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 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.6.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## 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_0.6.33 R6_2.5.1 fastmap_1.1.1 xfun_0.41 cachem_1.0.8 knitr_1.45
## [7] htmltools_0.5.7 rmarkdown_2.25 cli_3.6.1 sass_0.4.7 jquerylib_0.1.4 compiler_4.3.2
## [13] tools_4.3.2 evaluate_0.23 bslib_0.5.1 yaml_2.3.7 rlang_1.1.1 jsonlite_1.8.7