ArticleNature communications2019
Simulating multiple faceted variability in single cell RNA sequencing.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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Who cites it
77 citing papers in PubMed.
- Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.Briefings in bioinformatics · 2026Article
- STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics.Nature communications · 2026Article
- The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime.Nucleic acids research · 2026Article
- Cross-dataset annotation harmonization for cell-type hierarchy construction.Bioinformatics (Oxford, England) · 2026Article
- Semi-supervised disentangled representation learning for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Unravelling bacterial complexity at high resolution with single-cell transcriptomics.Nature microbiology · 2026Review
- A deep adversarial network model for multi-task analysis of single-cell omics data.Briefings in bioinformatics · 2026Article
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.Briefings in bioinformatics · 2026Article
- Evaluating imputation methods for accurate estimation of cell population fractions in single-cell RNA sequencing.NAR genomics and bioinformatics · 2026Article
- Development and comparison of evaluation metrics for batch correction reveals performance differences.Bioinformatics advances · 2026Article
- Deciphering the Tumor Microenvironment Composition Using Bulk Transcriptomics: A Guide to Recent Advances and Open Challenges.Methods in molecular biology (Clifton, N.J.) · 2026Article
- From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation.bioRxiv : the preprint server for biology · 2025Article
- Interpretable phenotype decoding from multicondition sequencing data with ALPINE.Genome research · 2025Article
- SCOUT: Ornstein-Uhlenbeck modelling of gene expression evolution on single-cell lineage trees.bioRxiv : the preprint server for biology · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Simulating paired and longitudinal single-cell RNA sequencing data with rescueSim.Bioinformatics (Oxford, England) · 2025Article
- scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions.Nature methods · 2025Article
- Multi-task benchmarking of spatially resolved gene expression simulation models.Genome biology · 2025Article
- Interpretable single-cell factor decomposition using sciRED.Nature communications · 2025Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.