Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data
Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to autom...
Saved in:
| Published in: | Genome Biology Vol. 23; no. 1; p. 49 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
08.02.2022
Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1474-760X, 1474-7596, 1474-760X |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background
A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.
Results
We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.
Conclusions
We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from (
https://github.com/PYangLab/scCCESS
). |
|---|---|
| AbstractList | Abstract Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. Results We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. Conclusions We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. Results We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. Conclusions We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from (https://github.com/PYangLab/scCCESS). A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.BACKGROUNDA key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods.We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.RESULTSWe systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data.We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ).CONCLUSIONSWe identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. Results We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. Conclusions We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ). |
| ArticleNumber | 49 |
| Author | Yang, Pengyi Yang, Jean Y. H. Yu, Lijia Cao, Yue |
| Author_xml | – sequence: 1 givenname: Lijia surname: Yu fullname: Yu, Lijia organization: School of Mathematics and Statistics, University of Sydney, Computational Systems Biology Group, Children’s Medical Research Institute, University of Sydney, Charles Perkins Centre, University of Sydney – sequence: 2 givenname: Yue surname: Cao fullname: Cao, Yue organization: School of Mathematics and Statistics, University of Sydney, Charles Perkins Centre, University of Sydney – sequence: 3 givenname: Jean Y. H. surname: Yang fullname: Yang, Jean Y. H. organization: School of Mathematics and Statistics, University of Sydney, Charles Perkins Centre, University of Sydney – sequence: 4 givenname: Pengyi orcidid: 0000-0003-1098-3138 surname: Yang fullname: Yang, Pengyi email: pengyi.yang@sydney.edu.au organization: School of Mathematics and Statistics, University of Sydney, Computational Systems Biology Group, Children’s Medical Research Institute, University of Sydney, Charles Perkins Centre, University of Sydney |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35135612$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkktv1DAUhS1URB_wB1igSGzYBPy2Z4NUKgqVKpAQSOwsx7mZyZDYg-0g9d_XmbTQdlGxiGPZ53y-vj7H6MAHDwi9JPgtIVq-S4RhsaoxpeWT8_gEHRGueK0k_nlwZ36IjlPaYkxWnMpn6JAJwoQk9Aj5D-DdZrTxV-_XlRumlCHOUzusQ-zzZkxV8BWk3I82zxt5A5WfxgZiFbrKwTBU-WoHqepiGKtUJAPU--VvX07rBL-ncsJsbG22z9HTzg4JXtz8T9CP84_fzz7Xl18_XZydXtZOYZ1rrQAL0CvcadE40VhqGQBnFFaACeWWrQRThDgprLBc2JZhJax2xcKAO3aCLhZuG-zW7GIpPl6ZYHuzXwhxbWzMvRvAiEbwTjUaSNNysFQrrZoOWkGxdqC6wnq_sHZTM0LrwOdoh3vQ-zu-35h1-GO0plRpWQBvbgAxlG6kbMY-zR2yHsKUDJVcayk10f8hpYowSeRMff1Aug1T9KWrRVWCUd6Y46J6dbf4v1XfJqAI9CJwMaQUoTOuz-Wlw3yXfjAEmzlsZgmbKWEz-7CZmU0fWG_pj5rYYkq7OWcQ_5X9iOsaaV7ngQ |
| CitedBy_id | crossref_primary_10_1016_j_csbj_2025_03_033 crossref_primary_10_1016_j_ajog_2024_08_040 crossref_primary_10_1093_bib_bbac387 crossref_primary_10_1038_s41593_024_01806_0 crossref_primary_10_1109_JBHI_2024_3460761 crossref_primary_10_1007_s11227_025_07263_1 crossref_primary_10_1007_s00281_022_00972_2 crossref_primary_10_1101_gr_278439_123 crossref_primary_10_1186_s12864_022_09027_0 crossref_primary_10_3390_biology14030283 crossref_primary_10_1016_j_yjmcc_2024_04_016 crossref_primary_10_1038_s41467_025_59620_6 crossref_primary_10_3390_genes14030756 crossref_primary_10_1097_JS9_0000000000002628 crossref_primary_10_1002_glia_24633 crossref_primary_10_1016_j_compbiomed_2024_108351 crossref_primary_10_1186_s13059_024_03290_y crossref_primary_10_3390_cells12151970 crossref_primary_10_1093_bib_bbae633 crossref_primary_10_1093_bib_bbad389 crossref_primary_10_1038_s42003_024_07001_y crossref_primary_10_1016_j_compbiolchem_2025_108362 crossref_primary_10_1016_j_ajhg_2025_02_014 crossref_primary_10_1093_nargab_lqaf073 crossref_primary_10_1097_COH_0000000000000809 crossref_primary_10_1007_s00170_023_12101_w crossref_primary_10_1016_j_compbiomed_2023_107414 crossref_primary_10_1038_s41522_025_00781_z crossref_primary_10_1016_j_semcancer_2023_02_005 crossref_primary_10_1186_s13059_025_03719_y crossref_primary_10_1038_s41467_025_58874_4 crossref_primary_10_1016_j_cels_2025_101294 crossref_primary_10_1016_j_csbj_2025_03_051 crossref_primary_10_1016_j_jcyt_2024_10_009 crossref_primary_10_1109_TCBBIO_2025_3534776 crossref_primary_10_1093_bib_bbad335 crossref_primary_10_1016_j_crmeth_2023_100547 crossref_primary_10_1016_j_compbiomed_2023_106939 crossref_primary_10_1007_s11427_023_2561_0 crossref_primary_10_1016_j_cotox_2024_100477 crossref_primary_10_1038_s41587_024_02161_y crossref_primary_10_1016_j_xgen_2024_100739 crossref_primary_10_1371_journal_pcbi_1012854 crossref_primary_10_1038_s41598_024_63492_z crossref_primary_10_1186_s12864_025_11511_2 crossref_primary_10_1038_s41467_022_35752_x crossref_primary_10_1093_nargab_lqae174 crossref_primary_10_1093_bib_bbae216 crossref_primary_10_1038_s41467_025_60702_8 crossref_primary_10_3389_fbinf_2025_1562410 crossref_primary_10_3390_cancers16071350 crossref_primary_10_1093_bib_bbae411 crossref_primary_10_1007_s10462_022_10357_4 crossref_primary_10_1016_j_isci_2025_112464 crossref_primary_10_1038_s41598_024_77630_0 crossref_primary_10_1186_s13059_024_03339_y crossref_primary_10_1371_journal_pgen_1011505 crossref_primary_10_1371_journal_pone_0317987 crossref_primary_10_3390_cells12131808 crossref_primary_10_1103_wjcn_l4ms crossref_primary_10_1186_s12859_024_05927_y crossref_primary_10_1002_bies_202200084 crossref_primary_10_1016_j_csbj_2023_08_005 crossref_primary_10_1186_s40104_024_01063_y crossref_primary_10_3389_fgene_2023_1166404 crossref_primary_10_1016_j_csbj_2024_11_038 crossref_primary_10_1007_s11831_025_10230_x crossref_primary_10_1093_nar_gkac781 |
| Cites_doi | 10.1007/BF02100489 10.1186/s13059-016-0927-y 10.1088/1742-5468/2008/10/P10008 10.1186/s13059-017-1188-0 10.1101/gr.254557.119 10.1186/s12859-019-3179-5 10.1038/nmeth.4402 10.1038/nmeth.4236 10.1038/s41598-020-66848-3 10.1007/s11222-007-9033-z 10.1371/journal.pcbi.1006245 10.1093/bib/bby076 10.12688/f1000research.15666.2 10.1016/0377-0427(87)90125-7 10.1016/j.cell.2021.04.048 10.1126/science.aab1601 10.1093/nar/gkz826 10.1038/s41576-018-0088-9 10.1198/016214503000000666 10.1016/j.mam.2017.07.002 10.1038/s41467-020-18416-6 10.1038/nbt.2859 10.12688/f1000research.15809.1 10.1162/089976604773717621 10.1016/S0167-8655(99)00008-2 10.1038/nature14966 10.1093/bioinformatics/btw777 10.1080/03610927408827101 10.1111/1467-9868.00293 10.1016/j.ins.2017.02.010 10.15252/msb.20199389 10.1038/s41586-018-0590-4 10.1038/s41598-019-41695-z 10.1093/bioinformatics/btz704 10.1371/journal.pgen.0020190 10.1038/s42256-020-0217-y 10.1371/journal.pcbi.1004575 10.1038/s41586-019-0969-x 10.1038/nmeth.4207 10.1093/bioinformatics/btw227 10.1038/nmeth.4380 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2022 2022. The Author(s). 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2022 – notice: 2022. The Author(s). – notice: 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 7S9 L.6 5PM DOA |
| DOI | 10.1186/s13059-022-02622-0 |
| DatabaseName | SpringerOpen Free (Free internet resource, activated by CARLI) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences Health & Medical Collection (Alumni) Medical Database Biological Science Database Proquest Central Premium ProQuest One Academic ProQuest Publicly Available Content ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic AGRICOLA AGRICOLA - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE AGRICOLA |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1474-760X |
| EndPage | 49 |
| ExternalDocumentID | oai_doaj_org_article_5b54f7b8e1bd4ea28787bfed5208ce7f PMC8822786 35135612 10_1186_s13059_022_02622_0 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: National Health and Medical Research Council grantid: 1173469 funderid: http://dx.doi.org/10.13039/501100000925 – fundername: ; grantid: 1173469 |
| GroupedDBID | --- 0R~ 29H 4.4 53G 5GY 5VS 7X7 88E 8FE 8FH 8FI 8FJ AAFWJ AAHBH AAJSJ AASML ABUWG ACGFO ACGFS ACJQM ACPRK ADBBV ADUKV AEGXH AFKRA AFPKN AHBYD AIAGR ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIAM AOIJS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BHPHI BMC BPHCQ BVXVI C6C CCPQU EBD EBLON EBS EMOBN FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK IAO IGS IHR ISR ITC KPI LK8 M1P M7P PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO ROL RPM RSV SJN SOJ SV3 UKHRP AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7XB 8FK AZQEC DWQXO GNUQQ K9. PKEHL PQEST PQUKI PRINS 7X8 7S9 L.6 5PM |
| ID | FETCH-LOGICAL-c708t-87e05e890f85bc5ba2a3ee432e9e0124a3953711c65a5a45ad3075a8c8903e4c3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 81 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000752892000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1474-760X 1474-7596 |
| IngestDate | Fri Oct 03 12:50:45 EDT 2025 Tue Nov 04 02:01:22 EST 2025 Sun Nov 09 12:35:20 EST 2025 Sun Nov 09 11:42:31 EST 2025 Tue Oct 14 12:42:28 EDT 2025 Mon Jul 21 05:45:41 EDT 2025 Sat Nov 29 04:55:58 EST 2025 Tue Nov 18 22:04:53 EST 2025 Sat Sep 06 07:17:29 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2022. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c708t-87e05e890f85bc5ba2a3ee432e9e0124a3953711c65a5a45ad3075a8c8903e4c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-1098-3138 |
| OpenAccessLink | https://doaj.org/article/5b54f7b8e1bd4ea28787bfed5208ce7f |
| PMID | 35135612 |
| PQID | 2630535140 |
| PQPubID | 2040232 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_5b54f7b8e1bd4ea28787bfed5208ce7f pubmedcentral_primary_oai_pubmedcentral_nih_gov_8822786 proquest_miscellaneous_2648866818 proquest_miscellaneous_2627136166 proquest_journals_2630535140 pubmed_primary_35135612 crossref_citationtrail_10_1186_s13059_022_02622_0 crossref_primary_10_1186_s13059_022_02622_0 springer_journals_10_1186_s13059_022_02622_0 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-02-08 |
| PublicationDateYYYYMMDD | 2022-02-08 |
| PublicationDate_xml | – month: 02 year: 2022 text: 2022-02-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Genome Biology |
| PublicationTitleAbbrev | Genome Biol |
| PublicationTitleAlternate | Genome Biol |
| PublicationYear | 2022 |
| Publisher | BioMed Central Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: Springer Nature B.V – name: BMC |
| References | T Calinski (2622_CR12) 1974; 3 R Tibshirani (2622_CR15) 2001; 63 Y Lin (2622_CR37) 2020; 16 TA Geddes (2622_CR33) 2019; 20 C Trapnell (2622_CR20) 2014; 32 A Peyvandipour (2622_CR36) 2020; 10 B Wang (2622_CR25) 2017; 14 N Patterson (2622_CR29) 2006; 2 CA Tracy (2622_CR28) 1994; 159 M Stoeckius (2622_CR39) 2017; 14 2622_CR45 VA Traag (2622_CR18) 2019; 9 2622_CR44 2622_CR47 R Kothari (2622_CR7) 1999; 20 P Lin (2622_CR11) 2017; 18 TS Andrews (2622_CR6) 2018; 59 M Guo (2622_CR16) 2015; 11 E Lord (2622_CR31) 2017; 393 S Freytag (2622_CR5) 2018; 7 D Grün (2622_CR14) 2015; 525 CR John (2622_CR26) 2020; 36 J Cao (2622_CR22) 2019; 566 CA Sugar (2622_CR8) 2003; 98 PJ Rousseeuw (2622_CR10) 1987; 20 U von Luxburg (2622_CR24) 2007; 17 2622_CR3 C Cheng (2622_CR9) 2019; 47 S Mohammadi (2622_CR19) 2020; 11 VY Kiselev (2622_CR27) 2017; 14 DJ McCarthy (2622_CR42) 2017; 33 A Duò (2622_CR4) 2018; 7 DA Cusanovich (2622_CR38) 2015; 348 NX Vinh (2622_CR46) 2010; 11 L Zappia (2622_CR43) 2018; 14 R Bacher (2622_CR1) 2016; 17 T Lange (2622_CR30) 2004; 16 2622_CR35 VY Kiselev (2622_CR2) 2019; 20 2622_CR17 S Wan (2622_CR13) 2020; 30 Y Cao (2622_CR41) 2020; 2 Y Hao (2622_CR23) 2021; 184 Tabula Muris Consortium, Overall coordination, Logistical coordination, Organ collection and processing, Library preparation and sequencing, Computational data analysis (2622_CR34) 2018; 562 J Ding (2622_CR32) 2016; 32 X Qiu (2622_CR21) 2017; 14 Y Yang (2622_CR40) 2018 |
| References_xml | – volume: 159 start-page: 151 year: 1994 ident: 2622_CR28 publication-title: Commun Math Phys. doi: 10.1007/BF02100489 – volume: 17 start-page: 63 year: 2016 ident: 2622_CR1 publication-title: Genome Biol. doi: 10.1186/s13059-016-0927-y – ident: 2622_CR17 doi: 10.1088/1742-5468/2008/10/P10008 – volume: 18 start-page: 59 year: 2017 ident: 2622_CR11 publication-title: Genome Biol. doi: 10.1186/s13059-017-1188-0 – volume: 30 start-page: 205 year: 2020 ident: 2622_CR13 publication-title: Genome Res. doi: 10.1101/gr.254557.119 – volume: 20 start-page: 660 year: 2019 ident: 2622_CR33 publication-title: BMC Bioinformatics. doi: 10.1186/s12859-019-3179-5 – volume: 14 start-page: 979 year: 2017 ident: 2622_CR21 publication-title: Nat Methods. doi: 10.1038/nmeth.4402 – volume: 14 start-page: 483 year: 2017 ident: 2622_CR27 publication-title: Nat Methods. doi: 10.1038/nmeth.4236 – volume: 10 start-page: 12349 year: 2020 ident: 2622_CR36 publication-title: Sci Rep. doi: 10.1038/s41598-020-66848-3 – volume: 17 start-page: 395 year: 2007 ident: 2622_CR24 publication-title: Stat Comput. doi: 10.1007/s11222-007-9033-z – volume: 14 year: 2018 ident: 2622_CR43 publication-title: PLoS Comput Biol. doi: 10.1371/journal.pcbi.1006245 – ident: 2622_CR44 doi: 10.1093/bib/bby076 – volume: 7 start-page: 1141 year: 2018 ident: 2622_CR4 publication-title: F1000Res. doi: 10.12688/f1000research.15666.2 – volume: 20 start-page: 53 year: 1987 ident: 2622_CR10 publication-title: J Comput Appl Math. doi: 10.1016/0377-0427(87)90125-7 – volume: 184 start-page: 3573 year: 2021 ident: 2622_CR23 publication-title: Cell. doi: 10.1016/j.cell.2021.04.048 – ident: 2622_CR3 – volume: 348 start-page: 910 year: 2015 ident: 2622_CR38 publication-title: Science. doi: 10.1126/science.aab1601 – volume: 47 year: 2019 ident: 2622_CR9 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkz826 – ident: 2622_CR45 – volume: 20 start-page: 273 year: 2019 ident: 2622_CR2 publication-title: Nat Rev Genet. doi: 10.1038/s41576-018-0088-9 – volume: 98 start-page: 750 year: 2003 ident: 2622_CR8 publication-title: J Am Stat Assoc. doi: 10.1198/016214503000000666 – ident: 2622_CR47 – ident: 2622_CR35 – volume: 59 start-page: 114 year: 2018 ident: 2622_CR6 publication-title: Mol Aspects Med. doi: 10.1016/j.mam.2017.07.002 – volume: 11 start-page: 5399 year: 2020 ident: 2622_CR19 publication-title: Nat Commun. doi: 10.1038/s41467-020-18416-6 – volume: 32 start-page: 381 year: 2014 ident: 2622_CR20 publication-title: Nat Biotechnol. doi: 10.1038/nbt.2859 – volume: 11 start-page: 2837 year: 2010 ident: 2622_CR46 publication-title: J Mach Learn Res – volume: 7 start-page: 1297 year: 2018 ident: 2622_CR5 publication-title: F1000Res. doi: 10.12688/f1000research.15809.1 – volume: 16 start-page: 1299 year: 2004 ident: 2622_CR30 publication-title: Neural Comput. doi: 10.1162/089976604773717621 – volume: 20 start-page: 405 year: 1999 ident: 2622_CR7 publication-title: Pattern Recognit Lett. doi: 10.1016/S0167-8655(99)00008-2 – volume: 525 start-page: 251 year: 2015 ident: 2622_CR14 publication-title: Nature. doi: 10.1038/nature14966 – volume: 33 start-page: 1179 year: 2017 ident: 2622_CR42 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btw777 – volume: 3 start-page: 1 year: 1974 ident: 2622_CR12 publication-title: Commun Stat Theory Methods. doi: 10.1080/03610927408827101 – volume: 63 start-page: 411 year: 2001 ident: 2622_CR15 publication-title: J R Stat Soc Series B Stat Methodol doi: 10.1111/1467-9868.00293 – volume: 393 start-page: 29 year: 2017 ident: 2622_CR31 publication-title: Inf Sci doi: 10.1016/j.ins.2017.02.010 – volume: 16 start-page: e9389 year: 2020 ident: 2622_CR37 publication-title: Mol Syst Biol doi: 10.15252/msb.20199389 – volume: 562 start-page: 367 year: 2018 ident: 2622_CR34 publication-title: Nature. doi: 10.1038/s41586-018-0590-4 – volume: 9 start-page: 5233 year: 2019 ident: 2622_CR18 publication-title: Sci Rep. doi: 10.1038/s41598-019-41695-z – volume: 36 start-page: 1159 year: 2020 ident: 2622_CR26 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btz704 – volume: 2 year: 2006 ident: 2622_CR29 publication-title: PLoS Genet. doi: 10.1371/journal.pgen.0020190 – volume: 2 start-page: 500 year: 2020 ident: 2622_CR41 publication-title: Nature Machine Intelligence doi: 10.1038/s42256-020-0217-y – volume: 11 year: 2015 ident: 2622_CR16 publication-title: PLoS Comput Biol. doi: 10.1371/journal.pcbi.1004575 – volume: 566 start-page: 496 year: 2019 ident: 2622_CR22 publication-title: Nature. doi: 10.1038/s41586-019-0969-x – volume: 14 start-page: 414 year: 2017 ident: 2622_CR25 publication-title: Nat Methods. doi: 10.1038/nmeth.4207 – volume: 32 start-page: 2567 year: 2016 ident: 2622_CR32 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btw227 – start-page: 83 volume-title: Big Data Min Anal year: 2018 ident: 2622_CR40 – volume: 14 start-page: 865 year: 2017 ident: 2622_CR39 publication-title: Nat Methods. doi: 10.1038/nmeth.4380 |
| SSID | ssj0019426 ssj0017866 |
| Score | 2.6371603 |
| Snippet | Background
A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical... A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for... Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical... BACKGROUND: A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical... Abstract Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 49 |
| SubjectTerms | Algorithms Animal Genetics and Genomics Benchmarking Bioinformatics Biomedical and Life Sciences Cluster Analysis Clustering data collection Datasets Evolutionary Biology Gene expression Gene Expression Profiling - methods genome Heuristic Human Genetics Life Sciences memory Methods Microbial Genetics and Genomics Performance evaluation Plant Genetics and Genomics RNA sequence analysis Sequence Analysis, RNA - methods Single-Cell Analysis - methods |
| SummonAdditionalLinks | – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR3LbtQw0IICEhfe0EBBRuIGURO_c0ItouK0qhBIvVmO43QrbZOy2a3E3zPjOFstj71wiRK_ZGcenhmPZwh5B0JFKL1rcqe9zEWlQu4k4LIWjoF42vBWtDHZhJ7NzNlZdZoMbkNyq5x4YmTUTe_RRn7IFMdQJKAPfLz6kWPWKDxdTSk0bpM7GCWBRde9080pgjYoq6SPSrDxqhE6IMpKTTdojDocgJHLKkfHdlBJ8Lm1S8Vg_n-TQP90pPztNDVuUicP_3d5j8iDJJ7SoxGfHpNboXtC7o0JK38-Jd0xIPX80kUDO_WLNYZZwFe3OIfRVvPLgfYdxcgdKAlDBciXdMw6QvuW4jEBRavvQPFeC0VDxSLksfjr7ChPjt3YEV1Xn5HvJ5-_ffqSp4wNudeFWQFrDYUMpipaI2sva8ccD0FwFqoAO6FwvJJcl6VX0kknpGuAxUhnPHThQXj-nOx1fRf2CW0N99qXla4NE161jjvXMl4pz1ztpM5IOUHI-hTOHLNqLGxUa4yyI1QtQNVGqNoiI-83fa7GYB47Wx8j4DctMRB3LOiX5zbRtZW1FC3MMZR1I4ID_dPoug2NZIXxQbcZOZjgbRN3GOwNsDPydlMNdI2_23WhX2MbpkuuSqV2tQH2qxTIXBl5MWLiZrYwPMfMpxnRWzi6tZztmu5iHuOLg9LFgHQy8mHC5pup__t3vdy90lfkPhsJLC_MAdlbLdfhNbnrr1cXw_JNpNtf1mJHtA priority: 102 providerName: ProQuest – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR3LbtQw0IICUi-8C4GCjMQNIhK_c2wRFacVKg_1Zk0cu7vSNkGb3Ur8PWMnWbRQKsEliuJx4oznaY9nCHmNRoUvHTQ5aCdzUSmfg0Ra1gIYmqcNDyKkYhN6NjNnZ9Wn8VBYP0W7T1uSSVIntjbqXY_SVlZ5jD5HvyFeb5JbqO5MLNhw-vnbdu-gQqUzHY-5st-OCkqZ-q8yL_-MkvxtqzRpoJN7_zf2--TuaHHSo4FEHpAbvn1I7gw1KH88Iu0x0un8AtKaOXXLTcycEG9hed6tFuv5RU-7lsZkHNG4xQY0GelQSIR2gcaVfxoXcnsaj6rQuPaw9Hl6fDo7ysdY7dgxRqM-Jl9PPnx5_zEfizDkThdmjdLSF9KbqghG1k7WwIB7LzjzlUflJoBXkuuydEqCBCGhQakhwTjswr1w_IDstV3rnxIaDHfalZWuDRNOBeAAgfFKOQY1SJ2RcpoX68YM5bFQxtImT8UoO-DRIh5twqMtMvJm2-f7kJ_jWujjON1byJhbOz3oVud2ZFUraykCjtGXdSM8oEtpdB18I1lhnNchI4cTsdiR4XvLFI-ZctBdzcirbTOyakQ3tL7bRBimS65Kpa6DQYmqFJpRGXky0N92tPh6HouZZkTvUObO7-y2tIt5ShmOfhTTBr_7dqLPX0P_O7qe_Rv4c7LPBhLPC3NI9tarjX9BbrvL9aJfvUxM-hPcnjke priority: 102 providerName: Springer Nature |
| Title | Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data |
| URI | https://link.springer.com/article/10.1186/s13059-022-02622-0 https://www.ncbi.nlm.nih.gov/pubmed/35135612 https://www.proquest.com/docview/2630535140 https://www.proquest.com/docview/2627136166 https://www.proquest.com/docview/2648866818 https://pubmed.ncbi.nlm.nih.gov/PMC8822786 https://doaj.org/article/5b54f7b8e1bd4ea28787bfed5208ce7f |
| Volume | 23 |
| WOSCitedRecordID | wos000752892000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1474-760X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017866 issn: 1474-760X databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Open Access Full Text customDbUrl: eissn: 1474-760X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019426 issn: 1474-760X databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1474-760X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019426 issn: 1474-760X databaseCode: M7P dateStart: 20150101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1474-760X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019426 issn: 1474-760X databaseCode: 7X7 dateStart: 20150101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1474-760X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019426 issn: 1474-760X databaseCode: BENPR dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1474-760X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019426 issn: 1474-760X databaseCode: PIMPY dateStart: 20150101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1474-760X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019426 issn: 1474-760X databaseCode: RSV dateStart: 20000201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZgAYkL4k1gqYzEDaJN7PiR4xbtCg5UUXmocLEc19lW6iaoaVfaf8-MkxTKY7lwsRI_UndmPJ5xJt8Q8hKMCp86O4-tciLOculjK0CWVWYZmKdzXmVVSDahJhM9m-XFT6m-MCasgwfuCHckSpFVqtQ-LeeZt2Dga1VWfi5Yop1XFWrfROWDM9W_P8hh4xk-kdHyqAVNLfIYI9fB58BybxsKaP1_MjF_j5T85XVp2IVO75I7vflIj7tp3yPXfH2f3OoSSl4-IPUYhG5xbsMBOHWrLcIg4KVdnTXr5WZx3tKmpoisgZYqNID9R7usILSpKB7jUzyVbSl-d0LxIGHl41A9nRzHfeA1DsTQ0ofk0-nJxzdv4z6jQuxUojeg-nwivM6TSovSidIyy73POPO5h50qszwXXKWpk8IKmwk7BxUgrHYwhPvM8UfkoG5q_4TQSnOnXJoDa1jmZGW5tRXjuXTMllaoiKQDgY3r4cYx68XKBLdDS9MxxQBTTGCKSSLyajfmWwe2cWXvMfJt1xOBskMFiI_pxcf8S3wicjhw3fSrtzVMcoS9Ad8zIi92zbDukNy29s0W-zDw72Uq5VV9QD1KCTZRRB53grSbLTyeY2bSiKg9Edv7O_st9XIR8L_BKWJKw---HoTxx9T_Tq6n_4Ncz8ht1q2iONGH5GCz3vrn5Ka72Czb9YhcVzMVSj0iN8Ynk2I6CutyhCG1BdQV794XX-BuOv6K5YfP3wGbtj6s |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELbKFgQX3o9AASPBCaImfueAUAtUXbVdrVCRyik4jtOttE3KPkD9U_xGZvLYannsrQcuURLbke18M57xjGcIeQlChY-dzUOrnQxFonxoJWBZC8tAPM15IYo62YQeDMzRUTJcIz-7szDoVtnxxJpR55XDPfJNpjiGIgF94N3ZtxCzRqF1tUuh0cBiz5__AJVt-rb_Af7vK8Z2Ph6-3w3brAKh05GZAfn7SHqTRIWRmZOZZZZ7LzjziQduLSxPJNdx7JS00gppcyADaY2DJtwLx-G7V8i6QLD3yPqwfzD8srBbaIPSUfuQCNYcbkKXR5mo7syOUZtTWDpkEqIrPShBeF1aF-v0AX-Tef903fzNflsvizu3_rcJvU1utgI43Woo5g5Z8-Vdcq1JyXl-j5TbQLajU1ubEKgbzzGQBN7a8TH0fjY6ndKqpBibBGV9KAAJmjZ5VWhVUDSEUNzXnlI8uUNxK2bsw_r1p8FW2LquY0N0zr1PPl_KYB-QXlmV_hGhheFOuzjRmWHCqcJyawvGE-WYzazUAYk7RKSuDdiOeUPGaa24GZU2KEoBRWmNojQKyOtFm7MmXMnK2tsItEVNDDVev6gmx2nLuVKZSVFAH32c5cJb0LCNzgqfSxYZ53URkI0OX2nL_6bpBbgC8mJRDJwLp9uWvppjHaZjrmKlVtWBBUYpkCoD8rBB_qK38HmOuV0DopdoYmk4yyXlyaiOoA5qJQNSDcibjnouuv7v6Xq8eqTPyfXdw4P9dL8_2HtCbrCGuMPIbJDebDL3T8lV9312Mp08a7kGJV8vm65-AavdpKY |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwELagHOKF-wgUMBJvEDXxnccWWIFAq4pLfbMcx-6utE2qPZD498w42YWFUgnxEkXxOLEn4_GMPf6GkOdgVITSuyZ32stcVCrkToIsa-EYmKcNjyKmZBN6PDZHR9XhL6f4U7T7ekuyP9OAKE3tcu-0if0QN2pvAZpXVjlGooMPgdeL5JLAQHr01z993ewjVDABrY_KnFlvazpKqP1nmZp_Rkz-tm2aZqPRjf_vx01yfbBE6X4vOrfIhdDeJlf63JTf75D2AOR3cuLSWjr1sxUiKuCtmx138-lycrKgXUsRpAONXigAU5L2CUZoFynuCFBc4F1QPMJCcU1iFvL0-ON4Px9iuLEiRqneJV9Gbz6_epsPyRlyrwuzBC0aChlMVUQjay9rxxwPQXAWqgCTnnC8klyXpVfSSSeka0CbSGc8VOFBeH6P7LRdGx4QGg332peVrg0TXkXHnYuMV8ozVzupM1Ku_5H1A3I5JtCY2eTBGGV7Plrgo018tEVGXmzqnPa4HedSH-Cv31Ai5nZ60M2P7TCErayliNDGUNaNCA5cTaPrGBrJCuODjhnZXQuOHRTBwjLFEUEH3NiMPNsUwxBGdrs2dCukYbrkqlTqPBrQtEqBeZWR-70sbloLr-eY5DQjektKt7qzXdJOJwlKHPwrpg189-VaVn82_e_sevhv5E_J1cPXI_vh3fj9I3KN9dKeF2aX7Cznq_CYXPbfltPF_Ekauz8AOsBE5g |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Benchmarking+clustering+algorithms+on+estimating+the+number+of+cell+types+from+single-cell+RNA-sequencing+data&rft.jtitle=Genome+biology&rft.au=Lijia+Yu&rft.au=Yue+Cao&rft.au=Jean+Y.+H.+Yang&rft.au=Pengyi+Yang&rft.date=2022-02-08&rft.pub=BMC&rft.eissn=1474-760X&rft.volume=23&rft.issue=1&rft.spage=1&rft.epage=21&rft_id=info:doi/10.1186%2Fs13059-022-02622-0&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_5b54f7b8e1bd4ea28787bfed5208ce7f |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1474-760X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1474-760X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1474-760X&client=summon |