Paper · 28899397

Splatter: simulation of single-cell RNA sequencing data.

Zappia L and 2 others. Genome Biol, 2017. Cited by 604 papers, by iCite.

11 sourcespapers-2026-11-12-0.1.0

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ConductScience Library. Splatter: simulation of single-cell RNA sequencing data. Papers, record 28899397. Release papers-2026-11-12-0.1.0, 12 Nov 2026. https://conductscience.org/library/papers/28899397

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Paper

PubMed's record of the paper, with iCite's citation figures. No abstract is shown.

Title: Splatter: simulation of single-cell RNA sequencing data.; PubMed
Journal: Genome Biol; PubMed
Year: 2017; PubMed
Publication types: Journal Article, Research Support, Non-U.S. Gov't; PubMed
PubMed: https://pubmed.ncbi.nlm.nih.gov/28899397/; PubMed
DOI: https://doi.org/10.1186/s13059-017-1305-0; PubMed
PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC5596896/; PubMed
The journal: https://conductscience.org/library/journal-index/100960660; PubMed
Citations, by iCite: 604; iCite
Relative citation ratio, by iCite: 15.5; iCite
Revised in PubMed: 2020-03-06; PubMed

Authors3

The authors in the order PubMed's record lists them, with the ORCID iDs it states.

Order	Author	Forename	ORCID iD
1	Zappia L	Luke	https://orcid.org/0000-0001-7744-8565
2	Phipson B	Belinda	https://orcid.org/0000-0002-1711-7454
3	Oshlack A	Alicia	https://orcid.org/0000-0001-9788-5690

Source PubMed

Topics10

NLM's MeSH indexing of the paper, its major topics first.

MeSH term	Major
Computer Simulation	Major topic
Sequence Analysis, RNA	Major topic
Single-Cell Analysis	Major topic
Software	Major topic
Animals
Cluster Analysis
Datasets as Topic
Humans
Models, Genetic
Reproducibility of Results

Source PubMed

Grants0

Each grant PubMed's record writes, matched to the NIH project NIH's grant and paper links name for this paper, and each of those links the record does not write.

None in this release.

Trials0

The trials whose ClinicalTrials.gov record lists the paper, and the trial numbers the paper's PubMed record names.

None in this release.

Comments and corrections0

PubMed's comments and corrections, NLM's own indexing: retractions first, then expressions of concern, errata and updates, and comments last.

None in this release.

References37

The papers the reference list in PubMed's record names, those in our copy of PubMed, in the list's order.

Order	PMID	Title	Journal	Year	In Papers	Source
1	26,644,564	Human cerebral organoids recapitulate gene expression programs of fetal neocortex development.	Proc Natl Acad Sci U S A	2015	https://conductscience.org/library/papers/26644564	PubMed
2	24,227,677	featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.	Bioinformatics	2014	https://conductscience.org/library/papers/24227677	PubMed
3	28,045,081	Batch effects and the effective design of single-cell gene expression studies.	Sci Rep	2017	https://conductscience.org/library/papers/28045081	PubMed
4	25,700,174	Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.	Science	2015	https://conductscience.org/library/papers/25700174	PubMed
5	22,287,627	Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.	Nucleic Acids Res	2012	https://conductscience.org/library/papers/22287627	PubMed
6	28,346,451	SC3: consensus clustering of single-cell RNA-seq data.	Nat Methods	2017	https://conductscience.org/library/papers/28346451	PubMed
7	29,520,097	An accurate and robust imputation method scImpute for single-cell RNA-seq data.	Nat Commun	2018	https://conductscience.org/library/papers/29520097	PubMed
8	28,334,062	Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data.	Biostatistics	2017	https://conductscience.org/library/papers/28334062	PubMed
9	27,184,599	Coming of age: ten years of next-generation sequencing technologies.	Nat Rev Genet	2016	https://conductscience.org/library/papers/27184599	PubMed
10	26,925,227	Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.	F1000Res	2015	https://conductscience.org/library/papers/26925227	PubMed

1 to 10 of 37

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Cited by480

The papers in our copy of PubMed whose reference list names this one, newest first. iCite, which reads more sources, counts 604 citations.

PMID	Title	Journal	Year	In Papers	Source
42,928,215	simPIC: flexible simulation of single-cell ATAC-seq paired-insertion counts from individuals to populations.	Genome Biol	2026	https://conductscience.org/library/papers/42928215	PubMed
42,926,404	scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation.	bioRxiv	2026	https://conductscience.org/library/papers/42926404	PubMed
42,891,284	Disentangling batch effects and biological signals across conditions in single-cell transcriptome.	Patterns (N Y)	2026	https://conductscience.org/library/papers/42891284	PubMed
42,853,537	scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.	Interdiscip Sci	2026	https://conductscience.org/library/papers/42853537	PubMed
42,845,962	An imputation method for single-cell RNA sequencing data based on a parallel gated recurrent unit neural network.	Quant Biol	2026	https://conductscience.org/library/papers/42845962	PubMed
42,832,552	A graph-attentive GAN for rare-cell-aware single-cell RNA-seq data generation.	PLoS Comput Biol	2026	https://conductscience.org/library/papers/42832552	PubMed
42,830,441	Quantifying distribution shifts in single-cell data with scXMatch.	Bioinformatics	2026	https://conductscience.org/library/papers/42830441	PubMed
42,749,702	STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics.	Nat Commun	2026	https://conductscience.org/library/papers/42749702	PubMed
42,704,272	scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.	Brief Bioinform	2026	https://conductscience.org/library/papers/42704272	PubMed
42,690,276	Specificity-driven graph learning for the identification of rare cell states.	Adv Biotechnol (Singap)	2026	https://conductscience.org/library/papers/42690276	PubMed

1 to 10 of 480

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Genes2

The genes the Gene search release ties to the paper: Swiss-Prot's references first, then PubTator's text mining.

Gene	NCBI Gene ID	Linked by	In Genes	Source
CAMP	820	PubTator's text mining	https://conductscience.org/library/genes/820	NCBI Gene
LGR5	8,549	PubTator's text mining	https://conductscience.org/library/genes/8549	NCBI Gene

Drugs0

The drugs whose trials, in the Pipelines release, list the paper.

None in this release.

Data0

The data bank entries PubMed's record names. Trial numbers are under Trials.

None in this release.

Data dates

The date of each source this release read.

PubMed: 2026-11-06; PubMed
iCite: 2026-11-05; iCite
PMC's ID file: 2026-11-05; PMC's ID file
NIH RePORTER's grant and paper links: 2026-11-01; NIH RePORTER's grant and paper links
Trials: 2026-11-12
Gene search: 2026-11-11
Pipelines: 2026-09-18

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ChEMBL, through Open TargetsCC BY-SA 3.0
ClinicalTrials.govNo license stated
HGNCCC0
iCiteCC BY 4.0
NCBI GenePublic domain (US government)
NIH RePORTER's grant and paper linksPublic domain
Open TargetsCC0 1.0
PMC's ID filePublic domain (US government)
PubMedNLM's terms
PubTator 3Public domain
UniProtCC BY 4.0

Each source's terms

ChEMBL, through Open TargetsCC BY-SA 3.0, for the parts from ChEMBL. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
ClinicalTrials.govnone stated
HGNCCC0: https://www.genenames.org/about/license/
iCiteCC BY 4.0
NCBI GeneNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
NIH RePORTER's grant and paper linksPublic domain (NIH RePORTER)
Open TargetsCC0 1.0. Open Targets asks that users cite its latest paper: Buniello et al. 2025, Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery, Nucleic Acids Research 53(D1):D1467, PMID 39657122. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
PMC's ID fileNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
PubMedNLM's terms
PubTator 3Public domain
UniProtCC BY 4.0. UniProt's licence file: "All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided that this copyright statement is reproduced with each copy." UniProt asks that users cite its latest paper: The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53:D609-D617, https://doi.org/10.1093/nar/gkae1010. Read on 9 Nov 2026 at https://www.uniprot.org/help/license

ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.

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