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 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
## [4] LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
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## time zone: Etc/UTC
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] rmarkdown_2.29
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## loaded via a namespace (and not attached):
## [1] digest_0.6.37 R6_2.5.1 fastmap_1.2.0 xfun_0.49 maketools_1.3.1
## [6] cachem_1.1.0 knitr_1.49 htmltools_0.5.8.1 buildtools_1.0.0 lifecycle_1.0.4
## [11] cli_3.6.3 sass_0.4.9 jquerylib_0.1.4 compiler_4.4.2 sys_3.4.3
## [16] tools_4.4.2 evaluate_1.0.1 bslib_0.8.0 yaml_2.3.10 jsonlite_1.8.9
## [21] rlang_1.1.4