Filter and plot DEG results

Description

Filters and plots DEG results for a given set of sample comparisons. The gene idenifiers of all (i) Up_or_Down, (ii) Up and (iii) Down regulated genes are stored as separate list components, while the corresponding summary statistics, stored in a fourth list component, is plotted in form of a stacked bar plot.

Usage

filterDEGs(degDF, filter, plot = TRUE)

Arguments

Details

Currently, there is no community standard available how to calculate fold changes (here logFC) of genomic ranges, such as gene or feature ranges, to unambiguously refer to them as features with increased or decreased read abundandce; or in case of gene expression experiments to up or down regulated genes, respectively. To be consistent within systemPipeR, the corresponding functions, such as filterDEGs, use here the following definition. Genomic ranges with positive logFC values are classified as up and those with negative logFC values as down. This means if a comparison among two samples a and b is specified in the corresponding targets file as a-b then the feature with a positive logFC has a higher _normalized_ read count value in sample a than in sample b, and vice versa. To inverse this assignment, users want to change the specification of their chosen sample comparison(s) in the targets file accordingly, e.g. change a-b to b-a. Alternatively, one can swap the column order of the matrix assigned to the cmp argument of the run_edgeR or run_DESeq2 functions. Users should also be aware that for logFC values close to zero (noise range), the direction of the fold change (sign of logFC) can be very sensitive to minor differences in the normalization method, while this assignment is much more robust for more pronounced changes or higher absolute logFC values.

Value

Returns list with four components

Author(s)

Thomas Girke

See Also

run_edgeR

Examples

targetspath <- system.file("extdata", "targets.txt", package="systemPipeR")
targets <- read.delim(targetspath, comment="#")
cmp <- readComp(file=targetspath, format="matrix", delim="-")
countfile <- system.file("extdata", "countDFeByg.xls", package="systemPipeR")
countDF <- read.delim(countfile, row.names=1)
edgeDF <- run_edgeR(countDF=countDF, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="")
pval <- edgeDF[, grep("_FDR$", colnames(edgeDF)), drop=FALSE]
fold <- edgeDF[, grep("_logFC$", colnames(edgeDF)), drop=FALSE]
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=10))
names(DEG_list)
DEG_list$Summary

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> library(systemPipeR)
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
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

Loading required package: S4Vectors

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: Biostrings
Loading required package: XVector
Loading required package: ShortRead
Loading required package: BiocParallel
Loading required package: GenomicAlignments
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/systemPipeR/filterDEGs.Rd_%03d_medium.png", width=480, height=480)
> ### Name: filterDEGs
> ### Title: Filter and plot DEG results
> ### Aliases: filterDEGs
> ### Keywords: utilities
> 
> ### ** Examples
> 
> targetspath <- system.file("extdata", "targets.txt", package="systemPipeR")
> targets <- read.delim(targetspath, comment="#")
> cmp <- readComp(file=targetspath, format="matrix", delim="-")
> countfile <- system.file("extdata", "countDFeByg.xls", package="systemPipeR")
> countDF <- read.delim(countfile, row.names=1)
> edgeDF <- run_edgeR(countDF=countDF, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="")
Disp = 0.20653 , BCV = 0.4545 
> pval <- edgeDF[, grep("_FDR$", colnames(edgeDF)), drop=FALSE]
> fold <- edgeDF[, grep("_logFC$", colnames(edgeDF)), drop=FALSE]
> DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=10))
> names(DEG_list)
[1] "UporDown" "Up"       "Down"     "Summary" 
> DEG_list$Summary
        Comparisons Counts_Up_or_Down Counts_Up Counts_Down
M1-A1         M1-A1                 0         0           0
M1-V1         M1-V1                 1         1           0
A1-V1         A1-V1                 1         1           0
M6-A6         M6-A6                 0         0           0
M6-V6         M6-V6                 1         0           1
A6-V6         A6-V6                 2         1           1
M12-A12     M12-A12                 4         2           2
M12-V12     M12-V12                 2         0           2
A12-V12     A12-V12                 1         0           1
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>