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Peer-reviewedCC BYGenome biology

simPIC: flexible simulation of single-cell ATAC-seq paired-insertion counts from individuals to populations.

  • Sagrika Chugh1,2
  • Heejung Shim2,3,4
  • Davis J McCarthy5,6,7
7 affiliations
  1. 1Bioinformatics and Cellular Genomics, St. Vincent's Institute of Medical Research, 9 Princes Street, 3065, Fitzroy, Victoria, Australia.
  2. 2School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Grattan Street, 3010, Parkville, Victoria, Australia.
  3. 3Snow Vision Accelerator, Sydney Precision Data Science Centre, School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia.
  4. 4Melbourne Integrative Genomics, ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems, School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, Victoria, Australia.
  5. 5Bioinformatics and Cellular Genomics, St. Vincent's Institute of Medical Research, 9 Princes Street, 3065, Fitzroy, Victoria, Australia. [email protected].
  6. 6School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Grattan Street, 3010, Parkville, Victoria, Australia. [email protected].
  7. 7Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Grattan Street, 3010, Parkville, Victoria, Australia. [email protected].
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Abstract

Single-cell Assay for Transposase Accessible Chromatin (scATAC-seq) is increasingly used at population scale to study how genetic variation shapes chromatin accessibility. Method development is limited by the lack of flexible simulation tools with known ground truth. Here, we present simPIC, a fast, memory-efficient framework for simulating realistic single-cell ATAC-seq count data across individuals and populations. simPIC models cell groups, batch effects, and genotype-dependent accessibility variation, enabling evaluation of population-scale methods, including chromatin accessibility quantitative trait locus mapping. Across multiple datasets and cell types, simPIC closely matches real data distributions while scaling to cohort sizes impractical for current tools.

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Cite

BibTeXRIS
BibTeX
@article{chugh2026simpic,
  title = {{simPIC: flexible simulation of single-cell ATAC-seq paired-insertion counts from individuals to populations.}},
  author = {Sagrika Chugh and Heejung Shim and Davis J McCarthy},
  journal = {Genome biology},
  year = {2026},
  month = jul,
  doi = {10.1186/s13059-026-04199-4},
  url = {https://europepmc.org/article/MED/42928215},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - simPIC: flexible simulation of single-cell ATAC-seq paired-insertion counts from individuals to populations.
AU  - Sagrika Chugh
AU  - Heejung Shim
AU  - Davis J McCarthy
PY  - 2026
DA  - 2026/07/29
JO  - Genome biology
DO  - 10.1186/s13059-026-04199-4
UR  - https://europepmc.org/article/MED/42928215
AB  - Single-cell Assay for Transposase Accessible Chromatin (scATAC-seq) is increasingly used at population scale to study how genetic variation shapes chromatin accessibility. Method development is limited by the lack of flexible simulation tools with known ground truth. Here, we present simPIC, a fast, memory-efficient framework for simulating realistic single-cell ATAC-seq count data across individuals and populations. simPIC models cell groups, batch effects, and genotype-dependent accessibility variation, enabling evaluation of population-scale methods, including chromatin accessibility quantitative trait locus mapping. Across multiple datasets and cell types, simPIC closely matches real data distributions while scaling to cohort sizes impractical for current tools.
SN  - 1474-7596
SN  - 1474-760X
ER  -