Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as
biological signals representing no or low gene expression, while others regard zeros as …

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial
omics data, including various cell states, experimental designs and feature modalities, by …

We present STARsolo, a comprehensive turnkey solution for quantifying gene expression in
single-cell/nucleus RNA-seq data, built into RNA-seq aligner STAR. Using simulated data …

Gene regulatory networks record relationships between transcription factors and the genes
whose expression they control. Recent computational methods have been developed to …

Inferring causal structure poses a combinatorial search problem that typically involves
evaluating structures with a score or independence test. The resulting search is costly, and …

Causal discovery methods typically extract causal relations between multiple nodes
(variables) based on univariate observations of each node. However, one frequently …

A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols
and computational methods. A solution is to use computational simulators, but existing …

Background With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq)
datasets, the number of computational tools to analyze aspects of the generated data has …

Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational
methods for analysing scRNA-seq data especially when ground truth is experimentally …

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based
causal implicit generative model for simulating single-cell RNA-seq data, in silico …