Hello,

I am new to transcriptomics, I mostly learn by online source and trial and error method. Now, I have 20 samples analyzed for miRNA. I was wondering if I can correct for the batch effects since as you can see below, there is only one sample that contains a unique batch, and for others, they have unequal distributions as well. I already did differential expression analysis using DeSeq2 and edgeR. But these two programs only take raw counts as input. Because by doing so with limma::removeBatchEffect, the data needs to be normalized.

sample  treatment   batch
1   high    4
2   high    6
3   high    6
4   high    6
5   high    6
6   high    7
7   high    8
8   high    9
9   high    9
10  high    9
11  low 6
12  low 7
13  low 8
14  low 9
15  low 9
16  low 9
17  low 9
18  low 9
19  low 9
20  low 9

dds <- DESeqDataSetFromMatrix(countData = cts, colData = meta, design = ~ batch + phenotype)

I was wondering how can I correct for batch and do differential expression analysis with my data? Thank you!