Statistical Bioinformatics Webinar: Cao -- Benchmarking of Simulation Methods for Single-cell RNA-seq Data

Single-cell RNA-sequencing (scRNA-seq) is a powerful technique for profiling the
transcriptome at the single cell resolution and has gained tremendous popularity since
its emergence in 2009. In recent years, there has been an increasing number of
simulation tools designed specifically for simulating scRNA-seq data. For simulation data
to be useful to aid in the development of analytical algorithms, simulation methods must
generate a faithful and realistic representation of the scRNA-seq data. Using a
systematic framework, the aim of our study is to evaluate each method at capturing the
underlying biological structure of scRNA-seq datasets. In our evaluation framework,
we evaluate a total of 12 simulation methods across 35 diverse datasets with a variety of
tissue types, biological conditions and sequencing platforms. We discover that some
measures are harder to capture by current methods than others and identified areas
that could benefit from further methodological development.