DESeq2 Differential Expression Analysis — h_diff_expr

This helper functions performs the differential expression analysis with DESeq2::DESeq() for a given AnyHermesData input and design matrix.

Usage

h_diff_expr_deseq2(object, design, ...)

Arguments

object: (HermesData)
input.
design: (matrix)
design matrix.
...: additional arguments internally passed to DESeq2::DESeq() (fitType, sfType, minReplicatesForReplace, useT, minmu).

Value

A data frame with columns log2_fc (estimated log2 fold change), stat (Wald statistic), p_val (raw p-value), adj_p_pval (Benjamini-Hochberg adjusted p-value).

References

DESeq2_packagehermes

Examples

object <- hermes_data

# Create the design matrix corresponding to the factor of interest.
design <- model.matrix(~SEX, colData(object))

# Then perform the `DESeq2` differential expression analysis.
result <- h_diff_expr_deseq2(object, design)
head(result)
#>                 log2_fc      stat        p_val    adj_p_val
#> GeneID:9834    5.859486  5.440035 5.327024e-08 0.0001852739
#> GeneID:221188  3.450713  4.805489 1.543734e-06 0.0026845539
#> GeneID:151242 -4.316426 -4.707534 2.507316e-06 0.0029068155
#> GeneID:9002    2.324969  4.390859 1.129039e-05 0.0097580091
#> GeneID:64344  -2.880611 -4.343419 1.402819e-05 0.0097580091
#> GeneID:4359    2.914210  4.206676 2.591542e-05 0.0113396717

# Change of the `fitType` can be required in some cases.
result2 <- h_diff_expr_deseq2(object, design, fitType = "local")
head(result2)
#>                  log2_fc      stat        p_val    adj_p_val
#> GeneID:9834    5.8595097  5.436419 5.436203e-08 0.0001605311
#> GeneID:221188  3.4507368  4.796226 1.616827e-06 0.0023872456
#> GeneID:151242 -4.3163441 -4.693049 2.691627e-06 0.0026494579
#> GeneID:9002    2.3249808  4.379230 1.190996e-05 0.0073970422
#> GeneID:64344  -2.8805945 -4.368250 1.252462e-05 0.0073970422
#> GeneID:51575  -0.7889652 -4.255997 2.081193e-05 0.0076822022