problem with regressing out rbc genes, not all variables found in seurat object meta data?

[jessicook]

Thanks for this great package, I've enjoyed using it. I'm attempting to regress out the effect of RBC genes, and I'm running into the error below. I've added my session info at the end.

> rbc.genes <- list("Hba-a1", "Hbb-bs", "Hbb-bt", "Hba-a2")
> om.all <- AddModuleScore(
+     object = om.all,
+     features = rbc.genes,
+     ctrl = 5,
+     name = 'RBC_regress'
+ )
> om.all <- SCTransform(om.all, vars.to.regress = c("RBC_regress"), verbose = FALSE)
Error in SCTransform(om.all, vars.to.regress = c("RBC_regress"), verbose = FALSE) : 
  problem with second non-regularized linear regression; not all variables found in seurat object meta data; check vars.to.regress parameter
> traceback()
2: stop("problem with second non-regularized linear regression; not all variables found in seurat object meta data; check vars.to.regress parameter")
1: SCTransform(om.all, vars.to.regress = c("RBC_regress"), verbose = FALSE)
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] scuttle_1.0.4               celldex_1.0.0               scater_1.18.3              
 [4] sctransform_0.3.2           scRNAseq_2.4.0              scran_1.18.3               
 [7] SingleR_1.4.0               Seurat_3.2.3                forcats_0.5.0              
[10] stringr_1.4.0               dplyr_1.0.3                 purrr_0.3.4                
[13] readr_1.4.0                 tidyr_1.1.2                 tibble_3.0.5               
[16] ggplot2_3.3.3               tidyverse_1.3.0             SingleCellExperiment_1.12.0
[19] SummarizedExperiment_1.20.0 Biobase_2.50.0              GenomicRanges_1.42.0       
[22] GenomeInfoDb_1.26.2         IRanges_2.24.1              MatrixGenerics_1.2.0       
[25] matrixStats_0.57.0          S4Vectors_0.28.1            BiocGenerics_0.36.0        

loaded via a namespace (and not attached):
  [1] reticulate_1.18               tidyselect_1.1.0              RSQLite_2.2.2                
  [4] AnnotationDbi_1.52.0          htmlwidgets_1.5.3             grid_4.0.3                   
  [7] BiocParallel_1.24.1           Rtsne_0.15                    munsell_0.5.0                
 [10] codetools_0.2-18              ica_1.0-2                     statmod_1.4.35               
 [13] future_1.21.0                 miniUI_0.1.1.1                withr_2.3.0                  
 [16] colorspace_2.0-0              knitr_1.30                    rstudioapi_0.13              
 [19] ROCR_1.0-11                   tensor_1.5                    listenv_0.8.0                
 [22] labeling_0.4.2                GenomeInfoDbData_1.2.4        polyclip_1.10-0              
 [25] pheatmap_1.0.12               bit64_4.0.5                   farver_2.0.3                 
 [28] parallelly_1.23.0             vctrs_0.3.6                   generics_0.1.0               
 [31] xfun_0.20                     BiocFileCache_1.14.0          R6_2.5.0                     
 [34] ggbeeswarm_0.6.0              rsvd_1.0.3                    locfit_1.5-9.4               
 [37] AnnotationFilter_1.14.0       bitops_1.0-6                  spatstat.utils_1.20-2        
 [40] DelayedArray_0.16.0           assertthat_0.2.1              promises_1.1.1               
 [43] scales_1.1.1                  beeswarm_0.2.3                gtable_0.3.0                 
 [46] beachmat_2.6.4                globals_0.14.0                goftest_1.2-2                
 [49] ensembldb_2.14.0              rlang_0.4.10                  splines_4.0.3                
 [52] rtracklayer_1.50.0            lazyeval_0.2.2                broom_0.7.3                  
 [55] yaml_2.2.1                    BiocManager_1.30.10           reshape2_1.4.4               
 [58] abind_1.4-5                   modelr_0.1.8                  GenomicFeatures_1.42.1       
 [61] backports_1.2.1               httpuv_1.5.5                  tools_4.0.3                  
 [64] ellipsis_0.3.1                RColorBrewer_1.1-2            ggridges_0.5.3               
 [67] Rcpp_1.0.6                    plyr_1.8.6                    progress_1.2.2               
 [70] sparseMatrixStats_1.2.0       zlibbioc_1.36.0               RCurl_1.98-1.2               
 [73] prettyunits_1.1.1             openssl_1.4.3                 rpart_4.1-15                 
 [76] deldir_0.2-3                  viridis_0.5.1                 pbapply_1.4-3                
 [79] cowplot_1.1.1                 zoo_1.8-8                     haven_2.3.1                  
 [82] ggrepel_0.9.1                 cluster_2.1.0                 fs_1.5.0                     
 [85] magrittr_2.0.1                RSpectra_0.16-0               data.table_1.13.6            
 [88] scattermore_0.7               lmtest_0.9-38                 reprex_0.3.0                 
 [91] RANN_2.6.1                    ProtGenerics_1.22.0           fitdistrplus_1.1-3           
 [94] evaluate_0.14                 hms_1.0.0                     patchwork_1.1.1              
 [97] mime_0.9                      xtable_1.8-4                  XML_3.99-0.5                 
[100] readxl_1.3.1                  gridExtra_2.3                 biomaRt_2.46.0               
[103] compiler_4.0.3                KernSmooth_2.23-18            crayon_1.3.4                 
[106] htmltools_0.5.1               mgcv_1.8-33                   later_1.1.0.1                
[109] lubridate_1.7.9.2             DBI_1.1.1                     ExperimentHub_1.16.0         
[112] dbplyr_2.0.0                  MASS_7.3-53                   rappdirs_0.3.1               
[115] Matrix_1.3-2                  cli_2.2.0                     igraph_1.2.6                 
[118] pkgconfig_2.0.3               GenomicAlignments_1.26.0      plotly_4.9.3                 
[121] xml2_1.3.2                    vipor_0.4.5                   dqrng_0.2.1                  
[124] XVector_0.30.0                rvest_0.3.6                   digest_0.6.27                
[127] RcppAnnoy_0.0.18              Biostrings_2.58.0             spatstat.data_1.7-0          
[130] rmarkdown_2.6                 cellranger_1.1.0              leiden_0.3.6                 
[133] uwot_0.1.10                   edgeR_3.32.1                  DelayedMatrixStats_1.12.2    
[136] curl_4.3                      Rsamtools_2.6.0               shiny_1.5.0                  
[139] lifecycle_0.2.0               nlme_3.1-151                  jsonlite_1.7.2               
[142] BiocNeighbors_1.8.2           askpass_1.1                   viridisLite_0.3.0            
[145] limma_3.46.0                  fansi_0.4.2                   pillar_1.4.7                 
[148] lattice_0.20-41               fastmap_1.0.1                 httr_1.4.2                   
[151] survival_3.2-7                interactiveDisplayBase_1.28.0 glue_1.4.2                   
[154] spatstat_1.64-1               png_0.1-7                     bluster_1.0.0                
[157] BiocVersion_3.12.0            bit_4.0.4                     stringi_1.5.3                
[160] blob_1.2.1                    BiocSingular_1.6.0            AnnotationHub_2.22.0         
[163] memoise_1.1.0                 irlba_2.3.3                   future.apply_1.7.0 

In addition, if I want to regress out multiple facotrs (percent.mt, cell cycle etc), should I do this all at once, or is running SCtransform sequentially okay?

[jessicook]

might have figured this out, looked at the meta.data and the regressions were stored per gene as "RBC_regress1" and so on, when I changed vars.to.regress = c("RBC_regress1", RBC_regress2"..."4") , it seems to be working. Was this the correct solution?

[ChristophH]

First a disclaimer, AddModuleScore and SCTransform are functions from the Seurat package, not sctransform.

There is one problem with your solution. The list you are passing to AddModuleScore as features is of length 4, when I think you want a list with one entry only.

rbc.genes <- list(c("Hba-a1", "Hbb-bs", "Hbb-bt", "Hba-a2"))

You'd then only get one RBC_regress variable (combining the signal from the four genes) that you could use like so:

vars.to.regress = "RBC_regress1"

[jessicook]

this solved it, thanks!