Method to get the significant genes

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

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

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

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

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

# S4 method for 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.

Examples

library(DESeq2)
library(dplyr)
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, contrast = list("treatment_B_vs_A",
                                     c("condition", "A", "B")))
#> Doing 2 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
#> 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
significants(res, full = TRUE) %>% head
#> # A tibble: 6 x 5
#>   gene    log2FoldChange      padj log2FoldChange_condition… padj_condition_A_v…
#>   <chr>            <dbl>     <dbl>                     <dbl>               <dbl>
#> 1 gene102          0.942   3.93e-2                     0.942          0.0393    
#> 2 gene108         -0.953   2.99e-2                    -0.953          0.0299    
#> 3 gene116         -1.31    2.59e-3                    -1.31           0.00259   
#> 4 gene119         -2.03    1.18e-6                    -2.03           0.00000118
#> 5 gene120          1.74    1.33e-5                     1.74           0.0000133 
#> 6 gene122          1.09    3.22e-2                     1.09           0.0322    
significants(res, full = TRUE, padj = 1) %>% head # all genes
#> # A tibble: 6 x 7
#>   gene  log2FoldChange   padj log2FoldChange_… log2FoldChange_… padj_condition_…
#>   <chr>          <dbl>  <dbl>            <dbl>            <dbl>            <dbl>
#> 1 gene1        -0.113  0.952           -0.788           -0.113            0.388 
#> 2 gene…         1.62   0.0931           1.62            -0.0855           0.0931
#> 3 gene…        -0.310  0.479           -0.310           -0.339            0.479 
#> 4 gene…         0.645  0.448            0.645           -0.528            0.448 
#> 5 gene…         0.0479 0.914            0.0479          -0.550            0.914 
#> 6 gene…         0.942  0.0393           0.942           -0.258            0.0393
#> # … with 1 more variable: padj_treatment_B_vs_A <dbl>