Results tables

resultsTables(object, ...)

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

# S4 method for DESeqAnalysis
resultsTables(object, results,
  lfcShrink = TRUE, rowData = TRUE, counts = TRUE,
  return = c("tbl_df", "DataFrameList"))

Arguments

object

Object.

return

character(1). Type of data frame to return in the list. Uses match.arg(). Note that DataFrame option will return with row names, whereas tbl_df option will return with "rowname" column.

results

character(1) or integer(1). Name or position of DESeqResults.

lfcShrink

logical(1). Use shrunken log2 fold change (LFC) values.

rowData

logical(1). Join the row annotations.

counts

logical(1). Join the size-factor adjusted normalized counts.

...

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

Do not apply post hoc log fold change cutoffs.

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: Bi-directional 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, results = 1L)
#> condition_B_vs_A (shrunken LFC)
#> Returning with the sample names unmodified.
#> Joining row annotations.
#> Joining size factor adjusted normalized counts.
#> 171 differentially expressed genes detected.
#> 84 upregulated genes detected.
#> 87 downregulated genes detected.
#> $all #> # A tibble: 500 x 22 #> rowname baseMean log2FoldChange lfcSE pvalue padj geneID geneName #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct> #> 1 gene001 148. 1.39 0.404 1.00e-4 1.27e-3 ENSG0… TSPAN6 #> 2 gene002 34.0 -0.178 0.368 5.64e-1 7.24e-1 ENSG0… TNMD #> 3 gene003 10.6 -1.76 0.684 9.93e-4 7.71e-3 ENSG0… DPM1 #> 4 gene004 81.2 -1.94 0.433 7.80e-7 2.77e-5 ENSG0… SCYL3 #> 5 gene005 11.6 0.195 0.479 5.74e-1 7.34e-1 ENSG0… C1orf112 #> 6 gene006 252. -0.0297 0.265 9.02e-1 9.40e-1 ENSG0… FGR #> 7 gene007 81.1 0.0880 0.293 7.41e-1 8.48e-1 ENSG0… CFH #> 8 gene008 64.1 0.529 0.432 1.25e-1 2.64e-1 ENSG0… FUCA2 #> 9 gene009 5.12 0.186 0.555 5.83e-1 7.41e-1 ENSG0… GCLC #> 10 gene010 0.728 -0.0237 0.658 8.88e-1 9.37e-1 ENSG0… NFYA #> # … with 490 more rows, and 14 more variables: geneBiotype <fct>, #> # broadClass <fct>, sample01 <dbl>, sample02 <dbl>, sample03 <dbl>, #> # sample04 <dbl>, sample05 <dbl>, sample06 <dbl>, sample07 <dbl>, #> # sample08 <dbl>, sample09 <dbl>, sample10 <dbl>, sample11 <dbl>, #> # sample12 <dbl> #> #> $up #> # A tibble: 84 x 22 #> rowname baseMean log2FoldChange lfcSE pvalue padj geneID geneName #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct> #> 1 gene095 123. 2.36 0.367 1.12e-11 1.86e-9 ENSG0… MPO #> 2 gene334 72.4 2.85 0.458 3.68e-11 4.57e-9 ENSG0… RWDD2A #> 3 gene323 195. 1.99 0.321 5.67e-11 5.64e-9 ENSG0… UBR7 #> 4 gene494 113. 1.76 0.284 7.91e-11 6.55e-9 ENSG0… EIPR1 #> 5 gene391 901. 1.62 0.276 7.30e-10 4.54e-8 ENSG0… PRDM11 #> 6 gene145 93.3 2.16 0.410 1.36e- 8 7.53e-7 ENSG0… USH1C #> 7 gene078 87.0 1.72 0.334 3.50e- 8 1.74e-6 ENSG0… HOXA11 #> 8 gene403 47.9 1.77 0.364 1.55e- 7 6.60e-6 ENSG0… RUNX3 #> 9 gene248 153. 1.31 0.271 2.47e- 7 9.44e-6 ENSG0… METTL13 #> 10 gene489 155. 1.58 0.367 2.44e- 6 7.59e-5 ENSG0… ARHGAP31 #> # … with 74 more rows, and 14 more variables: geneBiotype <fct>, #> # broadClass <fct>, sample01 <dbl>, sample02 <dbl>, sample03 <dbl>, #> # sample04 <dbl>, sample05 <dbl>, sample06 <dbl>, sample07 <dbl>, #> # sample08 <dbl>, sample09 <dbl>, sample10 <dbl>, sample11 <dbl>, #> # sample12 <dbl> #> #> $down #> # A tibble: 87 x 22 #> rowname baseMean log2FoldChange lfcSE pvalue padj geneID geneName #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct> #> 1 gene427 44.0 -2.90 0.359 6.44e-17 3.20e-14 ENSG0… ERP44 #> 2 gene419 27.8 -2.99 0.424 1.51e-13 3.75e-11 ENSG0… SLC45A4 #> 3 gene352 25.7 -2.86 0.496 6.38e-10 4.53e- 8 ENSG0… SLC30A9 #> 4 gene497 48.3 -2.17 0.455 1.59e- 7 6.60e- 6 ENSG0… ALG1 #> 5 gene004 81.2 -1.94 0.433 7.80e- 7 2.77e- 5 ENSG0… SCYL3 #> 6 gene285 116. -1.35 0.307 1.85e- 6 6.13e- 5 ENSG0… ABHD5 #> 7 gene023 67.0 -1.25 0.291 3.75e- 6 1.00e- 4 ENSG0… LAP3 #> 8 gene148 238. -1.12 0.258 3.83e- 6 1.00e- 4 ENSG0… TBXA2R #> 9 gene473 12.8 -2.56 0.648 5.90e- 6 1.33e- 4 ENSG0… SNX1 #> 10 gene412 152. -1.18 0.287 8.94e- 6 1.93e- 4 ENSG0… AQR #> # … with 77 more rows, and 14 more variables: geneBiotype <fct>, #> # broadClass <fct>, sample01 <dbl>, sample02 <dbl>, sample03 <dbl>, #> # sample04 <dbl>, sample05 <dbl>, sample06 <dbl>, sample07 <dbl>, #> # sample08 <dbl>, sample09 <dbl>, sample10 <dbl>, sample11 <dbl>, #> # sample12 <dbl> #> #> $both #> # A tibble: 171 x 22 #> rowname baseMean log2FoldChange lfcSE pvalue padj geneID geneName #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct> #> 1 gene427 44.0 -2.90 0.359 6.44e-17 3.20e-14 ENSG0… ERP44 #> 2 gene419 27.8 -2.99 0.424 1.51e-13 3.75e-11 ENSG0… SLC45A4 #> 3 gene095 123. 2.36 0.367 1.12e-11 1.86e- 9 ENSG0… MPO #> 4 gene334 72.4 2.85 0.458 3.68e-11 4.57e- 9 ENSG0… RWDD2A #> 5 gene323 195. 1.99 0.321 5.67e-11 5.64e- 9 ENSG0… UBR7 #> 6 gene494 113. 1.76 0.284 7.91e-11 6.55e- 9 ENSG0… EIPR1 #> 7 gene352 25.7 -2.86 0.496 6.38e-10 4.53e- 8 ENSG0… SLC30A9 #> 8 gene391 901. 1.62 0.276 7.30e-10 4.54e- 8 ENSG0… PRDM11 #> 9 gene145 93.3 2.16 0.410 1.36e- 8 7.53e- 7 ENSG0… USH1C #> 10 gene078 87.0 1.72 0.334 3.50e- 8 1.74e- 6 ENSG0… HOXA11 #> # … with 161 more rows, and 14 more variables: geneBiotype <fct>, #> # broadClass <fct>, sample01 <dbl>, sample02 <dbl>, sample03 <dbl>, #> # sample04 <dbl>, sample05 <dbl>, sample06 <dbl>, sample07 <dbl>, #> # sample08 <dbl>, sample09 <dbl>, sample10 <dbl>, sample11 <dbl>, #> # sample12 <dbl> #>