Simulation-based benchmarking of isoform quantification in single-cell RNA-seq

Westoby, Jennifer; Sjöberg-Herrera, Marcela; Ferguson-Smith, Anne C.; Hemberg, Martin

doi:10.1186/s13059-018-1571-5

Cited by 27 publications

(15 citation statements)

References 53 publications

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“…Third, we simulate dropouts based on a Michaelis-Menten model described by Andrews and Hemberg [9]. Fourth, we simulate quantification errors based on isoform detection error estimates based on work by Westoby et al [8]. We repeat these four steps for every four isoform gene and cell in our simulated dataset, then calculate the mean number of isoforms detected for that gene per cell.…”

Section: Resultsmentioning

confidence: 99%

“…We have exclusively considered the problem of isoform detection using isoform quantification tools in this study. We have chosen to use isoform quantification software in preference of exon centric approaches, such as the approach used by MISO [33], because an independent benchmark of the performance of isoform quantification tools run on scRNA-seq data has been performed [8]. To the best of our knowledge, there is no independent benchmark of the performance of exon centric approaches run on scRNA-seq data.…”

Section: Discussionmentioning

confidence: 99%

“…A benchmark of isoform quantification softwares in fulllength coverage mouse scRNA-seq datasets found that the error rate of many software tools was low and comparable to bulk RNA-seq [8]. This is encouraging; however, it should be noted that the error rate is likely to be substantially higher for non-model organisms with less well-annotated genomes than the mouse genome.…”

Section: Quantification Errors Are a Relatively Minor Obstacle To Stumentioning

confidence: 96%

“…A false negative occurs when an isoform is not called as expressed by the isoform quantification software when reads from that isoform are present. Based on our previous benchmark [8], we estimate that the probability of false-positive events (pFP) is around 1% and that the probability of false negative (pFN) events is around 4% (see the "Methods" section). In our simulations in Fig.…”

Section: Quantification Errors Are a Relatively Minor Obstacle To Stumentioning

confidence: 99%

“…A recent benchmark of isoform quantification for scRNA-seq found that many isoform quantification softwares perform almost as well for full-length scRNA-seq datasets as for bulk RNA-seq [8]. Whilst this is encouraging, it is important to note that the benchmark only evaluated the ability of quantification softwares to cor-rectly assign simulated scRNA-seq reads to the transcripts that generated them.…”

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Obstacles to detecting isoforms using full-length scRNA-seq data

et al. 2020

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Background: Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. Results: In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. Conclusions: To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Quantification Errors Are a Relatively Minor Obstacle To Stumentioning

confidence: 96%

Section: Quantification Errors Are a Relatively Minor Obstacle To Stumentioning

confidence: 99%

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