{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:42:37Z","timestamp":1753875757558,"version":"3.41.2"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Changping Laboratory and China Postdoctoral Science Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Dimension reduction (DR) plays an important role in single-cell RNA sequencing (scRNA-seq), such as data interpretation, visualization and other downstream analysis. A desired DR method should be applicable to various application scenarios, including identifying cell types, preserving the inherent structure of data and handling with batch effects. However, most of the existing DR methods fail to accommodate these requirements simultaneously, especially removing batch effects. In this paper, we develop a novel structure-preserved dimension reduction (SPDR) method using intra- and inter-batch triplets sampling. The constructed triplets jointly consider each anchor\u2019s mutual nearest neighbors from inter-batch, k-nearest neighbors from intra-batch and randomly selected cells from the whole data, which capture higher order structure information and meanwhile account for batch information of the data. Then we minimize a robust loss function for the chosen triplets to obtain a structure-preserved and batch-corrected low-dimensional representation. Comprehensive evaluations show that SPDR outperforms other competing DR methods, such as INSCT, IVIS, Trimap, Scanorama, scVI and UMAP, in removing batch effects, preserving biological variation, facilitating visualization and improving clustering accuracy. Besides, the two-dimensional (2D) embedding of SPDR presents a clear and authentic expression pattern, and can guide researchers to determine how many cell types should be identified. Furthermore, SPDR is robust to complex data characteristics (such as down-sampling, duplicates and outliers) and varying hyperparameter settings. We believe that SPDR will be a valuable tool for characterizing complex cellular heterogeneity.<\/jats:p>","DOI":"10.1093\/bib\/bbac608","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T01:06:59Z","timestamp":1673399219000},"source":"Crossref","is-referenced-by-count":0,"title":["Structure-preserved dimension reduction using joint triplets sampling for multi-batch integration of single-cell transcriptomic data"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5081-625X","authenticated-orcid":false,"given":"Xinyi","family":"Xu","sequence":"first","affiliation":[{"name":"Central University of Finance and Economics School of Statistics and Mathematics, , Beijing, 100081 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9600-8984","authenticated-orcid":false,"given":"Xiangjie","family":"Li","sequence":"additional","affiliation":[{"name":"Changping Laboratory , Beijing, 102206 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"2023011917132644500_ref1","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive integration of single-cell data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"},{"key":"2023011917132644500_ref2","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2023011917132644500_ref3","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2023011917132644500_ref4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"SCANPY: large-scale single-cell gene expression data analysis","volume":"19","author":"Wolf","year":"2018","journal-title":"Genome Biol"},{"key":"2023011917132644500_ref5","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/nmeth.4236","article-title":"SC3: consensus clustering of single-cell RNA-seq data","volume":"14","author":"Kiselev","year":"2017","journal-title":"Nat Methods"},{"key":"2023011917132644500_ref6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13059-017-1188-0","article-title":"CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data","volume":"18","author":"Lin","year":"2017","journal-title":"Genome Biol"},{"key":"2023011917132644500_ref7","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1073\/pnas.1817715116","article-title":"Semisoft clustering of single-cell data","volume":"116","author":"Zhu","year":"2019","journal-title":"PNAS"},{"key":"2023011917132644500_ref8","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1093\/bioinformatics\/btv715","article-title":"Destiny: diffusion maps for large-scale single-cell data in R","volume":"32","author":"Angerer","year":"2016","journal-title":"Bioinformatics"},{"key":"2023011917132644500_ref9","doi-asserted-by":"crossref","first-page":"giz087","DOI":"10.1093\/gigascience\/giz087","article-title":"ascend: R package for analysis of single-cell RNA-seq data","volume":"8","author":"Senabouth","year":"2019","journal-title":"GigaScience"},{"key":"2023011917132644500_ref10","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nbt.2859","article-title":"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells","volume":"32","author":"Trapnell","year":"2014","journal-title":"Nat Biotechnol"},{"key":"2023011917132644500_ref11","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13059-016-0975-3","article-title":"SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data","volume":"17","author":"Welch","year":"2016","journal-title":"Genome Biol"},{"key":"2023011917132644500_ref12","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","article-title":"Deep generative modeling for single-cell transcriptomics","volume":"15","author":"Lopez","year":"2018","journal-title":"Nat Methods"},{"key":"2023011917132644500_ref13","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1038\/s41592-019-0494-8","article-title":"scGen predicts single-cell perturbation responses","volume":"16","author":"Lotfollahi","year":"2019","journal-title":"Nat Methods"},{"key":"2023011917132644500_ref14","doi-asserted-by":"crossref","first-page":"2338","DOI":"10.1038\/s41467-020-15851-3","article-title":"Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis","volume":"11","author":"Li","year":"2020","journal-title":"Nat Commun"},{"key":"2023011917132644500_ref15","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1101\/gr.271874.120","article-title":"A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics","volume":"31","author":"Lakkis","year":"2021","journal-title":"Genome Res"},{"key":"2023011917132644500_ref16","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","article-title":"Fast, sensitive and accurate integration of single-cell data with harmony","volume":"16","author":"Korsunsky","year":"2019","journal-title":"Nat Methods"},{"key":"2023011917132644500_ref17","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1038\/nbt.4091","article-title":"Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors","volume":"36","author":"Haghverdi","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2023011917132644500_ref18","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1038\/s41587-019-0113-3","article-title":"Efficient integration of heterogeneous single-cell transcriptomes using Scanorama","volume":"37","author":"Hie","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2023011917132644500_ref19","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1038\/s41592-019-0466-z","article-title":"Joint analysis of heterogeneous single-cell RNA-seq dataset collections","volume":"16","author":"Barkas","year":"2019","journal-title":"Nat Methods"},{"key":"2023011917132644500_ref20","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1016\/j.cell.2019.05.006","article-title":"Single-cell multi-omic integration compares and contrasts features of brain cell identity","volume":"177","author":"Welch","year":"2019","journal-title":"Cell"},{"key":"2023011917132644500_ref21","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1038\/s42256-021-00361-8","article-title":"Integration of millions of transcriptomes using batch-aware triplet neural networks","volume":"3","author":"Simon","year":"2021","journal-title":"Nat Mach Intell"},{"key":"2023011917132644500_ref22","doi-asserted-by":"crossref","first-page":"8914","DOI":"10.1038\/s41598-019-45301-0","article-title":"Structure-preserving visualisation of high dimensional single-cell datasets","volume":"9","author":"Szubert","year":"2019","journal-title":"Sci Rep"},{"article-title":"TriMap: Large-scale Dimensionality Reduction Using Triplets","year":"2019","author":"Amid","key":"2023011917132644500_ref23"},{"key":"2023011917132644500_ref24","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1101\/gr.254557.119","article-title":"SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection","volume":"30","author":"Wan","year":"2020","journal-title":"Genome Res"},{"key":"2023011917132644500_ref25","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1038\/s41467-018-07931-2","article-title":"Single-cell RNA-seq denoising using a deep count autoencoder","volume":"10","author":"Eraslan","year":"2019","journal-title":"Nat Commun"},{"key":"2023011917132644500_ref26","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s13059-015-0805-z","article-title":"ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis","volume":"16","author":"Pierson","year":"2015","journal-title":"Genome Biol"},{"key":"2023011917132644500_ref27","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41467-017-02554-5","article-title":"A general and flexible method for signal extraction from single-cell RNA-seq data","volume":"9","author":"Risso","year":"2018","journal-title":"Nat Commun"},{"key":"2023011917132644500_ref28","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1038\/s41592-019-0353-7","article-title":"Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning","volume":"16","author":"Deng","year":"2019","journal-title":"Nat Methods"},{"key":"2023011917132644500_ref29","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1038\/s41467-018-04368-5","article-title":"Interpretable dimensionality reduction of single cell transcriptome data with deep generative models","volume":"9","author":"Ding","year":"2018","journal-title":"Nat Commun"},{"key":"2023011917132644500_ref30","doi-asserted-by":"crossref","first-page":"5261","DOI":"10.1038\/s41467-021-25534-2","article-title":"Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data","volume":"12","author":"Zhao","year":"2021","journal-title":"Nat Commun"},{"article-title":"UMAP: uniform manifold approximation and projection for dimension reduction","year":"2018","author":"McInnes","key":"2023011917132644500_ref31"},{"key":"2023011917132644500_ref32","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell RNA sequencing data","volume":"18","author":"Zappia","year":"2017","journal-title":"Genome Biol"},{"key":"2023011917132644500_ref33","doi-asserted-by":"crossref","DOI":"10.1101\/2020.05.22.111161","article-title":"Benchmarking atlas-level data integration in single-cell genomics","author":"Luecken","year":"2020"},{"key":"2023011917132644500_ref34","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/s41587-020-0465-8","article-title":"Systematic comparison of single-cell and single-nucleus RNA-sequencing methods","volume":"38","author":"Ding","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2023011917132644500_ref35","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1016\/j.cell.2019.01.004","article-title":"Molecular classification and comparative Taxonomics of Foveal and peripheral cells in primate retina","volume":"176","author":"Peng","year":"2019","journal-title":"Cell"},{"key":"2023011917132644500_ref36","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/nbt.4042","article-title":"Multiplexed droplet single-cell RNA-sequencing using natural genetic variation","volume":"36","author":"Kang","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2023011917132644500_ref37","first-page":"5888","article-title":"Major differences in the responses of primary human leukocyte subsets to IFN-\u03b2","volume":"185","author":"Boxel-Dezaire","year":"2010","journal-title":"JI"},{"key":"2023011917132644500_ref38","first-page":"17","article-title":"Self-tuning spectral clustering","author":"Zelnik-Manor","year":"2004","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023011917132644500_ref39","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1038\/s41592-018-0254-1","article-title":"A test metric for assessing single-cell RNA-seq batch correction","volume":"16","author":"B\u00fcttner","year":"2019","journal-title":"Nat Methods"},{"key":"2023011917132644500_ref40","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13059-019-1850-9","article-title":"A benchmark of batch-effect correction methods for single-cell RNA sequencing data","volume":"21","author":"Tran","year":"2020","journal-title":"Genome Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac608\/48783167\/bbac608.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac608\/48783167\/bbac608.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T17:40:21Z","timestamp":1674150021000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac608\/6982727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac608","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2023,1]]},"published":{"date-parts":[[2023,1]]},"article-number":"bbac608"}}