Hello, I realized that when I run Deseq2 in the terminal, it gives me a different result (different values in the res table except for baseMean) compare to when I run in with Snakemake (I was able to reproduce the same issue also on a different computer). However, when I use only one CPU for R in terminal taskset --cpu-list 1 R it gives me the same results as with Snakemake.

My code:

library("tximeta")
library("DESeq2")
library("dplyr")
library("ggplot2")
library("genefilter")
library("ggrepel")
library("DEGreport")

# Read metadata table
sample_metadata <- read.table('data/raw/samples.csv', sep=',', header=TRUE, stringsAsFactors = FALSE)

# Ad $files columns with path to the quant.sf files (result from salmon)
sample_metadata$files <- paste0("data/processed/30062020","/", sample_metadata$names, "/salmon_out/quant.sf")

# Parse salmon result with tximeta
se <- tximeta(sample_metadata)

# summarize transcript counts to genes
gse <- summarizeToGene(se)

# Construction of DESeqDataSet object
dds <- DESeqDataSet(gse, design = ~ intracellular_ig)

# Remove rows of the DESeqDataSet that have no counts, or only a single count across all samples
keep <- rowSums(counts(dds)) > 1
dds <- dds[keep,]

# differential expression pipeline
dds <- DESeq(dds)

# create a result table  with False discovery rate 0.05
res <- results(dds, alpha = 0.05)

# order according to padj
resOrdered <- res[order(res$padj),]
resOrderedDF <- as.data.frame(resOrdered)
# filtResOrderedDF = filter(resOrderedDF, padj < 0.05)
write.csv(resOrderedDF, file = "results/test/deseq_result.csv")

I was able to see a difference after the estimateDispersion with different values dispFit, dispIter, dispMap columns. I was also checking my cores during these commands, and it uses more than one core.

Do you please know what could cause this kind of error?

sessionInfo( )                                                                                                      
R version 4.0.0 (2020-04-24)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS/LAPACK: /home/david/anaconda3/envs/differential_expression/lib/libmkl_rt.so

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

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

other attached packages:
 [1] DEGreport_1.24.1            ggrepel_0.8.2              
 [3] genefilter_1.70.0           ggplot2_3.3.2              
 [5] dplyr_1.0.2                 DESeq2_1.30.0              
 [7] SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
 [9] matrixStats_0.57.0          Biobase_2.48.0             
[11] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
[13] IRanges_2.22.2              S4Vectors_0.26.1           
[15] BiocGenerics_0.34.0         tximeta_1.6.3              

loaded via a namespace (and not attached):
  [1] colorspace_2.0-0              rjson_0.2.20                 
  [3] ellipsis_0.3.1                circlize_0.4.11              
  [5] XVector_0.28.0                ggdendro_0.1.22              
  [7] GlobalOptions_0.1.2           clue_0.3-57                  
  [9] bit64_4.0.5                   interactiveDisplayBase_1.26.3
 [11] AnnotationDbi_1.50.3          xml2_1.3.2                   
 [13] splines_4.0.0                 logging_0.10-108             
 [15] mnormt_2.0.2                  tximport_1.16.1              
 [17] knitr_1.30                    geneplotter_1.66.0           
 [19] jsonlite_1.7.1                Nozzle.R1_1.1-1              
 [21] Rsamtools_2.4.0               broom_0.7.2                  
 [23] annotate_1.66.0               cluster_2.1.0                
 [25] dbplyr_2.0.0                  png_0.1-7                    
 [27] shiny_1.5.0                   BiocManager_1.30.10          
 [29] compiler_4.0.0                httr_1.4.2                   
 [31] backports_1.2.0               assertthat_0.2.1             
 [33] Matrix_1.2-18                 fastmap_1.0.1                
 [35] lazyeval_0.2.2                limma_3.44.3                 
 [37] later_1.1.0.1                 lasso2_1.2-21.1              
 [39] htmltools_0.5.0               prettyunits_1.1.1            
 [41] tools_4.0.0                   gtable_0.3.0                 
 [43] glue_1.4.2                    GenomeInfoDbData_1.2.3       
 [45] rappdirs_0.3.1                Rcpp_1.0.5                   
 [47] vctrs_0.3.5                   Biostrings_2.56.0            
 [49] nlme_3.1-150                  rtracklayer_1.48.0           
 [51] psych_2.0.9                   xfun_0.19                    
 [53] stringr_1.4.0                 mime_0.9                     
 [55] lifecycle_0.2.0               ensembldb_2.12.1             
 [57] XML_3.99-0.5                  AnnotationHub_2.20.2         
 [59] edgeR_3.30.3                  MASS_7.3-53                  
 [61] zlibbioc_1.34.0               scales_1.1.1                 
 [63] hms_0.5.3                     promises_1.1.1               
 [65] ProtGenerics_1.20.0           AnnotationFilter_1.12.0      
 [67] RColorBrewer_1.1-2            ComplexHeatmap_2.4.3         
 [69] yaml_2.2.1                    curl_4.3                     
 [71] memoise_1.1.0                 biomaRt_2.44.4               
 [73] reshape_0.8.8                 stringi_1.5.3                
 [75] RSQLite_2.2.1                 BiocVersion_3.11.1           
 [77] GenomicFeatures_1.40.1        BiocParallel_1.22.0          
 [79] shape_1.4.5                   rlang_0.4.8                  
 [81] pkgconfig_2.0.3               bitops_1.0-6                 
 [83] lattice_0.20-41               purrr_0.3.4                  
 [85] GenomicAlignments_1.24.0      cowplot_1.1.0                
 [87] bit_4.0.4                     tidyselect_1.1.0             
 [89] plyr_1.8.6                    magrittr_2.0.1               
 [91] R6_2.5.0                      generics_0.1.0               
 [93] DBI_1.1.0                     pillar_1.4.7                 
 [95] withr_2.3.0                   survival_3.2-7               
 [97] RCurl_1.98-1.2                tibble_3.0.4                 
 [99] crayon_1.3.4                  tmvnsim_1.0-2                
[101] BiocFileCache_1.12.1          GetoptLong_1.0.4             
[103] progress_1.2.2                locfit_1.5-9.4               
[105] grid_4.0.0                    blob_1.2.1                   
[107] ConsensusClusterPlus_1.52.0   digest_0.6.27                
[109] xtable_1.8-4                  tidyr_1.1.2                  
[111] httpuv_1.5.4                  openssl_1.4.3                
[113] munsell_0.5.0                 askpass_1.1

My first guess would be different versions of DESeq2 in the different runs.

Try printing sessionInfo() in your runs.