Dear all,

I have freshly installed R and DEseq2 on a new device and am now unable to reproduce the results from the Pasilla tutorial. My code is:

library("pasilla")
pasCts <- system.file("extdata",
                      "pasilla_gene_counts.tsv",
                      package="pasilla", mustWork=TRUE)
pasAnno <- system.file("extdata",
                       "pasilla_sample_annotation.csv",
                       package="pasilla", mustWork=TRUE)
cts <- as.matrix(read.csv(pasCts,sep="\t",row.names="gene_id"))
coldata <- read.csv(pasAnno, row.names=1)
coldata <- coldata[,c("condition","type")]
coldata$condition <- factor(coldata$condition)
coldata$type <- factor(coldata$type)

library("DESeq2")

dds <- DESeqDataSetFromMatrix(countData = cts,
                              colData = coldata,
                              design = ~ condition)
dds


keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]


dds <- DESeq(dds)
res <- results(dds)
res
res <- results(dds, contrast=c("condition","treated","untreated"))
summary(res)

the result is

summary(res)

out of 9921 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 52, 0.52%
LFC < 0 (down)     : 137, 1.4%
outliers [1]       : 10, 0.1%
low counts [2]     : 1730, 17%
(mean count < 8)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

But it should be:

summary(res)

out of 9921 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 518, 5.2%
LFC < 0 (down)     : 536, 5.4%
outliers [1]       : 1, 0.01%
low counts [2]     : 1539, 16%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

`

I am confused, why do I have so little differentially expressed genes?

sessionInfo( )

R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=German_Germany.1252  LC_CTYPE=German_Germany.1252   
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C                   
[5] LC_TIME=German_Germany.1252    

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

other attached packages:
 [1] pasilla_1.18.1              IHW_1.18.0                 
 [3] DESeq2_1.30.0               SummarizedExperiment_1.20.0
 [5] Biobase_2.50.0              MatrixGenerics_1.2.1       
 [7] matrixStats_0.58.0          GenomicRanges_1.42.0       
 [9] GenomeInfoDb_1.26.2         IRanges_2.24.1             
[11] S4Vectors_0.28.1            BiocGenerics_0.36
[13] glue_1.4.2             DBI_1.1.1             
[15] BiocParallel_1.24.1    bit64_4.0.5           
[17] RColorBrewer_1.1-2     GenomeInfoDbData_1.2.4
[19] lifecycle_0.2.0        zlibbioc_1.36.0       
[21] munsell_0.5.0          gtable_0.3.0          
[23] memoise_2.0.0          geneplotter_1.68.0    
[25] fastmap_1.1.0          fdrtool_1.2.16        
[27] AnnotationDbi_1.52.0   Rcpp_1.0.6            
[29] xtable_1.8-4           scales_1.1.1          
[31] BiocManager_1.30.10    cachem_1.0.3          
[33] DelayedArray_0.16.1    annotate_1.68.0       
[35] XVector_0.30.0         lpsymphony_1.18.0     
[37] bit_4.0.4              ggplot2_3.3.3         
[39] grid_4.0.3             tools_4.0.3           
[41] bitops_1.0-6           magrittr_2.0.1        
[43] RCurl_1.98-1.2         RSQLite_2.2.3         
[45] tibble_3.0.6           crayon_1.4.1          
[47] pkgconfig_2.0.3        ellipsis_0.3.1        
[49] Matrix_1.2-18          httr_1.4.2            
[51] R6_2.5.0               compiler_4.0.3

You skipped the important step where the sample data is lined up with the counts.