Entering edit mode
Hello, I am attempting to use limma's removeBatchEffect for visualization purposes (heatmat & PCA) while fitting non-linear models (splines) to my expression data in DESeq2. Given that my design is balanced, would this approach work within the framework of this function? The results _looks_ correct, but I want to be sure as I've never seen it used like that.
My code:
mat <- assay(vsd)
mm <- model.matrix(~ ns(age_scaled, df = 3), colData(vsd))
mat <- limma::removeBatchEffect(mat, batch = vsd$genotype, design = mm)
assay(vsd) <- mat
Thanks a lot in advance.
sessionInfo( )
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] splines stats4 stats graphics grDevices utils datasets methods
[9] base
other attached packages:
[1] edgeR_3.42.4 limma_3.56.2
[3] ReactomePA_1.44.0 DOSE_3.26.2
[5] enrichplot_1.20.3 clusterProfiler_4.8.3
[7] shiny_1.8.0 xlsx_0.6.5
[9] readxl_1.4.3 EnhancedVolcano_1.18.0
[11] ggrepel_0.9.3 RColorBrewer_1.1-3
[13] pheatmap_1.0.12 DESeq2_1.40.2
[15] SummarizedExperiment_1.30.2 Biobase_2.60.0
[17] MatrixGenerics_1.12.3 matrixStats_1.0.0
[19] GenomicRanges_1.52.0 GenomeInfoDb_1.36.4
[21] IRanges_2.34.1 S4Vectors_0.38.1
[23] BiocGenerics_0.46.0 lubridate_1.9.3
[25] forcats_1.0.0 stringr_1.5.1
[27] dplyr_1.1.2 purrr_1.0.2
[29] readr_2.1.4 tidyr_1.3.0
[31] tibble_3.2.1 ggplot2_3.4.4
[33] tidyverse_2.0.0 BiocManager_1.30.22
loaded via a namespace (and not attached):
[1] rstudioapi_0.15.0 jsonlite_1.8.7 magrittr_2.0.3
[4] farver_2.1.1 rmarkdown_2.25 fs_1.6.3
[7] zlibbioc_1.46.0 vctrs_0.6.3 memoise_2.0.1
[10] RCurl_1.98-1.12 ggtree_3.8.2 htmltools_0.5.7
[13] S4Arrays_1.0.5 cellranger_1.1.0 gridGraphics_0.5-1
[16] plyr_1.8.9 cachem_1.0.8 igraph_1.5.1
[19] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
[22] Matrix_1.6-3 R6_2.5.1 fastmap_1.1.1
[25] gson_0.1.0 GenomeInfoDbData_1.2.10 digest_0.6.33
[28] aplot_0.2.2 colorspace_2.1-0 patchwork_1.1.3
[31] AnnotationDbi_1.62.2 RSQLite_2.3.1 timechange_0.2.0
[34] fansi_1.0.4 httr_1.4.7 polyclip_1.10-6
[37] abind_1.4-5 compiler_4.3.1 bit64_4.0.5
[40] withr_2.5.2 downloader_0.4 graphite_1.46.0
[43] BiocParallel_1.34.2 viridis_0.6.4 DBI_1.1.3
[46] ggforce_0.4.1 MASS_7.3-60 rappdirs_0.3.3
[49] DelayedArray_0.26.7 HDO.db_0.99.1 tools_4.3.1
[52] ape_5.7-1 scatterpie_0.2.1 httpuv_1.6.11
[55] glue_1.6.2 promises_1.2.1 nlme_3.1-162
[58] GOSemSim_2.26.1 grid_4.3.1 shadowtext_0.1.2
[61] reshape2_1.4.4 fgsea_1.26.0 generics_0.1.3
[64] gtable_0.3.4 tzdb_0.4.0 hms_1.1.3
[67] data.table_1.14.8 tidygraph_1.2.3 utf8_1.2.3
[70] XVector_0.40.0 pillar_1.9.0 yulab.utils_0.1.0
[73] later_1.3.1 rJava_1.0-6 tweenr_2.0.2
[76] treeio_1.24.3 lattice_0.21-8 bit_4.0.5
[79] tidyselect_1.2.0 GO.db_3.17.0 locfit_1.5-9.8
[82] Biostrings_2.68.1 reactome.db_1.84.0 knitr_1.45
[85] gridExtra_2.3 xfun_0.40 graphlayouts_1.0.1
[88] stringi_1.7.12 lazyeval_0.2.2 ggfun_0.1.3
[91] yaml_2.3.7 xlsxjars_0.6.1 evaluate_0.23
[94] codetools_0.2-19 ggraph_2.1.0 qvalue_2.32.0
[97] graph_1.78.0 ggplotify_0.1.2 cli_3.6.1
[100] xtable_1.8-4 munsell_0.5.0 Rcpp_1.0.11
[103] png_0.1-8 parallel_4.3.1 ellipsis_0.3.2
[106] blob_1.2.4 bitops_1.0-7 viridisLite_0.4.2
[109] tidytree_0.4.5 scales_1.2.1 crayon_1.5.2
[112] rlang_1.1.1 cowplot_1.1.1 fastmatch_1.1-4
[115] KEGGREST_1.40.1
