**0**wrote:

Hi all,

First post here :-)

I'm analyzing Differentially Expressed Genes (DEG) in 2 conditions (H = Healthy vs T = Tumor) using DEseq2 (version `DESeq2_1.22.2`

).

When checking/visualising significant DEG with the `plotCounts`

function, I came across one gene with a weird behavior.

The gene, called `RLOC_00033176`

, has a log2FoldChange >5 ( *i.e*. more expressed in T condition versus H):

```
>resMF["RLOC_00033176",]
log2 fold change (MLE): condition T vs H
Wald test p-value: condition T vs H
DataFrame with 1 row and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
RLOC_00033176 1882.05768131145 5.85706382402349 1.20253394435727 4.87060165869492 1.1125894014862e-06 0.000277571873094918
```

However, looking at the counts using `plotCounts`

, I'm observing the opposite trend:

```
> d <- plotCounts(ddsMF, gene="RLOC_00033176", intgroup=c("condition"), returnData=TRUE)
> d
count condition
H01_GRET 6770.361876 H
H01_LABR 4832.668360 H
H02_GRET 6726.804182 H
H02_PODL 2876.324159 H
H03_GRET 7046.313634 H
H03_PODL 6282.789856 H
H04_PODL 12212.188122 H
H06_GRET 6721.764971 H
H06_PODL 6898.072519 H
H07_PODL 10049.535318 H
H08_PODL 6071.940173 H
H09_PODL 3283.395077 H
H12_PODL 8665.185250 H
T01_GRET 556.160217 T
T01_LABR 3.557992 T
T01_PODL 2.817237 T
T02_GRET 6.123579 T
T02_LABR 0.500000 T
T02_PODL 1957.434186 T
T03_GRET 1625.660425 T
T03_PODL 1893.038074 T
T04_GRET 349.213210 T
T04_PODL 14.740731 T
T05_LABR 2.141938 T
T05_PODL 2.564385 T
T06_GRET 2.218089 T
T06_LABR 3.884637 T
T06_PODL 0.500000 T
T07_GRET 1.739414 T
T07_PODL 1.494927 T
T08_GRET 1.656400 T
T08_LABR 1.386739 T
T08_PODL 8.495810 T
T09_GRET 4.069766 T
T09_LABR 1.417592 T
T09_PODL 1.479236 T
T10_LABR 419.258909 T
T11_GRET 2.374560 T
T11_LABR 182.475189 T
T12_GRET 666.680985 T
T12_LABR 3.949453 T
T12_PODL 380.173398 T
T13_GRET 9.495421 T
T13_LABR 0.500000 T
T14_GRET 467.288773 T
T15_GRET 1.578646 T
T15_LABR 0.500000 T
T16_GRET 851.942543 T
T17_GRET 22.565463 T
T17_LABR 1.738759 T
T18_LABR 2.339252 T
T19_GRET 0.500000 T
```

I was wondering how this could be the case?

Thanks

Thomas

PS : Note that for the others significant DEG I've checked, everything seems ok *e.g*:

```
> resMF["RLOC_00008433",]
log2 fold change (MLE): condition T vs H
Wald test p-value: condition T vs H
DataFrame with 1 row and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
RLOC_00008433 21.793008121385 3.70792814522459 0.772645934994616 4.79900039239892 1.59459482173036e-06 0.000372157855974811
> d <- plotCounts(ddsMF gene="RLOC_00008433", intgroup=c("condition"), returnData=TRUE)
> d
count condition
H01_GRET 5.331474 H
H01_LABR 4.581223 H
H02_GRET 7.376440 H
H02_PODL 3.912766 H
H03_GRET 9.221608 H
H03_PODL 4.643991 H
H04_PODL 1.750685 H
H06_GRET 7.247129 H
H06_PODL 1.507386 H
H07_PODL 3.933220 H
H08_PODL 1.567037 H
H09_PODL 1.554576 H
H12_PODL 4.967254 H
T01_GRET 19.571021 T
T01_LABR 31.079923 T
T01_PODL 44.527503 T
T02_GRET 20.584212 T
T02_LABR 15.330119 T
T02_PODL 106.535597 T
T03_GRET 28.993733 T
T03_PODL 21.933047 T
T04_GRET 24.038142 T
T04_PODL 13.949579 T
T05_LABR 30.875846 T
T05_PODL 17.015079 T
T06_GRET 54.619789 T
T06_LABR 47.884916 T
T06_PODL 19.325809 T
T07_GRET 35.203591 T
T07_PODL 12.439128 T
T08_GRET 24.784397 T
T08_LABR 43.950211 T
T08_PODL 2.276847 T
T09_GRET 37.090099 T
T09_LABR 21.604619 T
T09_PODL 3.437709 T
T10_LABR 17.522720 T
T11_GRET 34.242083 T
T11_LABR 41.127019 T
T12_GRET 30.820331 T
T12_LABR 13.435447 T
T12_PODL 12.212290 T
T13_GRET 38.480664 T
T13_LABR 76.910348 T
T14_GRET 12.169719 T
T15_GRET 37.173954 T
T15_LABR 16.559524 T
T16_GRET 10.503894 T
T17_GRET 12.452126 T
T17_LABR 27.752692 T
T18_LABR 23.490647 T
T19_GRET 19.737259 T
```