Ok, actually this is good to know. However, what I meant with missing data was that data for a whole time point for one or more patients is missing. I think the manual extract means missing data in terms of count data for particular genes are missing.

To your question: As I understood it from the very recent article from Fischer et al. (https://www.ncbi.nlm.nih.gov/pubmed/30102402) ImpulseDE2 includes a normalization:

"Reference methods We used ImpulseDE2, DESeq2 and DESeq2splines on rounded expected count matrices (Supplementary Notes Section S5). We used DESeq2 in the log-likelihood ra- tio test mode in all cases. We used ImpulseDE, edge and limma on scaled data, where the scaling factor is deter- mined as the DESeq2 size factor (2). Therefore, the same library size normalization was used for ImpulseDE2, DE- Seq2, ImpulseDE, edge and limma...."

What do you think? In case you are uncertain about normalization I would recommend to do the normalization using DESeq2 like this:

    # in my case I import HTSeq count data
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design = ~ 1) 
ddsHTSeq <- ddsHTSeq[ rowSums(counts(ddsHTSeq)) > 1, ]
ddsHTSeq <- estimateSizeFactors(ddsHTSeq) # this is the normalization that is also meant in the article
counts.sf_normalized <- counts(ddsHTSeq, normalized=TRUE) # make count matrix for ImpulseDE2 from this