Hello,

I am performing an RNAseq analysis using the limma package.

From the same cell culture 3 samples(lane) were extracted, these samples were analysed at 4 different time points(Time).

No treatment was applied to any of the samples.

The goal of the project is to compare the 4 time points to each other, initially with a DEG.

The result of my analysis produces far too many differentially expressed genes with very low adj_p_values (even more than 100 genes with an adj_p_value < 0.0001).

I am also having difficulty understanding how I should consider the replicates, whether as technical or biological replicates.

Am I doing something wrong in setting up my model?

This is an example of my data:

Samples | Lane | Time_Point |
---|---|---|

S1 | A | T1 |

S2 | B | T1 |

S3 | C | T1 |

S4 | A | T2 |

S5 | B | T2 |

S6 | C | T2 |

S7 | A | T3 |

S8 | B | T3 |

S9 | C | T3 |

S10 | A | T4 |

S11 | B | T4 |

S12 | C | T4 |

**This is my code:**

```
dge = DGEList(counts = exp.f, genes = rownames(exp.f))
### Preprocessing
dge$counts <- edgeR::cpm(dge, log = F)
dge = calcNormFactors(dge, method = "TMM")
### Define model
f = as.factor(dataset$Time)
l = as.factor(dataset$Lane)
design = model.matrix(~0+f)
colnames(design) <- c(levels(f))
v = voomWithQualityWeights(dge, design = design, plot = TRUE)
corfit <- duplicateCorrelation(v, design, block = l, ndups = 1)
v = voomWithQualityWeights(dge, design = design, plot = TRUE, correlation = corfit$consensus.correlation)
corfit <- duplicateCorrelation(v, design, block = l, ndups = 1)
### Fit model
fit = lmFit(v, design, block = l, correlation = corfit$consensus.correlation)
contrast.matrix = makeContrasts(T4-T3,T3-T2,T2-T1,T4-T1, levels = design)
fit2 = contrasts.fit(fit, contrast.matrix)
fit2 = eBayes(fit2)
```

Thanks,

Alessandro

Hi gordon,

Sorry for only replying now.

Thank you very much for your reply, I have tried to do as you suggested.

If I can give you more information about what you are not clear about the experiment I can rewrite it to better understand if my method is correct.

Thank you very much.