{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:35Z","timestamp":1772138015846,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"SyMeC Project","award":["BT\/Med-II\/NIBMG\/SyMeC\/2014"],"award-info":[{"award-number":["BT\/Med-II\/NIBMG\/SyMeC\/2014"]}]},{"DOI":"10.13039\/501100010803","name":"Department of Biotechnology","doi-asserted-by":"publisher","award":["SRG\/2020\/000072"],"award-info":[{"award-number":["SRG\/2020\/000072"]}],"id":[{"id":"10.13039\/501100010803","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. Since single-cell data are susceptible to technical noise, the quality of genes selected prior to clustering is of crucial importance in the preliminary steps of downstream analysis. Therefore, interest in robust gene selection has gained considerable attention in recent years. We introduce sc-REnF [robust entropy based feature (gene) selection method], aiming to leverage the advantages of $R{\\prime}{e}nyi$ and $Tsallis$ entropies in gene selection for single cell clustering. Experiments demonstrate that with tuned parameter ($q$), $R{\\prime}{e}nyi$ and $Tsallis$ entropies select genes that improved the clustering results significantly, over the other competing methods. sc-REnF can capture relevancy and redundancy among the features of noisy data extremely well due to its robust objective function. Moreover, the selected features\/genes can able to determine the unknown cells with a high accuracy. Finally, sc-REnF yields good clustering performance in small sample, large feature scRNA-seq data. Availability: The sc-REnF is available at https:\/\/github.com\/Snehalikalall\/sc-REnF<\/jats:p>","DOI":"10.1093\/bib\/bbab517","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T15:16:19Z","timestamp":1638371779000},"source":"Crossref","is-referenced-by-count":19,"title":["sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data"],"prefix":"10.1093","volume":"23","author":[{"given":"Snehalika","family":"Lall","sequence":"first","affiliation":[{"name":"Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, West Bengal, India"}]},{"given":"Abhik","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Interdisciplinary Statistical Research Unit, Kolkata, 700108, West Bengal, India"}]},{"given":"Sumanta","family":"Ray","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Aliah University, Kolkata, India"},{"name":"Health Analytics Network, PA, USA"}]},{"given":"Sanghamitra","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, West Bengal, India"}]}],"member":"286","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"issue":"4","key":"2022031506224647600_ref1","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/nprot.2017.149","article-title":"Exponential scaling of single-cell rna-seq in the past decade","volume":"13","author":"Svensson","year":"2018","journal-title":"Nat Protoc"},{"issue":"1","key":"2022031506224647600_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"L\u00e4hnemann","year":"2020","journal-title":"Genome Biol"},{"issue":"1","key":"2022031506224647600_ref3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1755-8794-3-21","article-title":"Seurat: visual analytics for the integrated analysis of microarray data","volume":"3","author":"Gribov","year":"2010","journal-title":"BMC Med Genomics"},{"issue":"5","key":"2022031506224647600_ref4","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":"2022031506224647600_ref5","article-title":"A copula based topology preserving graph convolution network for clustering of single-cell RNA seq data","volume-title":"bioRxiv","author":"","year":"2021"},{"issue":"6391","key":"2022031506224647600_ref6","doi-asserted-by":"crossref","DOI":"10.1126\/science.aaq1736","article-title":"Cell type transcriptome atlas for the planarian schmidtea mediterranea","volume":"360","author":"Fincher","year":"2018","journal-title":"Science"},{"issue":"6391","key":"2022031506224647600_ref7","doi-asserted-by":"crossref","DOI":"10.1126\/science.aaq1723","article-title":"Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics","volume":"360","author":"Plass","year":"2018","journal-title":"Science"},{"key":"2022031506224647600_ref8","doi-asserted-by":"crossref","DOI":"10.1101\/2020.09.22.307512","article-title":"Markercapsule: Explainable single cell typing using capsule networks","author":"Ray","year":"2020"},{"issue":"1","key":"2022031506224647600_ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1874-1","article-title":"Normalization and variance stabilization of single-cell rna-seq data using regularized negative binomial regression","volume":"20","author":"Hafemeister","year":"2019","journal-title":"Genome Biol"},{"issue":"3","key":"2022031506224647600_ref10","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/nmeth.4150","article-title":"Single-cell mrna quantification and differential analysis with census","volume":"14","author":"Qiu","year":"2017","journal-title":"Nat Methods"},{"issue":"6","key":"2022031506224647600_ref11","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004333","article-title":"Basics: Bayesian analysis of single-cell sequencing data","volume":"11","author":"Vallejos","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"22","key":"2022031506224647600_ref12","doi-asserted-by":"crossref","first-page":"e179","DOI":"10.1093\/nar\/gkx828","article-title":"Linnorm: improved statistical analysis for single cell rna-seq expression data","volume":"45","author":"Yip","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2022031506224647600_ref13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","author":"Hao","year":"2021","journal-title":"Cell"},{"issue":"5","key":"2022031506224647600_ref14","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"Satija","year":"2015","journal-title":"Nat Biotechnol"},{"issue":"1","key":"2022031506224647600_ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"Zheng","year":"2017","journal-title":"Nat Commun"},{"issue":"6","key":"2022031506224647600_ref16","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J Educ Psychol"},{"key":"2022031506224647600_ref17","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.mam.2017.07.002","article-title":"Identifying cell populations with scrnaseq","volume":"59","author":"Andrews","year":"2018","journal-title":"Mol Aspects Med"},{"key":"2022031506224647600_ref18","first-page":"5","article-title":"A step-by-step workflow for low-level analysis of single-cell rna-seq data with bioconductor","author":"Lun","year":"2016","journal-title":"F1000Research"},{"issue":"8","key":"2022031506224647600_ref19","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1093\/bioinformatics\/btw777","article-title":"Scater: pre-processing, quality control, normalization and visualization of single-cell rna-seq data in r","volume":"33","author":"McCarthy","year":"2017","journal-title":"Bioinformatics"},{"key":"2022031506224647600_ref20","doi-asserted-by":"crossref","first-page":"e1009464","DOI":"10.1371\/journal.pcbi.1009464","article-title":"RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data","volume":"17","author":"Lall","year":"2021","journal-title":"PLoS computational biology"},{"issue":"1","key":"2022031506224647600_ref21","first-page":"1","article-title":"Feature selection and dimension reduction for single-cell rna-seq based on a multinomial model","volume":"20","author":"William Townes","year":"2019","journal-title":"Genome Biol"},{"issue":"5","key":"2022031506224647600_ref22","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"},{"issue":"1","key":"2022031506224647600_ref23","first-page":"1","article-title":"Single-cell entropy for accurate estimation of differentiation potency from a cell\u2019s transcriptome","volume":"8","author":"Enver","year":"2017","journal-title":"Nat Commun"},{"issue":"3","key":"2022031506224647600_ref24","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TIP.2012.2219544","article-title":"Additive white gaussian noise level estimation in svd domain for images","volume":"22","author":"Liu","year":"2012","journal-title":"IEEE Trans Image Process"},{"key":"2022031506224647600_ref25","article-title":"Generating realistic cell samples for gene selection in scrna-seq data: A novel generative framework","author":"Ray","year":"2021"},{"issue":"1","key":"2022031506224647600_ref26","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/18.61115","article-title":"Divergence measures based on the shannon entropy","volume":"37","author":"Lin","year":"1991","journal-title":"IEEE Transactions on Information theory"},{"issue":"1","key":"2022031506224647600_ref27","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s13059-016-1010-4","article-title":"Giniclust: detecting rare cell types from single-cell gene expression data with gini index","volume":"17","author":"Jiang","year":"2016","journal-title":"Genome Biol"},{"issue":"7","key":"2022031506224647600_ref28","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"},{"issue":"1","key":"2022031506224647600_ref29","doi-asserted-by":"crossref","first-page":"1","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"},{"issue":"3","key":"2022031506224647600_ref30","first-page":"291","article-title":"Large-scale bayesian logistic regression for text categorization","volume":"49","author":"Genkin","year":"2007","journal-title":"Dent Tech"},{"key":"2022031506224647600_ref31","first-page":"601","article-title":"Feature selection for high-dimensional genomic microarray data","volume-title":"ICML","author":"Xing","year":"2001"},{"key":"2022031506224647600_ref32","doi-asserted-by":"crossref","first-page":"107697","DOI":"10.1016\/j.patcog.2020.107697","article-title":"Stable feature selection using copula based mutual information","volume":"112","author":"","year":"2021","journal-title":"Pattern Recognition"},{"key":"2022031506224647600_ref33","first-page":"1","article-title":"CODC: a Copula-based model to identify differential coexpression","volume":"6","author":"","year":"2020","journal-title":"NPJ systems biology and applications"},{"issue":"4","key":"2022031506224647600_ref34","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1109\/TPDS.2016.2603511","article-title":"A parallel random forest algorithm for big data in a spark cloud computing environment","volume":"28","author":"Chen","year":"2016","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"issue":"Mar","key":"2022031506224647600_ref35","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"Journal of machine learning research"},{"key":"2022031506224647600_ref36","doi-asserted-by":"crossref","first-page":"39","DOI":"10.15439\/2015F121","article-title":"Comparison of decision trees with r\u00e9nyi and tsallis entropy applied for imbalanced churn dataset","volume-title":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","author":"Gajowniczek","year":"2015"},{"issue":"1","key":"2022031506224647600_ref37","doi-asserted-by":"crossref","first-page":"012331","DOI":"10.1103\/PhysRevA.89.012331","article-title":"From the quantum relative tsallis entropy to its conditional form: separability criterion beyond local and global spectra","volume":"89","author":"Rajagopal","year":"2014","journal-title":"Phys Rev A"},{"key":"2022031506224647600_ref38","first-page":"166","article-title":"On a general definition of conditional r\u00e9nyi entropies","volume-title":"Multidisciplinary Digital Publishing Institute Proceedings","author":"Ili\u0107","year":"2017"},{"issue":"7","key":"2022031506224647600_ref39","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1109\/TIT.2012.2192713","article-title":"Conditional r\u00e9nyi entropies","volume":"58","author":"Teixeira","year":"2012","journal-title":"IEEE Transactions on Information Theory"},{"key":"2022031506224647600_ref40","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1007\/978-3-319-91253-0_68","article-title":"Multi-class and cluster evaluation measures based on renyi and tsallis entropies and mutual information","volume-title":"International Conference on Artificial Intelligence and Soft Computing","author":"Villmann","year":"2018"},{"key":"2022031506224647600_ref41","author":"Arimoto","year":"1977","journal-title":"Topics in information theory"},{"key":"2022031506224647600_ref42","article-title":"Revisiting conditional r\u00e9nyi entropies and generalizing shannons bounds in information theoretically secure encryption","author":"Iwamoto","year":"2013","journal-title":"Technical report, Cryptology ePrint Archive 440\/2013"},{"issue":"3","key":"2022031506224647600_ref43","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.68.031101","article-title":"Geometry of escort distributions","volume":"68","author":"Abe","year":"2003","journal-title":"Physical Review E"},{"issue":"4","key":"2022031506224647600_ref44","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TIT.2021.3054980","article-title":"A scale-invariant generalization of the r\u00e9nyi entropy, associated divergences and their optimizations under tsallis\u2019 nonextensive framework","volume":"67","author":"Ghosh","year":"2021","journal-title":"IEEE Transactions on Information Theory"},{"issue":"9","key":"2022031506224647600_ref45","doi-asserted-by":"crossref","first-page":"4924","DOI":"10.1109\/TIT.2016.2595586","article-title":"Projection theorems for the r\u00e9nyi divergence on $\\alpha $ -convexsets","volume":"62","author":"Ashok Kumar","year":"2016","journal-title":"IEEE Transactions on Information Theory"},{"key":"2022031506224647600_ref46","volume-title":"Introduction to nonextensive statistical mechanics: approaching a complex world","author":"Tsallis","year":"2009"},{"issue":"9","key":"2022031506224647600_ref47","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1038\/nsmb.2660","article-title":"Single-cell rna-seq profiling of human preimplantation embryos and embryonic stem cells","volume":"20","author":"Yan","year":"2013","journal-title":"Nat Struct Mol Biol"},{"issue":"5","key":"2022031506224647600_ref48","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"issue":"6282","key":"2022031506224647600_ref49","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1126\/science.aad0501","article-title":"Dissecting the multicellular ecosystem of metastatic melanoma by single-cell rna-seq","volume":"352","author":"Tirosh","year":"2016","journal-title":"Science"},{"issue":"6","key":"2022031506224647600_ref50","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41592-019-0425-8","article-title":"Benchmarking single cell rna-sequencing analysis pipelines using mixture control experiments","volume":"16","author":"Tian","year":"2019","journal-title":"Nat Methods"},{"issue":"9","key":"2022031506224647600_ref51","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1038\/nmeth.4380","article-title":"Simultaneous epitope and transcriptome measurement in single cells","volume":"14","author":"Stoeckius","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2022031506224647600_ref52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-21650-1","article-title":"Identication of leukemic and pre-leukemic stem cells by clonal tracking from singlecell transcriptomics","volume":"12","author":"Velten","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"2022031506224647600_ref53","doi-asserted-by":"crossref","first-page":"1","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"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/2\/bbab517\/42805108\/bbab517.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/2\/bbab517\/42805108\/bbab517.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T08:08:49Z","timestamp":1699862929000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab517\/6509050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,17]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3,10]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab517","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-355014\/v1","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,3]]},"published":{"date-parts":[[2022,1,17]]},"article-number":"bbab517"}}