significants {DEGreport} | R Documentation

## Method to get the significant genes

### Description

Function to get the features that are significant according to some thresholds from a DEGSet, DESeq2::DESeqResults and edgeR::topTags.

### Usage

    significants(object, padj = 0.05, fc = 0, direction = NULL, full = FALSE, ...)

    ## S4 method for signature 'DEGSet'
    significants(object, padj = 0.05, fc = 0, direction = NULL, full = FALSE, ...)

    ## S4 method for signature 'DESeqResults'
    significants(object, padj = 0.05, fc = 0, direction = NULL, full = FALSE, ...)

    ## S4 method for signature 'TopTags'
    significants(object, padj = 0.05, fc = 0, direction = NULL, full = FALSE, ...)

    ## S4 method for signature 'list'
    significants(
      object,
      padj = 0.05,
      fc = 0,
      direction = NULL,
      full = FALSE,
      newFDR = FALSE,
      ...
    )

### Arguments

`object` | DEGSet
`padj` | Cutoff for the FDR column.
`fc` | Cutoff for the log2FC column.
`direction` | Whether to take down/up/ignore. Valid arguments are down, up and NULL.
`full` | Whether to return full table or not.
`...` | Passed to deg. Default: value = NULL. Value can be 'raw', 'shrunken'.
`newFDR` | Whether to recalculate the FDR or not. See https://support.bioconductor.org/p/104059/#104072. Only used when a list is giving to the method.

### Value

a dplyr::tbl_df data frame. `gene` column has the feature name. In the case of using this method with the results from degComps, `log2FoldChange` has the higher foldChange from the comparisons, and `padj` has the padj associated to the previous column. Then, there is two columns for each comparison, one for the log2FoldChange and another for the padj.

### Author(s)

Lorena Pantano

### Examples

    library(DESeq2)
    library(dplyr)
    dds <- makeExampleDESeqDataSet(betaSD=1)
    colData(dds)[["treatment"]] <- sample(colData(dds)[["condition"]], 12)
      design(dds) <-  ~ condition + treatment
    dds <- DESeq(dds)
    res <- degComps(dds, contrast = list("treatment_B_vs_A",
                                         c("condition", "A", "B")))
    significants(res, full = TRUE) %>% head
    significants(res, full = TRUE, padj = 1) %>% head # all genes

[Package DEGreport version 1.26.0 Index]