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Convert EdgeR results object to a data.frame

Source: R/edger-to-df.R
edger_to_df.Rd

Create a data.frame from an edgeR results object. This function calls edgeR::topTags() on the object and extracts the table data.frame with all features. This function returns all rows unsorted by default i.e. topTags(..., n=Inf, sort.by="none").

Usage

edger_to_df(x, ...)

Arguments

x

edgeR results object to be converted

...

Additional arguments passed to edgeR::topTags()

Value

data.frame

Examples

library(edgeR)
#> Loading required package: limma
library(coriell)

# create some fake data
x <- data.frame(
  ctl1 = rnbinom(1000, size = 0.4, prob = 1e-5),
  ctl2 = rnbinom(1000, size = 0.4, prob = 1e-5),
  trt1 = rnbinom(1000, size = 0.4, prob = 1e-5),
  trt2 = rnbinom(1000, size = 0.4, prob = 1e-5),
  row.names = paste0("gene", 1:1000)
)

# run edger pipeline
group <- factor(c(1, 1, 2, 2))
y <- DGEList(counts = x, group = group)
y <- calcNormFactors(y)
design <- model.matrix(~group)
y <- estimateDisp(y, design)

# To perform quasi-likelihood F-tests:
fit <- glmQLFit(y, design)
qlf <- glmQLFTest(fit, coef = 2)

# convert the results object to a dataframe -- do not filter the results
res_df <- edger_to_df(qlf)

head(res_df)
#>   feature_id      logFC    logCPM          F    PValue       FDR
#> 1      gene1  3.2976779  8.636724 1.62710813 0.2022516 0.7999905
#> 2      gene2  1.1300049 11.013724 0.25956733 0.6104738 0.9122581
#> 3      gene3  1.3662738 10.451157 0.35855331 0.5493786 0.9018168
#> 4      gene4 -0.6897648 10.032711 0.08902337 0.7654534 0.9313930
#> 5      gene5  0.5133503  7.443797 0.04442432 0.8330876 0.9481586
#> 6      gene6 -1.1184763  9.084475 0.21682650 0.6415194 0.9163448