text: user12038: I have RNA samples sequenced with TruSeq Stranded Total RNA kit protocol in Illumina HiSeq (2x125bp) and NovaSeq platforms (2x150bp) - almost 100 samples altogether. I have to use the samples data for expression analysis. Question is how do we normalize samples from different runs and of read length? What is best way (with available packages) we can normalize the count (to overcome the bias) and do a DE analysis? ANy suggestion would be helpful.
  
  user200: There are a number of methods. If you're doing DE, you have ComBatSeq, SVAseq, RUVseq, BUSseq. You could also try Z-score normalization ($(x - \overline{x}) / \sigma$) or even quantile transformation. For the latter two, make sure you work on each batch individually, not the whole dataset at once. See more on that here.
  To visually compare if transformation works, plotting PCA/UMAP/t-SNE on raw and transformed data can perhaps be of some insight.
  
original_text: 12038: <p>I have RNA samples sequenced with TruSeq Stranded Total RNA kit protocol in Illumina HiSeq (2x125bp) and NovaSeq platforms (2x150bp) - almost 100 samples altogether. I have to use the samples data for expression analysis. Question is how do we normalize samples from different runs and of read length? What is best way (with available packages) we can normalize the count (to overcome the bias) and do a DE analysis? ANy suggestion would be helpful.</p>
   200: <p>There are a number of methods. If you're doing DE, you have ComBatSeq, SVAseq, RUVseq, BUSseq. You could also try Z-score normalization (<span class="math-container">$(x - \overline{x}) / \sigma$</span>) or even quantile transformation. For the latter two, make sure you work on each batch individually, not the whole dataset at once. See more on that <a href="https://www.nature.com/articles/s41598-020-72664-6" rel="nofollow noreferrer">here</a>.</p>
  <p>To visually compare if transformation works, plotting PCA/UMAP/t-SNE on raw and transformed data can perhaps be of some insight.</p>
  