DEGSet

S4 class to store data from differentially expression analysis. It should be compatible with different package and stores the information in a way the methods will work with all of them.

DEGSet(resList, default)

DEGSet(resList, default)

as.DEGSet(object, ...)

# S4 method for TopTags
as.DEGSet(object, default = "raw", extras = NULL)

# S4 method for data.frame
as.DEGSet(object, contrast, default = "raw",
  extras = NULL)

# S4 method for DESeqResults
as.DEGSet(object, default = "shrunken",
  extras = NULL)

Arguments

resList	List with results as elements containing log2FoldChange, pvalues and padj as column. Rownames should be feature names. Elements should have names.
default	The name of the element to use by default.
object	Different objects to be transformed to DEGSet when using `as.DEGSet`.
...	Optional parameters of the generic.
extras	List of extra tables related to the same comparison when using `as.DEGSet`.
contrast	To name the comparison when using `as.DEGSet`.

Details

For now supporting only DESeq2::results() output. Use constructor degComps() to create the object.

The list will contain one element for each comparison done. Each element has the following structure:

DEG table
Optional table with shrunk Fold Change when it has been done.

To access the raw table use deg(dgs, "raw"), to access the shrunken table use deg(dgs, "shrunken") or just deg(dgs).

Examples

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, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
#>     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
#>     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
#>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#>     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
#>     union, unique, unsplit, which, which.max, which.min
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:base’:
#> 
#>     expand.grid
#> 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")'.
#> Loading required package: DelayedArray
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘matrixStats’
#> The following objects are masked from ‘package:Biobase’:
#> 
#>     anyMissing, rowMedians
#> Loading required package: BiocParallel
#> 
#> Attaching package: ‘DelayedArray’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
#> The following objects are masked from ‘package:base’:
#> 
#>     aperm, apply, rowsum
library(edgeR)
#> Loading required package: limma
#> 
#> Attaching package: ‘limma’
#> The following object is masked from ‘package:DESeq2’:
#> 
#>     plotMA
#> The following object is masked from ‘package:BiocGenerics’:
#> 
#>     plotMA
library(limma)
dds <- makeExampleDESeqDataSet(betaSD = 1)
colData(dds)[["treatment"]] <- sample(colData(dds)[["condition"]], 12)
design(dds) <-  ~ condition + treatment
dds <- DESeq(dds)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res <- degComps(dds, combs = c("condition"))
#> Doing 1 element(s).
#> Doing results() for each element.
#> Doing lcfSrink() for each element.
#> using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
#> 
#> Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
#> See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
#> Reference: https://doi.org/10.1093/bioinformatics/bty895
deg(res)
#> log2 fold change (MAP): condition B vs A 
#> Wald test p-value: condition B vs A 
#> DataFrame with 1000 rows and 6 columns
#>                  baseMean     log2FoldChange             lfcSE
#>                 <numeric>          <numeric>         <numeric>
#> gene202  595.738080502665   2.84842810352625 0.266347713336691
#> gene710  117.423055944441   2.27457157814062 0.269412722270367
#> gene365  62.4434899710059   2.58422310739862 0.312590047837112
#> gene92   220.780204589055   2.34371133129609 0.293935357285173
#> gene364  504.522116500683   2.02714357936619 0.261124625693044
#> ...                   ...                ...               ...
#> gene817  1.01863690444656  0.449433023322897 0.445454565205662
#> gene843 0.337116176782868 -0.213542721980008 0.335419175582519
#> gene896 0.641791262357825 -0.193113193147619 0.453528848117175
#> gene929 0.400097105619871  0.148754379342596 0.336592062169539
#> gene986  1.03912504596143  -0.08078789533133 0.501091699324092
#>                       stat               pvalue                 padj
#>                  <numeric>            <numeric>            <numeric>
#> gene202   10.4242414625226 1.92186681865069e-25 1.84307027908601e-22
#> gene710   8.19578166036082 2.48969903778264e-16 1.19381068861678e-13
#> gene365   8.02865482517521 9.85471916802617e-16 3.15022522737903e-13
#> gene92     7.7626720152767 8.31584691463868e-15 1.99372429778462e-12
#> gene364   7.51756890822073 5.58041485417993e-14 1.07032356903171e-11
#> ...                    ...                  ...                  ...
#> gene817   1.05361747680227    0.292058084434074                   NA
#> gene843 -0.406932816121088    0.684057332932902                   NA
#> gene896 -0.612499137852159      0.5402075682426                   NA
#> gene929  0.414219583248068    0.678713301164469                   NA
#> gene986 0.0436335995171099    0.965196468651691                   NA
#> <numeric>
deg(res, tidy = "tibble")
#> # A tibble: 1,000 x 7
#>    gene    baseMean log2FoldChange lfcSE  stat   pvalue     padj
#>    <chr>      <dbl>          <dbl> <dbl> <dbl>    <dbl>    <dbl>
#>  1 gene202    596.            2.85 0.266 10.4  1.92e-25 1.84e-22
#>  2 gene710    117.            2.27 0.269  8.20 2.49e-16 1.19e-13
#>  3 gene365     62.4           2.58 0.313  8.03 9.85e-16 3.15e-13
#>  4 gene92     221.            2.34 0.294  7.76 8.32e-15 1.99e-12
#>  5 gene364    505.            2.03 0.261  7.52 5.58e-14 1.07e-11
#>  6 gene968     69.4           2.62 0.345  7.11 1.17e-12 1.87e-10
#>  7 gene836     49.2           2.41 0.335  7.05 1.78e-12 2.43e-10
#>  8 gene77      32.5          -2.31 0.334 -6.75 1.50e-11 1.80e- 9
#>  9 gene342    484.            1.45 0.225  6.50 7.90e-11 8.42e- 9
#> 10 gene880     27.5           2.80 0.402  6.48 9.01e-11 8.64e- 9
#> # … with 990 more rows
# From edgeR
dge <- DGEList(counts=counts(dds), group=colData(dds)[["treatment"]])
dge <- estimateCommonDisp(dge)
res <- as.DEGSet(topTags(exactTest(dge)))
# From limma
v <- voom(counts(dds), model.matrix(~treatment, colData(dds)), plot=FALSE)
fit <- lmFit(v)
fit <- eBayes(fit, robust=TRUE)
res <- as.DEGSet(topTable(fit, n = "Inf"), "A_vs_B")
#> Removing intercept from test coefficients

Arguments

Details

Examples

Contents

Author