{"doi":"10.1038/s41597-021-00809-x","title":"A multi-center cross-platform single-cell RNA sequencing reference dataset","abstract":"Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.","journal":"Scientific Data","year":2021,"id":213192,"datarank":0.9572260491319089,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"self_citation_contribution":0.5244761342199721,"citation_network_contribution":0.4327499149119368,"self_endowment_contribution":0.5244761342199721,"citer_contribution":0.4327499149119368,"corpus_percentile":78.88141100023208,"corpus_rank":2731,"citation_count":32,"citer_count":25,"citers_with_citation_signal":18,"citers_with_endowment":18,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9334,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":271307,"name":"Zhaowei Yang","orcid":"0000-0002-1805-4360","position":1,"is_corresponding":false},{"id":271305,"name":"Wanqiu Chen","orcid":"0000-0003-3706-7834","position":2,"is_corresponding":false},{"id":228426,"name":"Yongmei Zhao","orcid":"0000-0003-0800-4658","position":3,"is_corresponding":false},{"id":7313,"name":"Andrew J. Farmer","orcid":"0000-0002-6170-4402","position":4,"is_corresponding":false},{"id":271310,"name":"Bao Tran","orcid":"0000-0002-0261-4458","position":5,"is_corresponding":false},{"id":762771,"name":"Vyacheslav Furtak","orcid":null,"position":6,"is_corresponding":false},{"id":271311,"name":"Malcolm Moos","orcid":"0000-0002-9575-9938","position":7,"is_corresponding":false},{"id":271312,"name":"Wenming Xiao","orcid":"0000-0003-4096-9724","position":8,"is_corresponding":false},{"id":24562,"name":"Charles Wang","orcid":null,"position":9,"is_corresponding":false},{"id":271306,"name":"Xin Chen","orcid":"0000-0002-5208-2154","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-18T23:52:31.378994Z","pmid":"33531477","pmcid":"PMC7854649","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}