Answers.Body: <p>Use raw counts as much as you can. Add in various relevant factors as covariates in DESeq2. RNAseq metrics have come a long way but are still misused by people because it's more convenient to compare some sort of normalized metric that give out relevant confounding factors, I guess.</p>
  
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Answers.Text: Thank you for your comment！Just curious, raw count data does not account for sequencing depth, but my intuitive assumption is that accounting for sequencing depth is necessary for the comparison of gene expression between samples. Why using count data is more appropriate than TPM or other methods? Thank you!
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Answers.Text: DESeq2 will account for library depth. Comparing anything between two samples is not robust, one should compare between groups of samples using statistically sound methods. You can of course compare TPM but it leaves room for false results.
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Answers.Body: <p>For scRNA-seq, you don't want to normalize for gene length because the most popular 10X technology only sequences a the 3' or 5' end of the transcript. Hence CPM, which normalizes for sequencing depth alone, is popular for single cell data.</p>
  <p>This is different from your average bulk RNA-seq, where you typically get full length transcripts (unless you're doing something like DRUG-seq).</p>
  
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Body: <p>It is shown that TPM  values are not suitable for DEG analysis but good for within-sample comparison since TPM normalized the gene length. My question is first: if TPM is not suitable for across sample comparison, then why are some heatmaps using TPM values?  In what scenario are TPM values really useful? Should downstream analysis use TPM or raw count matrix?</p>
  <p>When I analyzed sc-RNA seq data, the Seurat package uses normalized counts (which is CPM) for DEG, but TPM is not advised for differential testing. I'm not sure what I'm missing in the understanding. It seems very convoluted to me.</p>
  <p>I just don't get the point that TPM is commonly used as an input for DEG testing by Seurat (Seurat findmarker function uses &quot;data&quot; slot, which is normalized counts), but TPM is not recommended for DEG testing for bulk.</p>
  
Tags: <rna-seq><scrnaseq><tpm>
Title: The confusion of using TPM (transcripts per million)
ThreadText: # The confusion of using TPM (transcripts per million)
  
  (Asked by: username_0 on 2021-07-13)
  
  It is shown that TPM values are not suitable for DEG analysis but good for within-sample comparison since TPM normalized the gene length. My question is first: if TPM is not suitable for across sample comparison, then why are some heatmaps using TPM values? In what scenario are TPM values really useful? Should downstream analysis use TPM or raw count matrix?
  
  When I analyzed sc-RNA seq data, the Seurat package uses normalized counts (which is CPM) for DEG, but TPM is not advised for differential testing. I'm not sure what I'm missing in the understanding. It seems very convoluted to me.
  
  I just don't get the point that TPM is commonly used as an input for DEG testing by Seurat (Seurat findmarker function uses "data" slot, which is normalized counts), but TPM is not recommended for DEG testing for bulk.
  
  **Tags:** rna-seq, scrnaseq, tpm
  
  
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  ## 2 answers
  
  ### Answer by username_1 on 2021-07-13. Score: 4
  
  Use raw counts as much as you can. Add in various relevant factors as covariates in DESeq2. RNAseq metrics have come a long way but are still misused by people because it's more convenient to compare some sort of normalized metric that give out relevant confounding factors, I guess.
  
  > **Comments:**
  > **username_0**: Thank you for your comment！Just curious, raw count data does not account for sequencing depth, but my intuitive assumption is that accounting for sequencing depth is necessary for the comparison of gene expression between samples. Why using count data is more appropriate than TPM or other methods? Thank you!  
  > **username_1**: DESeq2 will account for library depth. Comparing anything between two samples is not robust, one should compare between groups of samples using statistically sound methods. You can of course compare TPM but it leaves room for false results.  
  
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  ### Answer by username_2 on 2021-07-13. Score: 1
  
  For scRNA-seq, you don't want to normalize for gene length because the most popular 10X technology only sequences a the 3' or 5' end of the transcript. Hence CPM, which normalizes for sequencing depth alone, is popular for single cell data.
  
  This is different from your average bulk RNA-seq, where you typically get full length transcripts (unless you're doing something like DRUG-seq).
  
  
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