Filter genes from a TCC-class object

Description

This function takes a TCC object and returns a new TCC object without genes having low count tags across samples. The threshold is configurable with low.count parameter.

Usage

filterLowCountGenes(tcc, low.count = 0)

Arguments

Value

TCC-class object consisting of genes whose sum of the counts across samples is equal or higher than the low.count value.

Examples

# Filtering genes with zero counts across samples (default) from
# a hypothetical count dataset that originally has 1,000 genes.
data(hypoData)
group <- c(1, 1, 1, 2, 2, 2)
tcc <- new("TCC", hypoData, group)
dim(tcc$count)
tcc <- filterLowCountGenes(tcc)
dim(tcc$count)

# Filtering genes with 10 counts across samples from hypoData.
data(hypoData)
group <- c(1, 1, 1, 2, 2, 2)
tcc <- new("TCC", hypoData, group)
dim(tcc$count)
tcc <- filterLowCountGenes(tcc, low.count = 10)
dim(tcc$count)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(TCC)
Loading required package: DESeq
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

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")'.

Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: lattice
    Welcome to 'DESeq'. For improved performance, usability and
    functionality, please consider migrating to 'DESeq2'.
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4

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

Attaching package: 'DESeq2'

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

    estimateSizeFactorsForMatrix, getVarianceStabilizedData,
    varianceStabilizingTransformation

Loading required package: edgeR
Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:DESeq2':

    plotMA

The following object is masked from 'package:DESeq':

    plotMA

The following object is masked from 'package:BiocGenerics':

    plotMA

Loading required package: baySeq
Loading required package: abind
Loading required package: perm
Loading required package: ROC

Attaching package: 'TCC'

The following object is masked from 'package:edgeR':

    calcNormFactors

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/TCC/filterLowCountGenes.Rd_%03d_medium.png", width=480, height=480)
> ### Name: filterLowCountGenes
> ### Title: Filter genes from a TCC-class object
> ### Aliases: filterLowCountGenes
> ### Keywords: methods
> 
> ### ** Examples
> 
> # Filtering genes with zero counts across samples (default) from
> # a hypothetical count dataset that originally has 1,000 genes.
> data(hypoData)
> group <- c(1, 1, 1, 2, 2, 2)
> tcc <- new("TCC", hypoData, group)
> dim(tcc$count)
[1] 1000    6
> tcc <- filterLowCountGenes(tcc)
> dim(tcc$count)
[1] 996   6
> 
> # Filtering genes with 10 counts across samples from hypoData.
> data(hypoData)
> group <- c(1, 1, 1, 2, 2, 2)
> tcc <- new("TCC", hypoData, group)
> dim(tcc$count)
[1] 1000    6
> tcc <- filterLowCountGenes(tcc, low.count = 10)
> dim(tcc$count)
[1] 837   6
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>