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 Patched (2023-11-01 r85457)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.7.1
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## time zone: America/New_York
## tzcode source: internal
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## loaded via a namespace (and not attached):
## [1] digest_0.6.34 R6_2.5.1 fastmap_1.1.1 xfun_0.42 cachem_1.0.8 knitr_1.45
## [7] htmltools_0.5.7 rmarkdown_2.25 lifecycle_1.0.4 cli_3.6.2 sass_0.4.8 jquerylib_0.1.4
## [13] compiler_4.3.2 tools_4.3.2 evaluate_0.23 bslib_0.6.1 yaml_2.3.8 rlang_1.1.3
## [19] jsonlite_1.8.8