Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data Article Swipe
Xiaoqiao Chen
,
Sisi Chen
,
Matt Thomson
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.06.15.448478
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.06.15.448478
Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here, we introduce an active learning method (ActiveSVM) that identifies minimal but highly-informative gene sets that enable the identification of cell-types, physiological states, and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data through an iterative cell-type classification task where misclassified cells are examined at each round of analysis to identify maximally informative genes through an ‘active’ support vector machine (ActiveSVM) classifier. By focusing computational resources on misclassified cells, ActiveSVM scales to analyze data sets with over a million single cells. We demonstrate that ActiveSVM feature selection identifies gene sets that enable 90% cell-type classification accuracy across a variety of data sets including cell atlas and disease characterization data sets. The method generalizes to reveal genes that respond to genetic perturbations and to identify region specific gene expression patterns in spatial transcriptomics data. The discovery of small but highly informative gene sets should enable substantial reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery, and genetic screens.
Related Topics
Concepts
Computational biology
Gene
Classifier (UML)
Gene expression profiling
Cell type
Biology
Feature selection
Genetics
Computer science
Gene expression
Artificial intelligence
Cell
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.06.15.448478
- https://www.biorxiv.org/content/biorxiv/early/2022/02/12/2021.06.15.448478.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3170554617
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3170554617Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2021.06.15.448478Digital Object Identifier
- Title
-
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq dataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-16Full publication date if available
- Authors
-
Xiaoqiao Chen, Sisi Chen, Matt ThomsonList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.06.15.448478Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/02/12/2021.06.15.448478.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/02/12/2021.06.15.448478.full.pdfDirect OA link when available
- Concepts
-
Computational biology, Gene, Classifier (UML), Gene expression profiling, Cell type, Biology, Feature selection, Genetics, Computer science, Gene expression, Artificial intelligence, CellTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 2Per-year citation counts (last 5 years)
- References (count)
-
73Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3170554617 |
|---|---|
| doi | https://doi.org/10.1101/2021.06.15.448478 |
| ids.doi | https://doi.org/10.1101/2021.06.15.448478 |
| ids.mag | 3170554617 |
| ids.openalex | https://openalex.org/W3170554617 |
| fwci | 0.19506485 |
| type | preprint |
| title | Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11289 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1312 |
| topics[0].subfield.display_name | Molecular Biology |
| topics[0].display_name | Single-cell and spatial transcriptomics |
| topics[1].id | https://openalex.org/T10885 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9829000234603882 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Gene expression and cancer classification |
| topics[2].id | https://openalex.org/T10207 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9721999764442444 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Advanced biosensing and bioanalysis techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C70721500 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6078605055809021 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q177005 |
| concepts[0].display_name | Computational biology |
| concepts[1].id | https://openalex.org/C104317684 |
| concepts[1].level | 2 |
| concepts[1].score | 0.589730441570282 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[1].display_name | Gene |
| concepts[2].id | https://openalex.org/C95623464 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5141637325286865 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[2].display_name | Classifier (UML) |
| concepts[3].id | https://openalex.org/C18431079 |
| concepts[3].level | 4 |
| concepts[3].score | 0.5052605271339417 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1502169 |
| concepts[3].display_name | Gene expression profiling |
| concepts[4].id | https://openalex.org/C189014844 |
| concepts[4].level | 3 |
| concepts[4].score | 0.49537432193756104 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q189118 |
| concepts[4].display_name | Cell type |
| concepts[5].id | https://openalex.org/C86803240 |
| concepts[5].level | 0 |
| concepts[5].score | 0.47898292541503906 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[5].display_name | Biology |
| concepts[6].id | https://openalex.org/C148483581 |
| concepts[6].level | 2 |
| concepts[6].score | 0.46570709347724915 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q446488 |
| concepts[6].display_name | Feature selection |
| concepts[7].id | https://openalex.org/C54355233 |
| concepts[7].level | 1 |
| concepts[7].score | 0.36389827728271484 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[7].display_name | Genetics |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.3557657301425934 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C150194340 |
| concepts[9].level | 3 |
| concepts[9].score | 0.3225303888320923 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q26972 |
| concepts[9].display_name | Gene expression |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.28257209062576294 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C1491633281 |
| concepts[11].level | 2 |
| concepts[11].score | 0.2668801546096802 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7868 |
| concepts[11].display_name | Cell |
| keywords[0].id | https://openalex.org/keywords/computational-biology |
| keywords[0].score | 0.6078605055809021 |
| keywords[0].display_name | Computational biology |
| keywords[1].id | https://openalex.org/keywords/gene |
| keywords[1].score | 0.589730441570282 |
| keywords[1].display_name | Gene |
| keywords[2].id | https://openalex.org/keywords/classifier |
| keywords[2].score | 0.5141637325286865 |
| keywords[2].display_name | Classifier (UML) |
| keywords[3].id | https://openalex.org/keywords/gene-expression-profiling |
| keywords[3].score | 0.5052605271339417 |
| keywords[3].display_name | Gene expression profiling |
| keywords[4].id | https://openalex.org/keywords/cell-type |
| keywords[4].score | 0.49537432193756104 |
| keywords[4].display_name | Cell type |
| keywords[5].id | https://openalex.org/keywords/biology |
| keywords[5].score | 0.47898292541503906 |
| keywords[5].display_name | Biology |
| keywords[6].id | https://openalex.org/keywords/feature-selection |
| keywords[6].score | 0.46570709347724915 |
| keywords[6].display_name | Feature selection |
| keywords[7].id | https://openalex.org/keywords/genetics |
| keywords[7].score | 0.36389827728271484 |
| keywords[7].display_name | Genetics |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.3557657301425934 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/gene-expression |
| keywords[9].score | 0.3225303888320923 |
| keywords[9].display_name | Gene expression |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.28257209062576294 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/cell |
| keywords[11].score | 0.2668801546096802 |
| keywords[11].display_name | Cell |
| language | en |
| locations[0].id | doi:10.1101/2021.06.15.448478 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2022/02/12/2021.06.15.448478.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2021.06.15.448478 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5102743593 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4685-3466 |
| authorships[0].author.display_name | Xiaoqiao Chen |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I122411786 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computing and Mathematical Sciences, California Institute of Technology. Pasadena, California, 925, USA |
| authorships[0].institutions[0].id | https://openalex.org/I122411786 |
| authorships[0].institutions[0].ror | https://ror.org/05dxps055 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I122411786 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | California Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xiaoqiao Chen |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computing and Mathematical Sciences, California Institute of Technology. Pasadena, California, 925, USA |
| authorships[1].author.id | https://openalex.org/A5100341522 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9448-9713 |
| authorships[1].author.display_name | Sisi Chen |
| authorships[1].countries | US |
| authorships[1].affiliations[0].raw_affiliation_string | Beckman Center for Single-cell Profiling and Engineering. Pasadena, California, 91125, USA |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I122411786 |
| authorships[1].affiliations[1].raw_affiliation_string | Division of Biology and Biological Engineering, California Institute of Technology. Pasadena, California, 9115, USA |
| authorships[1].institutions[0].id | https://openalex.org/I122411786 |
| authorships[1].institutions[0].ror | https://ror.org/05dxps055 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I122411786 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | California Institute of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sisi Chen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Beckman Center for Single-cell Profiling and Engineering. Pasadena, California, 91125, USA, Division of Biology and Biological Engineering, California Institute of Technology. Pasadena, California, 9115, USA |
| authorships[2].author.id | https://openalex.org/A5014506392 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1021-1234 |
| authorships[2].author.display_name | Matt Thomson |
| authorships[2].countries | US |
| authorships[2].affiliations[0].raw_affiliation_string | Beckman Center for Single-cell Profiling and Engineering. Pasadena, California, 91125, USA |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I122411786 |
| authorships[2].affiliations[1].raw_affiliation_string | Department of Computing and Mathematical Sciences, California Institute of Technology. Pasadena, California, 925, USA |
| authorships[2].affiliations[2].institution_ids | https://openalex.org/I122411786 |
| authorships[2].affiliations[2].raw_affiliation_string | Division of Biology and Biological Engineering, California Institute of Technology. Pasadena, California, 9115, USA |
| authorships[2].institutions[0].id | https://openalex.org/I122411786 |
| authorships[2].institutions[0].ror | https://ror.org/05dxps055 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I122411786 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | California Institute of Technology |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Matt Thomson |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Beckman Center for Single-cell Profiling and Engineering. Pasadena, California, 91125, USA, Department of Computing and Mathematical Sciences, California Institute of Technology. Pasadena, California, 925, USA, Division of Biology and Biological Engineering, California Institute of Technology. Pasadena, California, 9115, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2022/02/12/2021.06.15.448478.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11289 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1312 |
| primary_topic.subfield.display_name | Molecular Biology |
| primary_topic.display_name | Single-cell and spatial transcriptomics |
| related_works | https://openalex.org/W2052655934, https://openalex.org/W104645380, https://openalex.org/W2027213253, https://openalex.org/W2017047508, https://openalex.org/W2165201120, https://openalex.org/W2023641020, https://openalex.org/W2188530857, https://openalex.org/W2133040682, https://openalex.org/W1971081588, https://openalex.org/W2041199873 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2021 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/2021.06.15.448478 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2022/02/12/2021.06.15.448478.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2021.06.15.448478 |
| primary_location.id | doi:10.1101/2021.06.15.448478 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2022/02/12/2021.06.15.448478.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2021.06.15.448478 |
| publication_date | 2021-06-16 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2342807339, https://openalex.org/W2098454110, https://openalex.org/W3013900499, https://openalex.org/W3113630028, https://openalex.org/W3023418641, https://openalex.org/W2952887658, https://openalex.org/W2982231537, https://openalex.org/W2982357115, https://openalex.org/W2071128523, https://openalex.org/W1902027874, https://openalex.org/W3128358897, https://openalex.org/W2896353945, https://openalex.org/W2135248331, https://openalex.org/W1964940342, https://openalex.org/W6756615331, https://openalex.org/W2514159238, https://openalex.org/W2894687190, https://openalex.org/W3096333621, https://openalex.org/W2561754210, https://openalex.org/W2923300855, https://openalex.org/W2765284830, https://openalex.org/W2102212449, https://openalex.org/W2034365297, https://openalex.org/W2026498605, https://openalex.org/W2403153566, https://openalex.org/W2118585731, https://openalex.org/W2781865485, https://openalex.org/W2036384654, https://openalex.org/W2118561568, https://openalex.org/W2125283600, https://openalex.org/W2560103205, https://openalex.org/W1981552604, https://openalex.org/W2106578604, https://openalex.org/W2131681506, https://openalex.org/W2973989845, https://openalex.org/W3092316288, https://openalex.org/W2140405352, https://openalex.org/W2166643977, https://openalex.org/W2146180544, https://openalex.org/W3082077009, https://openalex.org/W2998891415, https://openalex.org/W2151987348, https://openalex.org/W2076003134, https://openalex.org/W2160470066, https://openalex.org/W2095162291, https://openalex.org/W2147423551, https://openalex.org/W2336509969, https://openalex.org/W2339229309, https://openalex.org/W1973267259, https://openalex.org/W2900487160, https://openalex.org/W2781615683, https://openalex.org/W3194298692, https://openalex.org/W3110592098, https://openalex.org/W2462053388, https://openalex.org/W1527620915, https://openalex.org/W2335295795, https://openalex.org/W2077815912, https://openalex.org/W6686604339, https://openalex.org/W2168020168, https://openalex.org/W2768455369, https://openalex.org/W6761025279, https://openalex.org/W1968426398, https://openalex.org/W2139104449, https://openalex.org/W2151570219, https://openalex.org/W151188873, https://openalex.org/W3104887532, https://openalex.org/W3105524694, https://openalex.org/W2546945975, https://openalex.org/W1663973292, https://openalex.org/W2951506174, https://openalex.org/W3099768174, https://openalex.org/W2903158431, https://openalex.org/W2153635508 |
| referenced_works_count | 73 |
| abstract_inverted_index.a | 32, 67, 128, 148 |
| abstract_inverted_index.By | 113 |
| abstract_inverted_index.We | 132 |
| abstract_inverted_index.an | 40, 85, 106 |
| abstract_inverted_index.at | 95 |
| abstract_inverted_index.by | 22 |
| abstract_inverted_index.in | 63, 180, 197 |
| abstract_inverted_index.of | 7, 35, 56, 70, 98, 150, 186, 200, 205 |
| abstract_inverted_index.on | 117 |
| abstract_inverted_index.to | 10, 100, 122, 164, 169, 173, 208 |
| abstract_inverted_index.we | 38 |
| abstract_inverted_index.90% | 143 |
| abstract_inverted_index.Our | 72 |
| abstract_inverted_index.The | 161, 184 |
| abstract_inverted_index.and | 13, 60, 156, 172, 213 |
| abstract_inverted_index.are | 93 |
| abstract_inverted_index.but | 48, 188 |
| abstract_inverted_index.for | 203 |
| abstract_inverted_index.the | 5, 54, 198 |
| abstract_inverted_index.cell | 154 |
| abstract_inverted_index.data | 65, 83, 124, 151, 159 |
| abstract_inverted_index.each | 96 |
| abstract_inverted_index.from | 81 |
| abstract_inverted_index.gene | 25, 50, 79, 139, 177, 191 |
| abstract_inverted_index.many | 11 |
| abstract_inverted_index.over | 127 |
| abstract_inverted_index.sets | 26, 51, 80, 125, 140, 152, 192 |
| abstract_inverted_index.task | 89 |
| abstract_inverted_index.that | 27, 45, 52, 134, 141, 167 |
| abstract_inverted_index.with | 31, 126 |
| abstract_inverted_index.Here, | 37 |
| abstract_inverted_index.atlas | 155 |
| abstract_inverted_index.cells | 92 |
| abstract_inverted_index.costs | 2, 21 |
| abstract_inverted_index.data. | 183 |
| abstract_inverted_index.genes | 104, 166 |
| abstract_inverted_index.round | 97 |
| abstract_inverted_index.sets. | 160 |
| abstract_inverted_index.small | 68, 187 |
| abstract_inverted_index.using | 66 |
| abstract_inverted_index.where | 90 |
| abstract_inverted_index.across | 147 |
| abstract_inverted_index.active | 41, 73 |
| abstract_inverted_index.cells, | 119 |
| abstract_inverted_index.cells. | 131 |
| abstract_inverted_index.enable | 53, 142, 194 |
| abstract_inverted_index.genes. | 36, 71 |
| abstract_inverted_index.highly | 189 |
| abstract_inverted_index.method | 43, 162 |
| abstract_inverted_index.number | 34, 69, 199 |
| abstract_inverted_index.region | 175 |
| abstract_inverted_index.reveal | 165 |
| abstract_inverted_index.scales | 121 |
| abstract_inverted_index.should | 193 |
| abstract_inverted_index.single | 130 |
| abstract_inverted_index.tests, | 210 |
| abstract_inverted_index.vector | 109 |
| abstract_inverted_index.analyze | 123 |
| abstract_inverted_index.capture | 28 |
| abstract_inverted_index.disease | 157 |
| abstract_inverted_index.feature | 74, 136 |
| abstract_inverted_index.genetic | 61, 170, 214 |
| abstract_inverted_index.machine | 110 |
| abstract_inverted_index.million | 129 |
| abstract_inverted_index.minimal | 33, 47, 78 |
| abstract_inverted_index.reduced | 24 |
| abstract_inverted_index.reduces | 19 |
| abstract_inverted_index.respond | 168 |
| abstract_inverted_index.spatial | 181 |
| abstract_inverted_index.states, | 59 |
| abstract_inverted_index.support | 108 |
| abstract_inverted_index.through | 84, 105 |
| abstract_inverted_index.variety | 149 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Targeted | 16 |
| abstract_inverted_index.accuracy | 146 |
| abstract_inverted_index.analysis | 99 |
| abstract_inverted_index.clinical | 14, 209 |
| abstract_inverted_index.examined | 94 |
| abstract_inverted_index.focusing | 114 |
| abstract_inverted_index.identify | 101, 174 |
| abstract_inverted_index.learning | 42 |
| abstract_inverted_index.mRNA-seq | 9, 207 |
| abstract_inverted_index.patterns | 179 |
| abstract_inverted_index.prohibit | 4 |
| abstract_inverted_index.screens. | 215 |
| abstract_inverted_index.specific | 176 |
| abstract_inverted_index.ActiveSVM | 120, 135 |
| abstract_inverted_index.analyses. | 15 |
| abstract_inverted_index.cell-type | 87, 144 |
| abstract_inverted_index.currently | 3 |
| abstract_inverted_index.discovery | 185 |
| abstract_inverted_index.generates | 77 |
| abstract_inverted_index.including | 153 |
| abstract_inverted_index.introduce | 39 |
| abstract_inverted_index.iterative | 86 |
| abstract_inverted_index.maximally | 102 |
| abstract_inverted_index.necessary | 202 |
| abstract_inverted_index.procedure | 76 |
| abstract_inverted_index.profiling | 23 |
| abstract_inverted_index.resources | 116 |
| abstract_inverted_index.selection | 75, 137 |
| abstract_inverted_index.Sequencing | 1 |
| abstract_inverted_index.biological | 12, 29 |
| abstract_inverted_index.discovery, | 212 |
| abstract_inverted_index.expression | 178 |
| abstract_inverted_index.identifies | 46, 138 |
| abstract_inverted_index.reductions | 196 |
| abstract_inverted_index.sequencing | 20 |
| abstract_inverted_index.(ActiveSVM) | 44, 111 |
| abstract_inverted_index.application | 6, 204 |
| abstract_inverted_index.cell-types, | 57 |
| abstract_inverted_index.classifier. | 112 |
| abstract_inverted_index.demonstrate | 133 |
| abstract_inverted_index.generalizes | 163 |
| abstract_inverted_index.information | 30 |
| abstract_inverted_index.informative | 103, 190 |
| abstract_inverted_index.single-cell | 8, 17, 64, 82, 206 |
| abstract_inverted_index.substantial | 195 |
| abstract_inverted_index.therapeutic | 211 |
| abstract_inverted_index.measurements | 201 |
| abstract_inverted_index.‘active’ | 107 |
| abstract_inverted_index.computational | 115 |
| abstract_inverted_index.misclassified | 91, 118 |
| abstract_inverted_index.perturbations | 62, 171 |
| abstract_inverted_index.physiological | 58 |
| abstract_inverted_index.classification | 88, 145 |
| abstract_inverted_index.identification | 55 |
| abstract_inverted_index.mRNA-sequencing | 18 |
| abstract_inverted_index.transcriptomics | 182 |
| abstract_inverted_index.characterization | 158 |
| abstract_inverted_index.highly-informative | 49 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 93 |
| corresponding_author_ids | https://openalex.org/A5014506392 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I122411786 |
| citation_normalized_percentile.value | 0.50121388 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |