Hi!

I understand that when rows do not converge in beta while running DESeq2, they are labeled in mcols(object)$betaConv as "FALSE". However, I've noticed that some elements of mcols(object)$betaConv are <NA>. What does that mean exactly? Is it ok to drop these genes from the results step of DESeq2 (as recommended in this post - DESeq2 Error: rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest ).

Also, I'm performing exploratory data analyses on VST transformed count matrices by running PCA and making heatmaps (see code below). Does it matter if I use the full VST matrix for this purpose, or the trimmed-down VST matrix (one in which the genes which did not converge in beta or have NA's in mcols(object)$betaConv are dropped)? Will I get different results in these two scenarios (see Scenario 1 and Scenario 2 in the code below)?

Thank you!

library(DESeq2)

# run DE analysis
dds=DESeq(dds)

ddsClean <- dds[which(mcols(dds)$betaConv),]

#############################
## SCENARIO 1 (data exploration using the full vst count matrix, extracted from the dds object)
#############################

# make transformed count data, using variance stabilizing transformation (VST) 
vsd = vst(dds, blind=FALSE)

#run PCA 
pca = prcomp(t(assay(vsd)))

# make heatmap of VST-transformed count matrix
pheatmap(assay(vsd), cluster_rows=TRUE, cluster_cols=TRUE, annotation_col=annotation_col, scale='row')

#############################
## SCENARIO 2 (data exploration using the trimmed-down vst count matrix, extracted from the ddsClean object)
#############################

# make transformed count data, using variance stabilizing transformation (VST) 
vsd = vst(ddsClean, blind=FALSE)

#run PCA 
pca = prcomp(t(assay(vsd)))

# make heatmap of VST-transformed count matrix
pheatmap(assay(vsd), cluster_rows=TRUE, cluster_cols=TRUE, annotation_col=annotation_col, scale='row')

sessionInfo( )

> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /pbtech_mounts/homes027/nai2008/anaconda3/envs/R_4.0/lib/libopenblasp-r0.3.10.so

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

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

other attached packages:
 [1] sva_3.38.0                  BiocParallel_1.24.0
 [3] genefilter_1.72.0           mgcv_1.8-33
 [5] nlme_3.1-151                tximeta_1.8.4
 [7] ensembldb_2.14.0            AnnotationFilter_1.14.0
 [9] GenomicFeatures_1.42.1      AnnotationDbi_1.52.0
[11] AnnotationHub_2.22.0        BiocFileCache_1.14.0
[13] dbplyr_2.0.0                RColorBrewer_1.1-2
[15] pheatmap_1.0.12             DESeq2_1.30.0
[17] SummarizedExperiment_1.20.0 Biobase_2.50.0
[19] MatrixGenerics_1.2.0        matrixStats_0.58.0
[21] GenomicRanges_1.42.0        GenomeInfoDb_1.26.0
[23] IRanges_2.24.0              S4Vectors_0.28.0
[25] BiocGenerics_0.36.0

loaded via a namespace (and not attached):
 [1] ProtGenerics_1.22.0           bitops_1.0-6
 [3] bit64_4.0.5                   progress_1.2.2
 [5] httr_1.4.2                    tools_4.0.3
 [7] R6_2.5.0                      DBI_1.1.1
 [9] lazyeval_0.2.2                colorspace_2.0-0
[11] withr_2.4.1                   tidyselect_1.1.0
[13] prettyunits_1.1.1             bit_4.0.4
[15] curl_4.3                      compiler_4.0.3
[17] xml2_1.3.2                    DelayedArray_0.16.0
[19] rtracklayer_1.50.0            scales_1.1.1
[21] readr_1.4.0                   askpass_1.1
[23] rappdirs_0.3.1                stringr_1.4.0
[25] digest_0.6.27                 Rsamtools_2.6.0
[27] XVector_0.30.0                pkgconfig_2.0.3
[29] htmltools_0.5.1.1             limma_3.46.0
[31] fastmap_1.1.0                 rlang_0.4.10
[33] rstudioapi_0.13               RSQLite_2.2.3
[35] shiny_1.6.0                   farver_2.0.3
[37] generics_0.1.0                jsonlite_1.7.2
[39] dplyr_1.0.4                   RCurl_1.98-1.2
[41] magrittr_2.0.1                GenomeInfoDbData_1.2.4
[43] Matrix_1.3-2                  Rcpp_1.0.6
[45] munsell_0.5.0                 lifecycle_0.2.0
[47] edgeR_3.32.0                  stringi_1.5.3
[49] yaml_2.2.1                    zlibbioc_1.36.0
[51] grid_4.0.3                    blob_1.2.1
[53] promises_1.1.1                crayon_1.4.1
[55] lattice_0.20-41               Biostrings_2.58.0
[57] splines_4.0.3                 annotate_1.68.0
[59] hms_1.0.0                     locfit_1.5-9.4
[61] pillar_1.4.7                  geneplotter_1.68.0
[63] biomaRt_2.46.1                XML_3.99-0.5
[65] glue_1.4.2                    BiocVersion_3.12.0
[67] BiocManager_1.30.10           vctrs_0.3.6
[69] httpuv_1.5.5                  gtable_0.3.0
[71] openssl_1.4.3                 purrr_0.3.4
[73] assertthat_0.2.1              cachem_1.0.3
[75] ggplot2_3.3.3                 mime_0.9
[77] xtable_1.8-4                  later_1.1.0.1
[79] survival_3.2-7                tibble_3.0.6
[81] GenomicAlignments_1.26.0      memoise_2.0.0
[83] tximport_1.18.0               ellipsis_0.3.1
[85] interactiveDisplayBase_1.28.0

Re: Q1, do these have all zero counts?

If you run vst(dds, blind=FALSE) on a dds in which DESeq() has already been run, it will be the same transformation regardless of if there are more or less rows of data present.