Results tables

resultsTables(object, ...)

# S4 method for DESeqAnalysis
resultsTables(
  object,
  i,
  alphaThreshold = NULL,
  baseMeanThreshold = NULL,
  lfcThreshold = NULL,
  extra = TRUE,
  return = c("tbl_df", "DataFrameList")
)

# S4 method for DESeqResults
resultsTables(
  object,
  alphaThreshold = NULL,
  baseMeanThreshold = NULL,
  lfcThreshold = NULL,
  return = c("tbl_df", "DataFrameList")
)

Arguments

object

Object.

i

character(1) or integer(1). DESeqResults contrast name or position in results slot.

alphaThreshold

numeric(1) or NULL. Adjusted P value ("alpha") cutoff. If left NULL, will use the cutoff defined in the object.

baseMeanThreshold

numeric(1) or NULL. Base mean (i.e. average expression across all samples) threshold. If left NULL, will use the cutoff defined in the object. Applies in general to DESeq2 RNA-seq differential expression output.

lfcThreshold

numeric(1) or NULL. Log (base 2) fold change ratio cutoff threshold. If left NULL, will use the cutoff defined in the object.

extra

logical(1). Include row data (i.e. gene metadata) and normalized counts from the internal DESeqDataSet.

return

character(1). Type of data frame to return as a list. Uses match.arg().

  • DataFrameList: Returns DataFrameList with row names.

  • tbl_df: Returns list of tbl_df containing "rowname" column.

...

Additional arguments.

Value

list. Named list containing subsets of DESeqResults.

Details

Generate tables summarizing the differential expression, with subsets for differentially expressed genes (DEGs). DEG tables (i.e. everything except the all table), are arranged by adjusted P value.

Note

Updated 2021-06-28.

It is generally recommended to not apply post hoc log fold change cutoffs. If a specific effect size is desired, instead run DESeq2::results() using the lfcThreshold parameter. Refer to the DESeq2 documentation and vignette for details.

Tables

  • all: All genes, including genes without an adjusted P value. This table is unmodified, and the rows have not been re-arranged or subset. It is suitable for gene set enrichment analysis (GSEA).

  • up: Upregulated genes.

  • down: Downregulated genes.

  • both: Bidirectional DEGs (up- and down-regulated). This table can be used for overrepresentation testing but should NOT be used for GSEA.

Examples

data(deseq) ## DESeqAnalysis ==== x <- resultsTables(deseq, i = 1L)
#> → condition_B_vs_A (shrunken LFC)
#> 38 differentially expressed genes (alpha < 0.01)
#> 24 upregulated genes (alpha < 0.01)
#> 14 downregulated genes (alpha < 0.01)
names(x)
#> [1] "all" "up" "down" "both"