paste0("sample",rep(1:9, c(2,1,1,2,1,1,2,1,1)))))
dds$run <- paste0("run",1:12)

ddsColl <- collapseReplicates(dds, dds$sample, dds$run)

# examine the colData and column names of the collapsed data
colData(ddsColl)
colnames(ddsColl)

# check that the sum of the counts for "sample1" is the same
# as the counts in the "sample1" column in ddsColl
matchFirstLevel <- dds$sample == levels(dds$sample)[1]
stopifnot(all(rowSums(counts(dds[,matchFirstLevel])) == counts(ddsColl[,1])))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(DESeq2)
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit


Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/DESeq2/collapseReplicates.Rd_%03d_medium.png", width=480, height=480)
> ### Name: collapseReplicates
> ### Title: Collapse technical replicates in a RangedSummarizedExperiment or
> ###   DESeqDataSet
> ### Aliases: collapseReplicates
> 
> ### ** Examples
> 
> 
> dds <- makeExampleDESeqDataSet(m=12)
> 
> # make data with two technical replicates for three samples
> dds$sample <- factor(sample(paste0("sample",rep(1:9, c(2,1,1,2,1,1,2,1,1)))))
> dds$run <- paste0("run",1:12)
> 
> ddsColl <- collapseReplicates(dds, dds$sample, dds$run)
> 
> # examine the colData and column names of the collapsed data
> colData(ddsColl)
DataFrame with 9 rows and 4 columns
        condition   sample         run runsCollapsed
         <factor> <factor> <character>   <character>
sample1         A  sample1        run1     run1,run2
sample2         B  sample2       run10         run10
sample3         B  sample3        run7          run7
sample4         B  sample4        run8     run8,run9
sample5         A  sample5        run4          run4
sample6         B  sample6       run11         run11
sample7         A  sample7        run5    run5,run12
sample8         A  sample8        run6          run6
sample9         A  sample9        run3          run3
> colnames(ddsColl)
[1] "sample1" "sample2" "sample3" "sample4" "sample5" "sample6" "sample7"
[8] "sample8" "sample9"
> 
> # check that the sum of the counts for "sample1" is the same
> # as the counts in the "sample1" column in ddsColl
> matchFirstLevel <- dds$sample == levels(dds$sample)[1]
> stopifnot(all(rowSums(counts(dds[,matchFirstLevel])) == counts(ddsColl[,1])))
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>