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

AbstractSingle-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. We carried out a benchmark for five popular isoform quantification tools. Performance was generally good when run on simulated data based on SMARTer and SMART-seq2 data, but was poor for simulated Drop-seq data. Importantly, the reduction in performance for single-cell RNA-seq compared with bulk RNA-seq was small. An important biological insight comes from our analysis of real data which showed that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells.

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Obstacles to Studying Alternative Splicing Using scRNA-seq

10.1101/797951 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jennifer Westoby ◽

Pavel Artemov ◽

Martin Hemberg ◽

Anne Ferguson-Smith

Keyword(s):

Alternative Splicing ◽

Single Cell ◽

High Rate ◽

Model Organisms ◽

Rna Seq ◽

Simulation Based ◽

Simulation Results ◽

The Impact ◽

Isoform Quantification ◽

Low Rate

AbstractBackgroundEarly 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.ResultsIn 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.ConclusionsTo 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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Splatter: simulation of single-cell RNA sequencing data

10.1101/133173 ◽

2017 ◽

Cited By ~ 8

Author(s):

Luke Zappia ◽

Belinda Phipson ◽

Alicia Oshlack

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Real Data ◽

Cell Types ◽

Rna Seq ◽

Sequencing Data ◽

Sequencing Technologies ◽

Simulation Based ◽

Single Cell Rna Sequencing ◽

Multiple Cell

AbstractAs single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.Here we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

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