Given the number of simulation tools designed for single-cell RNA-seq data, it is critical to have a benchmark system that compares their relative performance under same set of data and same set of criteria

SimBench together with this website is developed for the exact purpose.

We invite all interested researchers to run their methods on the set of data we have curated, upload their result to this website and participate in this public leaderboard of scRNA-seq simulation tools

Data and instructions

- Download the set of curated data at link. Details about the data can be found in the package and can also be browsed under the shiny tab "More".

- Run your own method.

- Evaluate the simulated data against the real data across the set of criteria we have curated at link.

- Upload the result to the panel Submit your data.


SimBench article is published on Nature communications.


- est.multi.groups: whether can estimate from multiple groups.

- sim.multi.groups: whether can generate multiple groups.

- sim.custom.DE: whether can custom the differential expression pattern.

- libsize: total counts per cell.

- tmm: weighted trimmed mean of M-values normalisation factor

- efflibsize: library size multiplied by TMM

- mean.exprs: mean expression

- var.exprs: variance of expression

- scaled.var: standardisation of the variance of gene expression

- fraczerocell: fraction of zeros per cell

- fraczerogene: fraction of zeros per gene

- cell.cor: cell cell correlation

- gene.cor: gene gene correlation

- mean_var: relationship between mean and variance

- mean_fraczero: relationship between mean and fraction of zero

- libsize_fraczero: relationship between library size and fraction of zero