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Many of the modern gene-quantification tools (Salmon/Kallisto) output transcript-level (as opposed to gene-level) data. All of the scRNA-seq analysis I have seen just uses the gene-level values. I suppose I can just use the transcript-level matrix, but then I lose any links between the different isoforms of the same gene. Maybe it does not really matter?

There are a few options for bulk RNA-seq and there was a similar question here, but are there any single-cell options?

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    $\begingroup$ Single cell data tends to be very spotty, I don't know that you'll have enough reads to do a transcript level analysis from a typical sc-RNA experiment. $\endgroup$ – swbarnes2 Jan 23 '19 at 17:43
  • $\begingroup$ @swbarnes2 True, but it's spotty even on gene level. $\endgroup$ – burger Jan 23 '19 at 18:23
  • $\begingroup$ Could you please clarify the question? You want to know if using gene-level values in scRNA is fine with later methods or if it is fine to use transcript level values with later analysis? Or do you want to perform differential transcript usage and you don't know if it is fine (with what? other methods the conclusions the data?...) ? $\endgroup$ – llrs Jan 24 '19 at 14:39
  • $\begingroup$ @llrs I was trying to leave the question a little open-ended. The real question is at the end: what are DTU options for scRNA-seq? $\endgroup$ – burger Jan 24 '19 at 23:25
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I was hoping to find some tool that already exists.

The short answer is, none exist yet. I see a number of hurdles to overcome first.1

For example, no one has yet characterized saturation for transcript discovery, i.e., the reads per cell needed to maximize transcript information recovery. This is a typical stepping-stone as a sequencing approach matures, and until it's done, it makes it unlikely that most scRNA-seq datasets are going to be appropriate vis-à-vis statistical power, even if there was a tool. That is, if people are only aiming for cell-type discrimination via gene-level markers, datasets will likely be underpowered to measure transcript level differences.

Another issue is the lack of any transcript-level scRNA-seq simulators. All the simulators out there that I know of produce gene-level matrices. Having a transcript-level simulator is crucial for benchmarking and characterizing the performance of differential expression calling methodologies. Without anything to test against, that leaves a huge blindspot in terms of estimating the uncertainty (false positive rates, sensitivity) that any tool might have. However, since some scRNA-seq models (e.g., Velten, et al. 2015, or Lopez, et al., 2018) are generative, perhaps this is shouldn't be too difficult to adapt to generating FASTQs.

Simulators are fine and all, but ground truth is probably the key hurdle of the enterprise right now. While there are definitely groups using smFISH to distinguish isoforms quantitatively on a single-cell level, there doesn't appear to be enough smFISH data out there to measure a transcriptome-wide scRNA-seq method against. Hopefully this will change in the coming year or two, as results from more highly-multiplexed single-molecule technologies become available.

1 This is just my opinion formed as I try to keep up with the literature, so I could be missing things. Comments/corrections certainly welcome!

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There is a paper that takes advantage of transcript compatibility counts (TCCs). A TCC describes all of the potential transcripts that a cDNA alignment can be derived from. The senior author, Lior Pachter also has a nice blog post about differential transcript usage.

A discriminative learning approach to differential expression analysis for single-cell RNA-seq

Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3′ single-cell RNA-seq that can identify previously undetectable marker genes.

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    $\begingroup$ That's a nice paper. Unfortunately, it's more of a proof-of-concept. I was hoping to find some tool that already exists. $\endgroup$ – burger Jan 24 '19 at 23:26

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