Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis Article Swipe
Yanshuo Chen
,
Yixuan Wang
,
Yuelong Chen
,
Yuqi Cheng
,
Yumeng Wei
,
Yunxiang Li
,
Jiuming Wang
,
Yingying Wei
,
Ting‐Fung Chan
,
Yu Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-022-34550-9
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-022-34550-9
Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-022-34550-9
- https://www.nature.com/articles/s41467-022-34550-9.pdf
- OA Status
- gold
- Cited By
- 87
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308589388
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4308589388Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41467-022-34550-9Digital Object Identifier
- Title
-
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-08Full publication date if available
- Authors
-
Yanshuo Chen, Yixuan Wang, Yuelong Chen, Yuqi Cheng, Yumeng Wei, Yunxiang Li, Jiuming Wang, Yingying Wei, Ting‐Fung Chan, Yu LiList of authors in order
- Landing page
-
https://doi.org/10.1038/s41467-022-34550-9Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41467-022-34550-9.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41467-022-34550-9.pdfDirect OA link when available
- Concepts
-
Autoencoder, Deconvolution, Computer science, Cell type, Artificial intelligence, Deep learning, Pattern recognition (psychology), RNA, Computational biology, Cell, Gene, Algorithm, Biology, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
87Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 25, 2024: 45, 2023: 16, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4308589388 |
|---|---|
| doi | https://doi.org/10.1038/s41467-022-34550-9 |
| ids.doi | https://doi.org/10.1038/s41467-022-34550-9 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/36347853 |
| ids.openalex | https://openalex.org/W4308589388 |
| fwci | 10.95315506 |
| mesh[0].qualifier_ui | Q000379 |
| mesh[0].descriptor_ui | D017423 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | methods |
| mesh[0].descriptor_name | Sequence Analysis, RNA |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000081246 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | RNA-Seq |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D059010 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Single-Cell Analysis |
| mesh[3].qualifier_ui | Q000379 |
| mesh[3].descriptor_ui | D020869 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | methods |
| mesh[3].descriptor_name | Gene Expression Profiling |
| mesh[4].qualifier_ui | Q000379 |
| mesh[4].descriptor_ui | D017423 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | methods |
| mesh[4].descriptor_name | Sequence Analysis, RNA |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D000081246 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | RNA-Seq |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D059010 |
| mesh[6].is_major_topic | True |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Single-Cell Analysis |
| mesh[7].qualifier_ui | Q000379 |
| mesh[7].descriptor_ui | D020869 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | methods |
| mesh[7].descriptor_name | Gene Expression Profiling |
| mesh[8].qualifier_ui | Q000379 |
| mesh[8].descriptor_ui | D017423 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | methods |
| mesh[8].descriptor_name | Sequence Analysis, RNA |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D000081246 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | RNA-Seq |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D059010 |
| mesh[10].is_major_topic | True |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Single-Cell Analysis |
| mesh[11].qualifier_ui | Q000379 |
| mesh[11].descriptor_ui | D020869 |
| mesh[11].is_major_topic | True |
| mesh[11].qualifier_name | methods |
| mesh[11].descriptor_name | Gene Expression Profiling |
| type | article |
| title | Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis |
| awards[0].id | https://openalex.org/G6801648110 |
| awards[0].funder_id | https://openalex.org/F4320322942 |
| awards[0].display_name | |
| awards[0].funder_award_id | 4937025, 4937026, 5501517, 5501329 |
| awards[0].funder_display_name | Chinese University of Hong Kong |
| biblio.issue | 1 |
| biblio.volume | 13 |
| biblio.last_page | 6735 |
| biblio.first_page | 6735 |
| 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/T10062 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9944999814033508 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1306 |
| topics[1].subfield.display_name | Cancer Research |
| topics[1].display_name | MicroRNA in disease regulation |
| topics[2].id | https://openalex.org/T10773 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9912999868392944 |
| 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 | Extracellular vesicles in disease |
| funders[0].id | https://openalex.org/F4320322942 |
| funders[0].ror | https://ror.org/00t33hh48 |
| funders[0].display_name | Chinese University of Hong Kong |
| is_xpac | False |
| apc_list.value | 3920 |
| apc_list.currency | GBP |
| apc_list.value_usd | 4808 |
| apc_paid.value | 3920 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 4808 |
| concepts[0].id | https://openalex.org/C101738243 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9065020084381104 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q786435 |
| concepts[0].display_name | Autoencoder |
| concepts[1].id | https://openalex.org/C174576160 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7702896595001221 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1183700 |
| concepts[1].display_name | Deconvolution |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6910334825515747 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C189014844 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5437700152397156 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q189118 |
| concepts[3].display_name | Cell type |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.536673367023468 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C108583219 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4610674977302551 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[5].display_name | Deep learning |
| concepts[6].id | https://openalex.org/C153180895 |
| concepts[6].level | 2 |
| concepts[6].score | 0.42361846566200256 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[6].display_name | Pattern recognition (psychology) |
| concepts[7].id | https://openalex.org/C67705224 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4156743884086609 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11053 |
| concepts[7].display_name | RNA |
| concepts[8].id | https://openalex.org/C70721500 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4113909602165222 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q177005 |
| concepts[8].display_name | Computational biology |
| concepts[9].id | https://openalex.org/C1491633281 |
| concepts[9].level | 2 |
| concepts[9].score | 0.349735289812088 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7868 |
| concepts[9].display_name | Cell |
| concepts[10].id | https://openalex.org/C104317684 |
| concepts[10].level | 2 |
| concepts[10].score | 0.3422449231147766 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[10].display_name | Gene |
| concepts[11].id | https://openalex.org/C11413529 |
| concepts[11].level | 1 |
| concepts[11].score | 0.30017051100730896 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[11].display_name | Algorithm |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.18372184038162231 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| concepts[13].id | https://openalex.org/C54355233 |
| concepts[13].level | 1 |
| concepts[13].score | 0.1025443971157074 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[13].display_name | Genetics |
| keywords[0].id | https://openalex.org/keywords/autoencoder |
| keywords[0].score | 0.9065020084381104 |
| keywords[0].display_name | Autoencoder |
| keywords[1].id | https://openalex.org/keywords/deconvolution |
| keywords[1].score | 0.7702896595001221 |
| keywords[1].display_name | Deconvolution |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6910334825515747 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/cell-type |
| keywords[3].score | 0.5437700152397156 |
| keywords[3].display_name | Cell type |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.536673367023468 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/deep-learning |
| keywords[5].score | 0.4610674977302551 |
| keywords[5].display_name | Deep learning |
| keywords[6].id | https://openalex.org/keywords/pattern-recognition |
| keywords[6].score | 0.42361846566200256 |
| keywords[6].display_name | Pattern recognition (psychology) |
| keywords[7].id | https://openalex.org/keywords/rna |
| keywords[7].score | 0.4156743884086609 |
| keywords[7].display_name | RNA |
| keywords[8].id | https://openalex.org/keywords/computational-biology |
| keywords[8].score | 0.4113909602165222 |
| keywords[8].display_name | Computational biology |
| keywords[9].id | https://openalex.org/keywords/cell |
| keywords[9].score | 0.349735289812088 |
| keywords[9].display_name | Cell |
| keywords[10].id | https://openalex.org/keywords/gene |
| keywords[10].score | 0.3422449231147766 |
| keywords[10].display_name | Gene |
| keywords[11].id | https://openalex.org/keywords/algorithm |
| keywords[11].score | 0.30017051100730896 |
| keywords[11].display_name | Algorithm |
| keywords[12].id | https://openalex.org/keywords/biology |
| keywords[12].score | 0.18372184038162231 |
| keywords[12].display_name | Biology |
| keywords[13].id | https://openalex.org/keywords/genetics |
| keywords[13].score | 0.1025443971157074 |
| keywords[13].display_name | Genetics |
| language | en |
| locations[0].id | doi:10.1038/s41467-022-34550-9 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S64187185 |
| locations[0].source.issn | 2041-1723 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2041-1723 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Nature Communications |
| locations[0].source.host_organization | https://openalex.org/P4310319908 |
| locations[0].source.host_organization_name | Nature Portfolio |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.nature.com/articles/s41467-022-34550-9.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Nature Communications |
| locations[0].landing_page_url | https://doi.org/10.1038/s41467-022-34550-9 |
| locations[1].id | pmid:36347853 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Nature communications |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/36347853 |
| locations[2].id | pmh:oai:doaj.org/article:defba3b90ae74d5cb70d3da73bb19d77 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Nature Communications, Vol 13, Iss 1, Pp 1-17 (2022) |
| locations[2].landing_page_url | https://doaj.org/article/defba3b90ae74d5cb70d3da73bb19d77 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:9641692 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Nat Commun |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9641692 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5074768496 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6675-2449 |
| authorships[0].author.display_name | Yanshuo Chen |
| authorships[0].countries | HK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[0].institutions[0].id | https://openalex.org/I889458895 |
| authorships[0].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[0].institutions[0].country_code | HK |
| authorships[0].institutions[0].display_name | University of Hong Kong |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yanshuo Chen |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[1].author.id | https://openalex.org/A5100408474 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8989-3129 |
| authorships[1].author.display_name | Yixuan Wang |
| authorships[1].countries | HK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[1].institutions[0].id | https://openalex.org/I889458895 |
| authorships[1].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[1].institutions[0].country_code | HK |
| authorships[1].institutions[0].display_name | University of Hong Kong |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yixuan Wang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[2].author.id | https://openalex.org/A5051694181 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4501-396X |
| authorships[2].author.display_name | Yuelong Chen |
| authorships[2].countries | HK |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Life Sciences, CUHK, Hong Kong SAR, China |
| authorships[2].institutions[0].id | https://openalex.org/I889458895 |
| authorships[2].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[2].institutions[0].country_code | HK |
| authorships[2].institutions[0].display_name | University of Hong Kong |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yuelong Chen |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Life Sciences, CUHK, Hong Kong SAR, China |
| authorships[3].author.id | https://openalex.org/A5024928131 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6664-419X |
| authorships[3].author.display_name | Yuqi Cheng |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I205783295, https://openalex.org/I4387153466 |
| authorships[3].affiliations[0].raw_affiliation_string | Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, 10065, USA |
| authorships[3].institutions[0].id | https://openalex.org/I205783295 |
| authorships[3].institutions[0].ror | https://ror.org/05bnh6r87 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I205783295 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Cornell University |
| authorships[3].institutions[1].id | https://openalex.org/I4387153466 |
| authorships[3].institutions[1].ror | https://ror.org/02r109517 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I205783295, https://openalex.org/I4387153466 |
| authorships[3].institutions[1].country_code | US |
| authorships[3].institutions[1].display_name | Weill Cornell Medicine |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yuqi Cheng |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, 10065, USA |
| authorships[4].author.id | https://openalex.org/A5018102503 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Yumeng Wei |
| authorships[4].countries | HK |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[4].institutions[0].id | https://openalex.org/I889458895 |
| authorships[4].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[4].institutions[0].country_code | HK |
| authorships[4].institutions[0].display_name | University of Hong Kong |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yumeng Wei |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[5].author.id | https://openalex.org/A5100690430 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-4743-0975 |
| authorships[5].author.display_name | Yunxiang Li |
| authorships[5].countries | HK |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[5].institutions[0].id | https://openalex.org/I889458895 |
| authorships[5].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[5].institutions[0].country_code | HK |
| authorships[5].institutions[0].display_name | University of Hong Kong |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Yunxiang Li |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[6].author.id | https://openalex.org/A5078001917 |
| authorships[6].author.orcid | https://orcid.org/0009-0003-9591-156X |
| authorships[6].author.display_name | Jiuming Wang |
| authorships[6].countries | HK |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[6].institutions[0].id | https://openalex.org/I889458895 |
| authorships[6].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[6].institutions[0].country_code | HK |
| authorships[6].institutions[0].display_name | University of Hong Kong |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Jiuming Wang |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[7].author.id | https://openalex.org/A5044711268 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-3826-336X |
| authorships[7].author.display_name | Yingying Wei |
| authorships[7].countries | HK |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I177725633 |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[7].institutions[0].id | https://openalex.org/I177725633 |
| authorships[7].institutions[0].ror | https://ror.org/00t33hh48 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I177725633 |
| authorships[7].institutions[0].country_code | HK |
| authorships[7].institutions[0].display_name | Chinese University of Hong Kong |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Yingying Wei |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[8].author.id | https://openalex.org/A5080235217 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-0489-3884 |
| authorships[8].author.display_name | Ting‐Fung Chan |
| authorships[8].countries | HK |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[8].affiliations[0].raw_affiliation_string | School of Life Sciences, CUHK, Hong Kong SAR, China |
| authorships[8].institutions[0].id | https://openalex.org/I889458895 |
| authorships[8].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[8].institutions[0].country_code | HK |
| authorships[8].institutions[0].display_name | University of Hong Kong |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Ting-Fung Chan |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | School of Life Sciences, CUHK, Hong Kong SAR, China |
| authorships[9].author.id | https://openalex.org/A5100345753 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-3664-6722 |
| authorships[9].author.display_name | Yu Li |
| authorships[9].countries | HK |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I889458895 |
| authorships[9].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| authorships[9].institutions[0].id | https://openalex.org/I889458895 |
| authorships[9].institutions[0].ror | https://ror.org/02zhqgq86 |
| authorships[9].institutions[0].type | education |
| authorships[9].institutions[0].lineage | https://openalex.org/I889458895 |
| authorships[9].institutions[0].country_code | HK |
| authorships[9].institutions[0].display_name | University of Hong Kong |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Yu Li |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.nature.com/articles/s41467-022-34550-9.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-23T05:10:03.516525 |
| 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/W3013693939, https://openalex.org/W2159052453, https://openalex.org/W2566616303, https://openalex.org/W3131327266, https://openalex.org/W2734887215, https://openalex.org/W4297051394, https://openalex.org/W2669956259, https://openalex.org/W4249005693, https://openalex.org/W4392946183, https://openalex.org/W3088732000 |
| cited_by_count | 87 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 25 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 45 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 16 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1038/s41467-022-34550-9 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S64187185 |
| best_oa_location.source.issn | 2041-1723 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2041-1723 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Nature Communications |
| best_oa_location.source.host_organization | https://openalex.org/P4310319908 |
| best_oa_location.source.host_organization_name | Nature Portfolio |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.nature.com/articles/s41467-022-34550-9.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Nature Communications |
| best_oa_location.landing_page_url | https://doi.org/10.1038/s41467-022-34550-9 |
| primary_location.id | doi:10.1038/s41467-022-34550-9 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S64187185 |
| primary_location.source.issn | 2041-1723 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2041-1723 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Nature Communications |
| primary_location.source.host_organization | https://openalex.org/P4310319908 |
| primary_location.source.host_organization_name | Nature Portfolio |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.nature.com/articles/s41467-022-34550-9.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Nature Communications |
| primary_location.landing_page_url | https://doi.org/10.1038/s41467-022-34550-9 |
| publication_date | 2022-11-08 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3110500961, https://openalex.org/W3113226902, https://openalex.org/W2775443868, https://openalex.org/W2743416243, https://openalex.org/W1988391018, https://openalex.org/W4295717668, https://openalex.org/W1985987855, https://openalex.org/W2952240309, https://openalex.org/W2942610007, https://openalex.org/W3019556496, https://openalex.org/W2953866345, https://openalex.org/W3186021169, https://openalex.org/W3112908745, https://openalex.org/W3044352330, https://openalex.org/W3136371060, https://openalex.org/W2123879591, https://openalex.org/W2107665951, https://openalex.org/W2894687190, https://openalex.org/W2313339984, https://openalex.org/W2320901857, https://openalex.org/W2911922390, https://openalex.org/W2809330972, https://openalex.org/W3058283092, https://openalex.org/W2190545194, https://openalex.org/W2950886245, https://openalex.org/W3114925893, https://openalex.org/W3127484940, https://openalex.org/W1552434751, https://openalex.org/W2052742260, https://openalex.org/W2774947569, https://openalex.org/W2799331265, https://openalex.org/W3082706749, https://openalex.org/W3016987930, https://openalex.org/W2523620612, https://openalex.org/W3154698785, https://openalex.org/W2892494775, https://openalex.org/W2179438025, https://openalex.org/W3025943349, https://openalex.org/W3048234386, https://openalex.org/W3096661584, https://openalex.org/W2601723752, https://openalex.org/W2109488005, https://openalex.org/W3035965352, https://openalex.org/W2019552331, https://openalex.org/W2901677030, https://openalex.org/W3012280517, https://openalex.org/W3186348651, https://openalex.org/W3167821357, https://openalex.org/W6675354045, https://openalex.org/W2183341477, https://openalex.org/W2011301426, https://openalex.org/W3150635270, https://openalex.org/W6969259846, https://openalex.org/W6968992706, https://openalex.org/W6894133568, https://openalex.org/W3099878876, https://openalex.org/W4308589388 |
| referenced_works_count | 57 |
| abstract_inverted_index.a | 5, 36, 51, 62, 84, 148 |
| abstract_inverted_index.By | 54 |
| abstract_inverted_index.We | 132 |
| abstract_inverted_index.an | 56 |
| abstract_inverted_index.at | 13, 91 |
| abstract_inverted_index.by | 26 |
| abstract_inverted_index.in | 50, 147 |
| abstract_inverted_index.is | 23, 97 |
| abstract_inverted_index.it | 96 |
| abstract_inverted_index.of | 116, 143 |
| abstract_inverted_index.on | 20, 79 |
| abstract_inverted_index.to | 8, 46, 107, 123 |
| abstract_inverted_index.we | 31 |
| abstract_inverted_index.and | 43, 59, 70, 88, 105, 138 |
| abstract_inverted_index.can | 66 |
| abstract_inverted_index.has | 3, 83 |
| abstract_inverted_index.its | 18, 27, 121 |
| abstract_inverted_index.the | 114, 140 |
| abstract_inverted_index.TAPE | 65, 82, 119, 135 |
| abstract_inverted_index.bulk | 41 |
| abstract_inverted_index.cell | 92, 102 |
| abstract_inverted_index.data | 22, 146 |
| abstract_inverted_index.deep | 37 |
| abstract_inverted_index.gene | 72, 126 |
| abstract_inverted_index.high | 15 |
| abstract_inverted_index.more | 98 |
| abstract_inverted_index.that | 134 |
| abstract_inverted_index.tool | 7 |
| abstract_inverted_index.type | 93 |
| abstract_inverted_index.wide | 149 |
| abstract_inverted_index.will | 136 |
| abstract_inverted_index.with | 76, 129 |
| abstract_inverted_index.Here, | 30 |
| abstract_inverted_index.among | 100 |
| abstract_inverted_index.data, | 118 |
| abstract_inverted_index.short | 52 |
| abstract_inverted_index.shows | 120 |
| abstract_inverted_index.study | 9 |
| abstract_inverted_index.time. | 53 |
| abstract_inverted_index.under | 61 |
| abstract_inverted_index.become | 4 |
| abstract_inverted_index.better | 85 |
| abstract_inverted_index.enable | 137 |
| abstract_inverted_index.level. | 94 |
| abstract_inverted_index.method | 39 |
| abstract_inverted_index.range. | 150 |
| abstract_inverted_index.robust | 99 |
| abstract_inverted_index.types, | 103 |
| abstract_inverted_index.unique | 63 |
| abstract_inverted_index.(TAPE), | 35 |
| abstract_inverted_index.RNA-seq | 42, 45 |
| abstract_inverted_index.ability | 122 |
| abstract_inverted_index.achieve | 47 |
| abstract_inverted_index.believe | 133 |
| abstract_inverted_index.decoder | 58 |
| abstract_inverted_index.faster, | 104 |
| abstract_inverted_index.limited | 25 |
| abstract_inverted_index.methods | 78 |
| abstract_inverted_index.overall | 86 |
| abstract_inverted_index.popular | 77 |
| abstract_inverted_index.precise | 48, 141 |
| abstract_inverted_index.predict | 67, 124 |
| abstract_inverted_index.provide | 108 |
| abstract_inverted_index.scheme, | 64 |
| abstract_inverted_index.several | 80 |
| abstract_inverted_index.through | 113 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Compared | 75 |
| abstract_inverted_index.However, | 17 |
| abstract_inverted_index.accuracy | 90 |
| abstract_inverted_index.analysis | 115, 142 |
| abstract_inverted_index.clinical | 117, 145 |
| abstract_inverted_index.emerging | 21 |
| abstract_inverted_index.learning | 38 |
| abstract_inverted_index.powerful | 6 |
| abstract_inverted_index.profiles | 128 |
| abstract_inverted_index.training | 60 |
| abstract_inverted_index.Moreover, | 112 |
| abstract_inverted_index.cell-type | 68 |
| abstract_inverted_index.currently | 24 |
| abstract_inverted_index.datasets, | 81 |
| abstract_inverted_index.different | 101 |
| abstract_inverted_index.fractions | 69 |
| abstract_inverted_index.intrinsic | 28 |
| abstract_inverted_index.introduce | 32 |
| abstract_inverted_index.sensitive | 106 |
| abstract_inverted_index.accelerate | 139 |
| abstract_inverted_index.biological | 130 |
| abstract_inverted_index.comparable | 89 |
| abstract_inverted_index.connecting | 40 |
| abstract_inverted_index.explicitly | 14 |
| abstract_inverted_index.expression | 73, 127 |
| abstract_inverted_index.meaningful | 110 |
| abstract_inverted_index.Single-cell | 1 |
| abstract_inverted_index.application | 19 |
| abstract_inverted_index.autoEncoder | 34 |
| abstract_inverted_index.performance | 87 |
| abstract_inverted_index.resolution. | 16 |
| abstract_inverted_index.significant | 11 |
| abstract_inverted_index.single-cell | 44 |
| abstract_inverted_index.techniques. | 29 |
| abstract_inverted_index.biologically | 10, 109 |
| abstract_inverted_index.constructing | 55 |
| abstract_inverted_index.predictions. | 111 |
| abstract_inverted_index.Additionally, | 95 |
| abstract_inverted_index.deconvolution | 49 |
| abstract_inverted_index.interpretable | 57 |
| abstract_inverted_index.significance. | 131 |
| abstract_inverted_index.RNA-sequencing | 2 |
| abstract_inverted_index.Tissue-AdaPtive | 33 |
| abstract_inverted_index.characteristics | 12 |
| abstract_inverted_index.high-throughput | 144 |
| abstract_inverted_index.cell-type-specific | 71, 125 |
| abstract_inverted_index.tissue-adaptively. | 74 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.98599982 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |