Class containing all elements generated during differential expression analysis with DESeq2. This class is essentially a list with validity checks to ensure DESeqTransform and DESeqResults correspond to the DESeqDataSet.

DESeqAnalysis(data, transform, results, lfcShrink = NULL)

Arguments

data

DESeqDataSet.

transform

DESeqTransform. DESeq2::varianceStabilizingTransformation() recommended by default.

results

list or single DESeqResults. One or more unshrunken DESeqResults. Assign the DESeq2::results() return here.

lfcShrink

list, single DESeqResults, or NULL. Optional. One or more shrunken DESeqResults. Assign the DESeq2::lfcShrink() return here.

Value

DESeqAnalysis.

Note

Updated 2019-08-20.

Examples

data <- DESeq2::makeExampleDESeqDataSet() rowRanges <- emptyRanges(names = rownames(data)) mcols(rowRanges)[["geneID"]] <- paste0("id", seq_len(length(rowRanges))) mcols(rowRanges)[["geneName"]] <- paste0("name", seq_len(length(rowRanges))) rowRanges(data) <- rowRanges data <- DESeq2::DESeq(data)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
class(data)
#> [1] "DESeqDataSet" #> attr(,"package") #> [1] "DESeq2"
transform <- DESeq2::varianceStabilizingTransformation(data) class(transform)
#> [1] "DESeqTransform" #> attr(,"package") #> [1] "DESeq2"
resultsNames(data)
#> [1] "Intercept" "condition_B_vs_A"
name <- resultsNames(data)[[2L]] results <- DESeq2::results(data, name = name) class(results)
#> [1] "DESeqResults" #> attr(,"package") #> [1] "DESeq2"
lfcShrink <- DESeq2::lfcShrink(dds = data, res = results, coef = 2L)
#> using 'apeglm' for LFC shrinkage. If used in published research, please cite: #> Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for #> sequence count data: removing the noise and preserving large differences. #> Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
results <- list(results) names(results) <- name lfcShrink <- list(lfcShrink) names(lfcShrink) <- name object <- DESeqAnalysis( data = data, transform = transform, results = results, lfcShrink = lfcShrink ) print(object)
#> DESeqAnalysis 0.3.9; DESeq2 1.28.1 #> data: #> dim: 1000 12 #> metadata(1): version #> assays(4): counts mu H cooks #> rownames(1000): gene1 gene2 ... gene999 gene1000 #> rowData names(24): geneID geneName ... deviance maxCooks #> colnames(12): sample1 sample2 ... sample11 sample12 #> colData names(2): condition sizeFactor #> transformType: varianceStabilizingTransformation #> resultsNames: condition_B_vs_A #> alphaThreshold: 0.1 #> lfcShrinkType: apeglm