In general, I recommend against interpreting the fraction of variance explained by residuals. This fraction is driven by:
If you have additional variables that explain variation in measured gene expression, you should include them in order to avoid confounding with your variable of interest. But a particular residual fraction is not ‘good’ or ‘bad’ and is not a good metric of determining whether more variables should be included.
See GitHub page for up-to-date responses to users’ questions.
## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.37 R6_2.6.1 fastmap_1.2.0 xfun_0.52 cachem_1.1.0
## [6] knitr_1.50 htmltools_0.5.8.1 rmarkdown_2.29 lifecycle_1.0.4 cli_3.6.4
## [11] sass_0.4.10 jquerylib_0.1.4 compiler_4.5.0 tools_4.5.0 evaluate_1.0.3
## [16] bslib_0.9.0 yaml_2.3.10 rlang_1.1.6 jsonlite_2.0.0