Hi All,

I am wondering if anyone estimated transcript-level counts from plate based scRNA datasets like smart-seq that capture full length transcripts instead of 3' ends. I tried to use salmon but the estimated counts/TPMs seems to  be highly inflated.

For example, for this transcript, the output from a single cell looks like this.

    Name    Length  EffectiveLength TPM     NumReads
    ENST00000344843.7       721     250     247.016 471.945

When I looked the same cell bam file in IGV, that transcript has only around 50 reads mapped. Salmon `NumReads` is 471. This happens for a lot of transcripts.

![enter image description here][1]


I am wondering why the values are inflated ? One potential reason could be due to the default scaling factor used, as for bulk-rna where the total counts tend to be in millions.

I would like to know if I anyone estimated the transcript counts from scRNA before. There are tools like [Alevin][2] but they seem to work only with 3' enriched droplet based methods.

Thanks,

Goutham A


  [1]: https://i.ibb.co/202n9qQ/salmon.png
  [2]: https://salmon.readthedocs.io/en/latest/alevin.html
Since this is full length without UMIs, salmon would be the tool to use here.  Out of curiosity, how did you obtain the pile-up?  How do you know those are all reads coming from this transcript?  Is it a single transcript gene?  You can always align the reads with STAR and get a bam file using `—quantMode TranscriptomeSAM` if you want quantification from those alignments.