Hi,

I'm trying to use DESeq2 to perform time series analysis like a one-way ANOVA on multi-groups of samples,but there is a obvious batch effect in my data .

I would like to generate a single p-value that indicates difference among the time without batch effect.

and this is my coldata:

• List item

sample condition batch 2h1 2h one 2h2 2h one 2h3 2h one 2h4 2h two 2h5 2h two 2h6 2h two 4h1 4h one 4h2 4h one 4h3 4h one 4h4 4h two 4h5 4h two 4h6 4h two 6h1 6h one 6h2 6h one 6h3 6h one 6h4 6h two 6h5 6h two 6h6 6h two 8h1 8h one 8h2 8h one 8h3 8h one 8h4 8h two 8h5 8h two 8h6 8h two 10h1 10h one 10h2 10h one 10h3 10h one 10h4 10h two 10h5 10h two 10h6 10h two 12h1 12h one 12h2 12h one 12h3 12h one 12h4 12h two 12h5 12h two 12h6 12h two

and I run code :

ddsHTSeq1<- DESeqDataSetFromMatrix(counts, colData, design =~batch+condition) dds<-DESeq(ddsHTSeq1,full=~ batch + condition, reduced=~1+batch, test="LRT") estimating size factors estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing results(dds) log2 fold change (MLE): condition 10h vs 12h LRT p-value: '~ batch + condition' vs '~ 1 + batch' DataFrame with 54446 rows and 6 columns baseMean log2FoldChange lfcSE stat pvalue padj <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> ENSMUSG00000000001.4 1049.9401894 0.08067067 0.07007668 6.682485 0.245345266 0.39261103

So is it correct?

Yes, this LRT will give you the genes that appear to change across condition.