SEQanswers - DESeq2 with no replicates

Hi all,

I am using the DESeq2 package to analyse my RNA-Seq data set from the fruit fly (D. melanogaster). Unfortunately there are no replicates.

I know this is not optimal and one can't really relay on the statistical strength of the results, but we can still look into the data and relay on the fold-induction differences between the samples.

This is also the reason for my question.
I know the variance might be over-estimated, but what I don not understand is, why I get strange BaseMean and FoldChange results.

This is how I run DESeq2:

Code:

cds <- DESeqDataSetFromMatrix (

countData = Comp,

colData   = colData,  

design    = ~condition

)

fit = DESeq(cds)

res = results(fit)

But when I look at the results, I get the wrong numbers.
the raw values from my samples:

Code:

>Comp[13696:13706,]

            sample1  sample2

FBgn0085379        1    4

FBgn0085380      104  117

FBgn0085382      101  137

FBgn0085383       88  187

FBgn0085384       90  275

FBgn0085385       18   55

FBgn0085386       40   40

FBgn0085387       16  310

FBgn0085388      910 3333

FBgn0085390      192  179

FBgn0085391       96  359

and these is a snippet off the results from the "differential expression" analysis:

Code:

>res[13696:13706,]

log2 fold change (MAP): condition sample2 vs sample1 

Wald test p-value: condition sample2 vs sample1 

DataFrame with 11 rows and 6 columns

              baseMean log2FoldChange     lfcSE       stat    pvalue      padj

             <numeric>      <numeric> <numeric>  <numeric> <numeric> <numeric>

FBgn0085379   2.047768      0.1776917  1.656357  0.1072786 0.9145679  0.999346

FBgn0085380 119.997967     -1.0010375  1.365438 -0.7331255 0.4634819  0.999346

FBgn0085382 123.804622     -0.7832908  1.339541 -0.5847457 0.5587187  0.999346

FBgn0085383 128.899132     -0.2415351  1.299186 -0.1859127 0.8525132  0.999346

FBgn0085384 157.869569      0.2069421  1.275569  0.1622352 0.8711206  0.999346

...                ...            ...       ...        ...       ...       ...

FBgn0085386   44.59838     -1.0634868  1.528435 -0.6958011 0.4865534  0.999346

FBgn0085387  109.25461      2.2342308  1.536826  1.4537959 0.1460029  0.999346

FBgn0085388 1768.01176      0.4434720  1.179468  0.3759932 0.7069220  0.999346

FBgn0085390  210.03007     -1.2372886  1.341767 -0.9221335 0.3564590  0.999346

FBgn0085391  188.81235      0.4581024  1.270345  0.3606124 0.7183892  0.999346

My questions regards the values in the line "FBgn0085380" and "FBgn0085386", just as an example.

In the raw data for the first gene shows a slight higher read counts for sample2, while the number is equal for the second gene. But in the results of the differential expression I get a different picture.
for the first gene I get a BaseMean of ~119, though the numer of reads is lower, in the second I have a similar picture. The FoldChange values are off in the same way.
I get in both a downregulation in my first sample, though the number of reads is higher in the second or equal in the two samples respectively.

Is there an explanation for this behaviour? Are the numbers off due to the fact, that I have no replicate and all the samples are regarded as replicates ( but this still doesn't explain the BaseMean values)?

Thanks in advance

Assa