hi there!

My question is related to a DESeq2 output where apparently log2 fold-changes are wrongly calculated.

Let me, please, explain the case with one gene result:

Raw counts for that gene (HTSeq-count):

Ctrl   2200, Treatment 2097

Normalised counts by DESeq2:

Log2(treatment/control) from differential expression test

I expected the log2fc to be -0.0892376619

I must say that this test is done without replicates, only one control sample vs its corresponding treament sample. Could this affect in some way??

When I perform the same test with 3 controls and 3 treatments, used as replicates for the two conditions, the log2(fc) are correctly calculated.

**The control and treatment samples shown here participate also as one of these 3 replicates for each group.

thanks very much !

Sure I can show the code. I am pasting it below for this case. Thanks very much !

library("BiocParallel")
register(MulticoreParam(2))
library("DESeq2")

countTable = read.table("Treatment_3_vs_Ctrl_3.txt",header=TRUE, row.names=1)
condition = factor(c("Ctrl_3","Treatment_3"))
libType = c("oneFactor","oneFactor")
condition <- relevel(condition, "Ctrl_3")
experiment_design=data.frame(row.names = colnames(countTable), condition, libType)

cds <- DESeqDataSetFromMatrix(countData = countTable, colData=experiment_design, design=~condition)
cds_DESeqED <- DESeq(cds,parallel = TRUE)
res <- results(cds_DESeqED,parallel = TRUE,alpha = 0.05, pAdjustMethod = "BH")
write.table(res,file = "Treatment_3_vs_Ctrl_3.differentialExpression.txt",row.names = TRUE,col.names = NA,append = FALSE, quote = FALSE, sep = "\t",eol = "\n", na = "NA", dec = ".")

normalizedReadCounts = counts(cds_DESeqED,normalized=TRUE)
write.table(normalizedReadCounts,file = "Treatment_3_vs_Ctrl_3.normalizedCounts.xls",row.names = TRUE,col.names = NA,append = FALSE, quote = FALSE, sep = "\t",eol = "\n", na = "NA", dec = ".")

please find below what is printed out. Thanks!

cds_DESeqED <- DESeq(cds,parallel = TRUE)
estimating size factors
estimating dispersions
gene-wise dispersion estimates: 2 workers
mean-dispersion relationship
final dispersion estimates, MLE betas: 2 workers
fitting model and testing: 2 workers
Warning message:
In checkForExperimentalReplicates(object, modelMatrix) :
  same number of samples and coefficients to fit,
  estimating dispersion by treating samples as replicates.
  read the ?DESeq section on 'Experiments without replicates'
> res <- results(cds_DESeqED,parallel = TRUE,alpha = 0.05, pAdjustMethod = "BH")

yep, sorry.... here you go:

> res
log2 fold change (MAP): Intercept
Wald test p-value: Intercept
DataFrame with 23368 rows and 6 columns
              baseMean log2FoldChange      lfcSE       stat        pvalue
             <numeric>      <numeric>  <numeric>  <numeric>     <numeric>
A1BG          5.496657       2.458554 0.97249497  2.5280895  1.146851e-02
A1BG-AS1      2.000048       1.000035 1.58919037  0.6292732  5.291702e-01
A1CF        172.955513       7.434257 0.17722342 41.9485028  0.000000e+00
A2LD1       139.520753       7.124336 0.19661333 36.2352646 1.695768e-287
A2M         647.688886       9.339157 0.09746897 95.8167192  0.000000e+00
...                ...            ...        ...        ...           ...
ZYG11B      1126.95771      10.138218 0.07277373  139.31150  0.000000e+00
ZYX         1775.77177      10.794230 0.06014082  179.48261  0.000000e+00
ZZEF1       2234.31414      11.125616 0.05456313  203.90356  0.000000e+00
ZZZ3         635.78304       9.312391 0.09847726   94.56387  0.000000e+00
1/2-SBSRNA4   56.97357       5.832221 0.30675574   19.01259  1.341644e-80
                     padj
                <numeric>
A1BG         1.238768e-02
A1BG-AS1     5.322729e-01
A1CF         0.000000e+00
A2LD1       2.487762e-287
A2M          0.000000e+00
...                   ...
ZYG11B       0.000000e+00
ZYX          0.000000e+00
ZZEF1        0.000000e+00
ZZZ3         0.000000e+00
1/2-SBSRNA4  1.785498e-80

opps, I've just seen your last answers.

Thanks, I'll download v1.11.30