**10**wrote:

Hi Guys, I'm running my analysis for differential expression and the output give me no differentially expressed genes. Is there something wrong with my code or that's the true output of my samples?

First I imported the phenotype data:

```
phenoData <- read.AnnotatedDataFrame("design.txt", row.names = NULL, sep = "\t")
```

An object of class 'AnnotatedDataFrame' rowNames: 1 2 ... 6 (6 total) varLabels: Samples Treatment varMetadata: labelDescription

Then I ran the RMA:

```
eset <- justRMA("CN1_L10_T1.CEL","CN2_L10_T1.CEL","CN3_L10_T1.CEL",
"Ti50_1_L10_T1.CEL", "Ti50_2_L10_T1.CEL", "Ti50_3_L10_T1.CEL",
phenoData=phenoData)
```

I constructed the model matrix:

```
sample <- factor(rep(c("Cont", "Ti50"), each=3))
design <- model.matrix(~0 + sample)
colnames(design) <- levels(sample)
design
Cont Ti50
1 1 0
2 1 0
3 1 0
4 0 1
5 0 1
6 0 1
```

attr(,"assign") [1] 1 1 attr(,"contrasts") attr(,"contrasts")$sample [1] "contr.treatment"

I constructed the contrast matrix:

```
contrasts <- makeContrasts(Diff = Cont - Ti50, levels = design)
contrasts
```

Contrasts Levels Diff Cont 1 Ti50 -1

Then I ran the analysis:

```
fit <- lmFit(eset, design)
fit2 <- contrasts.fit(fit, contrasts)
efit <- eBayes(fit2)
deg <- topTable(efit, coef="Diff", p.value = 0.05, lfc = log2(1.5), adjust.method = "fdr", number = nrow(eset))
deg
```

data frame with 0 columns and 0 rows

I noticed that if I put p-value=1 I have 500 degs. So I investigated:

```
range(deg$adj.P.Val)
```

0.7866448 0.8526774

Indeed the threshold cuts all my genes. That's the true output ( no genes differentially expressed)? Or I did something wrong?

**20**• written 2.9 years ago by vitor.saldanha11 •

**10**

Did you inspect the MDS plot (

`plotMDS()`

function from limma)? Could you post the image here?32kThough you have used established and well used topTable function, Limma developers discourage such practice. Copy/pasted from their manual:

topTable does double filtering (by lfc and p-value(. There are/were considerable discussions among bioinformatics communities regarding this. To address this issue, limma recommends treat and topTreat method in case of filtering by fold change.

15kI second h.mon's comment: what does your data look like in the global analyses such as PCA or MDS? If you do not see a clear grouping that follows the design of your experiment, most likely you're not going to get any DE genes. The main task should then be to find out whether that's due to noise or to possibly confounding factors.

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