I am running a differential gene expression analysis on the TCGA-BRCA cohort to investigate the effect of tumour cellularity on differential gene expression analysis (how the biology inferred from these analyses may differ if cellularity is included as a covariate). My experimental setup include running the DESeq function with (1) batch effects (BatchId + TSS) and cellularity (ESTIMATE) as a covariate and (2) batch effects (BatchId + TSS) only. The DGE analysis is done between two subtypes of breast cancer (encoded as the HR_status variable)

# Generate DESeq object from raw counts and include batch effects and  cellularity as coveriate
DEseq.pur <- DESeqDataSetFromMatrix(countData = rounded.est,
                                    colData = pca.plots.all.purity.est,
                                    design = ~ HR_status + BatchId + TSS + ESTIMATE)

# Object generate without cellularity as a covariate
DEseq.nopur <- DESeqDataSetFromMatrix(countData = rounded.est,
                                    colData = pca.plots.all.purity.est,
                                    design = ~ HR_status + BatchId + TSS)

After filtering the initial dataset to remove low count genes, I have a DESeqDataSet consisting of 33980 genes and 823 samples. The problem is that for experiment 2 (batch effects only), the DESeq function is not running to completion, despite allowing for more than 12 hours and 10 cpus. The function is stuck on estimating gene-wise dispersion.

library(DESeq2)
library(BiocParallel)
load("DEseq.est.Rdata")
DEseq.est.run <- DESeq(DEseq.est, parallel=TRUE, BPPARAM=MulticoreParam(10))

Weird thing is that if run experiment 1 (batch effects and cellularity as a covariate) with the same specs, this time DESeq runs in 5 hours. I can't figure out what is happening to cause this....

Output of sessionInfo:

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /home/uqmdeluc/R-3.6.1/lib/libRblas.so
LAPACK: /home/uqmdeluc/R-3.6.1/lib/libRlapack.so

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

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

other attached packages:
 [1] DESeq2_1.26.0               SummarizedExperiment_1.16.1
 [3] DelayedArray_0.12.3         BiocParallel_1.20.1
 [5] matrixStats_0.58.0          Biobase_2.46.0
 [7] GenomicRanges_1.38.0        GenomeInfoDb_1.22.1
 [9] IRanges_2.20.2              S4Vectors_0.24.4
[11] BiocGenerics_0.32.0

loaded via a namespace (and not attached):
 [1] bit64_4.0.5            splines_3.6.1          Formula_1.2-4
 [4] assertthat_0.2.1       latticeExtra_0.6-29    blob_1.2.1
 [7] GenomeInfoDbData_1.2.2 pillar_1.5.0           RSQLite_2.2.3
[10] backports_1.2.1        lattice_0.20-38        glue_1.4.2
[13] digest_0.6.27          RColorBrewer_1.1-2     XVector_0.26.0
[16] checkmate_2.0.0        colorspace_2.0-0       htmltools_0.5.1.1
[19] Matrix_1.2-17          XML_3.99-0.3           pkgconfig_2.0.3
[22] genefilter_1.68.0      zlibbioc_1.32.0        purrr_0.3.4
[25] xtable_1.8-4           scales_1.1.1           jpeg_0.1-8.1
[28] htmlTable_2.1.0        tibble_3.1.0           annotate_1.64.0
[31] generics_0.1.0         ggplot2_3.3.3          ellipsis_0.3.1
[34] cachem_1.0.4           nnet_7.3-12            survival_3.2-7
[37] magrittr_2.0.1         crayon_1.4.1           memoise_2.0.0
[40] fansi_0.4.2            foreign_0.8-71         tools_3.6.1
[43] data.table_1.14.0      lifecycle_1.0.0        stringr_1.4.0
[46] locfit_1.5-9.4         munsell_0.5.0          cluster_2.1.0
[49] AnnotationDbi_1.48.0   compiler_3.6.1         rlang_0.4.10
[52] grid_3.6.1             RCurl_1.98-1.2         rstudioapi_0.13
[55] htmlwidgets_1.5.3      bitops_1.0-6           base64enc_0.1-3
[58] gtable_0.3.0           DBI_1.1.1              R6_2.5.0
[61] gridExtra_2.3          knitr_1.31             dplyr_1.0.4
[64] fastmap_1.1.0          bit_4.0.4              utf8_1.1.4
[67] Hmisc_4.5-0            stringi_1.5.3          Rcpp_1.0.6
[70] geneplotter_1.64.0     vctrs_0.3.6            rpart_4.1-15
[73] png_0.1-7              tidyselect_1.1.0       xfun_0.21

Sometimes using the parallelization hurts more than helps because you are sending a big dataset around on the cluster, and that becomes slower than the estimation itself. You can find some other posts on the support site where I give recommendations, but for datasets with hundreds of samples, we often use limma-voom in my lab.