lfcshrink error DESeq2
1
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Alina • 0
@36288a68
Last seen 2.3 years ago
Spain

Hello!

I'm having problems with lfcShrink in my DESeq2 workflow.

I'm trying to do a differential expression analysis (with only one comparison term: "MULTIseq_ID_call2") on my single-cell data. However when I do lfcShrink I get an error that I cannot interpret.

Can you help me?

dds <- DESeq(dds, test = "LRT", reduced=~1, useT=TRUE, minmu=1e-6,minReplicatesForReplace=Inf, fitType = "glmGamPoi")

res <- results(dds,
               alpha = 0.05)

res <- lfcShrink(dds, 
                 coef="MULTIseq_ID_call2iRBC_extract",
                  type="apeglm")

Error:

Error in if (objective(min.var) < 0) { :
missing value where TRUE/FALSE needed
3.
priorVar(mle)
2.
apeglm::apeglm(Y = Y, x = design, log.lik = log.lik, param = disps,
coef = coefNum, mle = mle, threshold = apeT, weights = weights,
offset = offset, method = apeMethod, ...)
1.
lfcShrink(dds, coef = "MULTIseq_ID_call2iRBC_extract", type = "apeglm",
res = res)

> sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.7 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /g/easybuild/x86_64/Rocky/8/haswell/software/FlexiBLAS/3.2.0-GCC-11.3.0/lib64/libflexiblas.so.3.2

locale:
 [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                  LC_TIME=en_US.UTF-8          
 [4] LC_COLLATE=en_US.UTF-8        LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8      
 [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8           LC_ADDRESS=en_US.UTF-8       
[10] LC_TELEPHONE=en_US.UTF-8      LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8

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

other attached packages:
 [1] ashr_2.2-54                 apeglm_1.18.0               glmGamPoi_1.8.0            
 [4] DESeq2_1.36.0               pheatmap_1.0.12             MAST_1.22.0                
 [7] ggrepel_0.9.1               NMF_0.25                    bigmemory_4.6.1            
[10] cluster_2.1.3               rngtools_1.5.2              registry_0.5-1             
[13] data.table_1.14.2           clusterProfiler_4.4.4       msigdbr_7.5.1              
[16] SingleR_1.10.0              celldex_1.6.0               writexl_1.4.0              
[19] bluster_1.6.0               scDblFinder_1.10.0          scran_1.24.1               
[22] scater_1.24.0               scuttle_1.6.3               SingleCellExperiment_1.18.1
[25] SummarizedExperiment_1.26.1 Biobase_2.56.0              GenomicRanges_1.48.0       
[28] GenomeInfoDb_1.32.2         IRanges_2.30.0              S4Vectors_0.34.0           
[31] BiocGenerics_0.42.0         MatrixGenerics_1.8.1        matrixStats_0.62.0         
[34] umap_0.2.9.0                reticulate_1.26             deMULTIplex_1.0.2          
[37] SeuratObject_4.1.3          Seurat_4.2.1                limma_3.52.2               
[40] forcats_0.5.1               stringr_1.4.0               dplyr_1.0.9                
[43] purrr_0.3.4                 readr_2.1.2                 tidyr_1.2.0                
[46] tibble_3.1.7                tidyverse_1.3.1             RColorBrewer_1.1-3         
[49] xlsx_0.6.5                  readxl_1.4.0                rstatix_0.7.0              
[52] ggpubr_0.4.0                ggplot2_3.4.0              

loaded via a namespace (and not attached):
  [1] rsvd_1.0.5                    ica_1.0-2                     Rsamtools_2.12.0             
  [4] foreach_1.5.2                 lmtest_0.9-40                 crayon_1.5.1                 
  [7] MASS_7.3-57                   nlme_3.1-158                  backports_1.4.1              
 [10] reprex_2.0.1                  GOSemSim_2.22.0               rlang_1.0.6                  
 [13] XVector_0.36.0                ROCR_1.0-11                   irlba_2.3.5                  
 [16] filelock_1.0.2                xgboost_1.6.0.1               BiocParallel_1.30.4          
 [19] rjson_0.2.21                  bit64_4.0.5                   glue_1.6.2                   
 [22] mixsqp_0.3-43                 sctransform_0.3.5             parallel_4.2.1               
 [25] vipor_0.4.5                   spatstat.sparse_3.0-0         AnnotationDbi_1.58.0         
 [28] DOSE_3.22.0                   spatstat.geom_3.0-3           haven_2.5.0                  
 [31] tidyselect_1.1.2              fitdistrplus_1.1-8            XML_3.99-0.10                
 [34] zoo_1.8-10                    GenomicAlignments_1.32.0      xtable_1.8-4                 
 [37] magrittr_2.0.3                evaluate_0.15                 cli_3.4.1                    
 [40] zlibbioc_1.42.0               rstudioapi_0.13               miniUI_0.1.1.1               
 [43] sp_1.5-1                      fastmatch_1.1-3               treeio_1.20.1                
 [46] shiny_1.7.1                   BiocSingular_1.12.0           xfun_0.31                    
 [49] askpass_1.1                   tidygraph_1.2.1               KEGGREST_1.36.2              
 [52] interactiveDisplayBase_1.34.0 ape_5.6-2                     listenv_0.8.0                
 [55] xlsxjars_0.6.1                Biostrings_2.64.0             png_0.1-7                    
 [58] future_1.26.1                 withr_2.5.0                   bitops_1.0-7                 
 [61] ggforce_0.3.3                 plyr_1.8.7                    cellranger_1.1.0             
 [64] coda_0.19-4                   dqrng_0.3.0                   pillar_1.7.0                 
 [67] cachem_1.0.6                  fs_1.5.2                      DelayedMatrixStats_1.18.0    
 [70] vctrs_0.5.1                   ellipsis_0.3.2                generics_0.1.2               
 [73] tools_4.2.1                   beeswarm_0.4.0                munsell_0.5.0                
 [76] tweenr_1.0.2                  fgsea_1.22.0                  DelayedArray_0.22.0          
 [79] fastmap_1.1.0                 compiler_4.2.1                abind_1.4-5                  
 [82] httpuv_1.6.5                  rtracklayer_1.56.1            ExperimentHub_2.4.0          
 [85] plotly_4.10.1                 rJava_1.0-6                   GenomeInfoDbData_1.2.8       
 [88] gridExtra_2.3                 edgeR_3.38.1                  lattice_0.20-45              
 [91] deldir_1.0-6                  utf8_1.2.2                    later_1.3.0                  
 [94] BiocFileCache_2.4.0           jsonlite_1.8.0                scales_1.2.0                 
 [97] ScaledMatrix_1.4.0            tidytree_0.3.9                pbapply_1.5-0                
[100] carData_3.0-5                 sparseMatrixStats_1.8.0       genefilter_1.78.0            
[103] lazyeval_0.2.2                promises_1.2.0.1              doParallel_1.0.17            
[106] car_3.1-0                     goftest_1.2-3                 spatstat.utils_3.0-1         
[109] rmarkdown_2.14                cowplot_1.1.1                 statmod_1.4.36               
[112] Rtsne_0.16                    downloader_0.4                uwot_0.1.14                  
[115] igraph_1.3.2                  numDeriv_2016.8-1.1           survival_3.3-1               
[118] yaml_2.3.5                    SQUAREM_2021.1                htmltools_0.5.2              
[121] memoise_2.0.1                 BiocIO_1.6.0                  locfit_1.5-9.5               
[124] graphlayouts_0.8.0            viridisLite_0.4.0             digest_0.6.29                
[127] assertthat_0.2.1              mime_0.12                     rappdirs_0.3.3               
[130] emdbook_1.3.12                bigmemory.sri_0.1.3           RSQLite_2.2.14               
[133] yulab.utils_0.0.5             future.apply_1.9.0            blob_1.2.3                   
[136] labeling_0.4.2                splines_4.2.1                 AnnotationHub_3.4.0          
[139] RCurl_1.98-1.7                broom_0.8.0                   hms_1.1.1                    
[142] modelr_0.1.8                  colorspace_2.0-3              BiocManager_1.30.18          
[145] ggbeeswarm_0.6.0              aplot_0.1.6                   Rcpp_1.0.8.3                 
[148] RANN_2.6.1                    mvtnorm_1.1-3                 enrichplot_1.16.2            
[151] fansi_1.0.3                   tzdb_0.3.0                    truncnorm_1.0-8              
[154] parallelly_1.32.0             R6_2.5.1                      grid_4.2.1                   
[157] ggridges_0.5.3                lifecycle_1.0.3               curl_4.3.2                   
[160] ggsignif_0.6.3                leiden_0.4.2                  DO.db_2.9                    
[163] Matrix_1.5-1                  qvalue_2.28.0                 RcppAnnoy_0.0.19             
[166] iterators_1.0.14              spatstat.explore_3.0-3        htmlwidgets_1.5.4            
[169] beachmat_2.12.0               polyclip_1.10-0               shadowtext_0.1.2             
[172] gridGraphics_0.5-1            rvest_1.0.2                   globals_0.15.0               
[175] openssl_2.0.2                 patchwork_1.1.2               spatstat.random_3.0-1        
[178] bdsmatrix_1.3-6               progressr_0.10.1              invgamma_1.1                 
[181] codetools_0.2-18              lubridate_1.8.0               GO.db_3.15.0                 
[184] metapod_1.4.0                 dbplyr_2.2.0                  gridBase_0.4-7               
[187] RSpectra_0.16-1               gtable_0.3.0                  DBI_1.1.3                    
[190] ggfun_0.0.6                   tensor_1.5                    httr_1.4.3                   
[193] KernSmooth_2.23-20            stringi_1.7.6                 uuid_1.1-0                   
[196] reshape2_1.4.4                farver_2.1.0                  annotate_1.74.0              
[199] viridis_0.6.2                 ggtree_3.4.1                  xml2_1.3.3                   
[202] bbmle_1.0.25                  BiocNeighbors_1.14.0          restfulr_0.0.15              
[205] geneplotter_1.74.0            ggplotify_0.1.0               scattermore_0.8              
[208] BiocVersion_3.15.2            bit_4.0.4                     scatterpie_0.1.7             
[211] spatstat.data_3.0-0           ggraph_2.0.5                  pkgconfig_2.0.3              
[214] babelgene_22.3                knitr_1.39
DESeq2 lfcshrink DESe • 3.4k views
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0
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I bet this is because apeglm required a lfcSE which is NA though when using apeglm, right Michael Love ?

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yes, lfcSE is NA. Can I change that?

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This case is now addressed (gives an informative error) in devel:

https://github.com/mikelove/DESeq2/issues/73

You can test that the error works with devtools::install_github("mikelove/DESeq2")

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@mikelove
Last seen 12 hours ago
United States

What is resultsNames(dds)
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> resultsNames(dds)
[1] "Intercept"                     "MULTIseq_ID_call2iRBC_extract"
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Is the general recommendation to always use "LRT" on single-cell data? Even if there is only one comparison factor?

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You can shrink coefficients listed in resultsNames, see ?lfcShrink

Yes, LRT recommended for scRNA-seq

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Thank you!

So it should be possible with coef="MULTIseq_ID_call2iRBC_extract"?

Is the problem that my lfcSE column is NA?

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Yeah, lfcSE needs to not be NA.

You can use a different fitType (the default for example).

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^ my advice is here.

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Thanks a lot!

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It's a reproducible error with glmGamPoi+apeglm causing it:

suppressMessages(library(DESeq2))
dds <- makeExampleDESeqDataSet()
dds <- DESeq(dds, test="LRT", reduced=~1, fitType="glmGamPoi", quiet=TRUE)
res <- lfcShrink(dds, coef=resultsNames(dds)[2], type="apeglm", quiet=TRUE)
#> Error in if (objective(min.var) < 0) {: Fehlender Wert, wo TRUE/FALSE nötig ist
sessionInfo()
#> R version 4.2.2 (2022-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 22000)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=German_Germany.utf8  LC_CTYPE=German_Germany.utf8   
#> [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C                   
#> [5] LC_TIME=German_Germany.utf8    
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] DESeq2_1.38.3               SummarizedExperiment_1.28.0
#>  [3] Biobase_2.58.0              MatrixGenerics_1.10.0      
#>  [5] matrixStats_0.63.0          GenomicRanges_1.50.1       
#>  [7] GenomeInfoDb_1.34.9         IRanges_2.32.0             
#>  [9] S4Vectors_0.36.1            BiocGenerics_0.44.0        
#> 
#> loaded via a namespace (and not attached):
#>  [1] httr_1.4.4                bit64_4.0.5              
#>  [3] DelayedMatrixStats_1.20.0 highr_0.9                
#>  [5] blob_1.2.3                GenomeInfoDbData_1.2.9   
#>  [7] yaml_2.3.6                numDeriv_2016.8-1.1      
#>  [9] pillar_1.9.0              RSQLite_2.2.19           
#> [11] lattice_0.20-45           glue_1.6.2               
#> [13] bbmle_1.0.25              digest_0.6.30            
#> [15] RColorBrewer_1.1-3        XVector_0.38.0           
#> [17] colorspace_2.0-3          plyr_1.8.8               
#> [19] htmltools_0.5.4           Matrix_1.5-1             
#> [21] XML_3.99-0.13             pkgconfig_2.0.3          
#> [23] emdbook_1.3.12            zlibbioc_1.44.0          
#> [25] mvtnorm_1.1-3             xtable_1.8-4             
#> [27] scales_1.2.1              apeglm_1.20.0            
#> [29] BiocParallel_1.32.4       tibble_3.2.1             
#> [31] annotate_1.76.0           KEGGREST_1.38.0          
#> [33] generics_0.1.3            ggplot2_3.4.2            
#> [35] cachem_1.0.6              withr_2.5.0              
#> [37] cli_3.6.1                 magrittr_2.0.3           
#> [39] crayon_1.5.2              memoise_2.0.1            
#> [41] evaluate_0.18             fs_1.5.2                 
#> [43] fansi_1.0.3               MASS_7.3-58.1            
#> [45] tools_4.2.2               lifecycle_1.0.3          
#> [47] stringr_1.5.0             munsell_0.5.0            
#> [49] reprex_2.0.2              locfit_1.5-9.6           
#> [51] DelayedArray_0.24.0       AnnotationDbi_1.60.0     
#> [53] Biostrings_2.66.0         compiler_4.2.2           
#> [55] rlang_1.1.0               grid_4.2.2               
#> [57] RCurl_1.98-1.9            rstudioapi_0.14          
#> [59] glmGamPoi_1.10.2          bitops_1.0-7             
#> [61] rmarkdown_2.18            gtable_0.3.1             
#> [63] codetools_0.2-18          DBI_1.1.3                
#> [65] R6_2.5.1                  bdsmatrix_1.3-6          
#> [67] knitr_1.41                dplyr_1.1.2              
#> [69] fastmap_1.1.0             bit_4.0.5                
#> [71] utf8_1.2.2                stringi_1.7.8            
#> [73] parallel_4.2.2            Rcpp_1.0.9               
#> [75] vctrs_0.6.2               geneplotter_1.76.0       
#> [77] png_0.1-8                 coda_0.19-4              
#> [79] tidyselect_1.2.0          xfun_0.35                
#> [81] sparseMatrixStats_1.10.0
Created on 2023-05-09 with reprex v2.0.2
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1
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Ah, I can just add a check for NA standard errors here. Will put it on the list.

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This case is now addressed (gives an informative error) in devel:

https://github.com/mikelove/DESeq2/issues/73

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Thank you!!

So should I for now just not use glmGamPoi? Or is there anything else I could do? Which fit type would you recommend for my data instead?

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I have exactly the same issue as Alina. Which fit type should we use?

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Answer is above in another thread.

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