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-   -   DESeq2 with no replicates - strange results (http://seqanswers.com/forums/showthread.php?t=45195)

frymor 07-23-2014 02:27 AM

DESeq2 with no replicates - strange results
 
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

Jeremy 07-23-2014 02:49 AM

Because the result is the data after it has been corrected for library size. E.g. if one sample had 20M reads and the other had 16M reads then they can't be compared directly. DESeq corrects for this and that is why you get so many decimal places.

Try this:
apply(Comp, 2, sum)

frymor 07-23-2014 03:17 AM

:D
oh yes you're right.

I have totally forgot it.

yes I checked the normalized values and it looks better:
Code:

              sample1    sample2
FBgn0085379    1.608047    2.48749
FBgn0085380  167.236850  72.75908
FBgn0085382  162.412710  85.19653
FBgn0085383  141.508104  116.29016
FBgn0085384  144.724197  171.01494
FBgn0085385  28.944839  34.20299
FBgn0085386  64.321865  24.87490
FBgn0085387  25.728746  192.78048
FBgn0085388 1463.322439 2072.70108
FBgn0085390  308.744954  111.31518
FBgn0085391  154.372477  223.25223

how embarrassing :confused:

thanks


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