Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data Article Swipe
Sabrina Mohd Rashid
,
Sohrab P. Shah
,
Ziv Bar‐Joseph
,
Ravi Pandya
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1101/183863
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1101/183863
Motivation Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data. Results Here we describe ‘Dhaka’, a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and 6 single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data. Availability and Implementation All the datasets used in the paper are publicly available and developed software package is available on Github https://github.com/MicrosoftGenomics/Dhaka . Supporting info and Software: https://github.com/MicrosoftGenomics/Dhaka
Related Topics
Concepts
Autoencoder
Computational biology
Computer science
Dimensionality reduction
Feature (linguistics)
Software
Single cell sequencing
Biology
Genomics
Gene
Artificial intelligence
Data mining
Pattern recognition (psychology)
Mutation
Genetics
Exome sequencing
Genome
Deep learning
Philosophy
Programming language
Linguistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/183863
- https://www.biorxiv.org/content/biorxiv/early/2018/04/19/183863.full.pdf
- OA Status
- green
- Cited By
- 8
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2753670678
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2753670678Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/183863Digital Object Identifier
- Title
-
Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-09-04Full publication date if available
- Authors
-
Sabrina Mohd Rashid, Sohrab P. Shah, Ziv Bar‐Joseph, Ravi PandyaList of authors in order
- Landing page
-
https://doi.org/10.1101/183863Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2018/04/19/183863.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/2018/04/19/183863.full.pdfDirect OA link when available
- Concepts
-
Autoencoder, Computational biology, Computer science, Dimensionality reduction, Feature (linguistics), Software, Single cell sequencing, Biology, Genomics, Gene, Artificial intelligence, Data mining, Pattern recognition (psychology), Mutation, Genetics, Exome sequencing, Genome, Deep learning, Philosophy, Programming language, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1, 2021: 1, 2020: 3, 2019: 2Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2753670678 |
|---|---|
| doi | https://doi.org/10.1101/183863 |
| ids.doi | https://doi.org/10.1101/183863 |
| ids.mag | 2753670678 |
| ids.openalex | https://openalex.org/W2753670678 |
| fwci | 0.66807355 |
| type | preprint |
| title | Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic 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 | 0.9998999834060669 |
| 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/T11287 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9998999834060669 |
| 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 | Cancer Genomics and Diagnostics |
| topics[2].id | https://openalex.org/T10231 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9922999739646912 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2730 |
| topics[2].subfield.display_name | Oncology |
| topics[2].display_name | Pancreatic and Hepatic Oncology Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C101738243 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8416503071784973 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q786435 |
| concepts[0].display_name | Autoencoder |
| concepts[1].id | https://openalex.org/C70721500 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5462803244590759 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q177005 |
| concepts[1].display_name | Computational biology |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5285961627960205 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C70518039 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5216569900512695 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q16000077 |
| concepts[3].display_name | Dimensionality reduction |
| concepts[4].id | https://openalex.org/C2776401178 |
| concepts[4].level | 2 |
| concepts[4].score | 0.47311922907829285 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[4].display_name | Feature (linguistics) |
| concepts[5].id | https://openalex.org/C2777904410 |
| concepts[5].level | 2 |
| concepts[5].score | 0.45566314458847046 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7397 |
| concepts[5].display_name | Software |
| concepts[6].id | https://openalex.org/C174749747 |
| concepts[6].level | 5 |
| concepts[6].score | 0.44994276762008667 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q15917269 |
| concepts[6].display_name | Single cell sequencing |
| concepts[7].id | https://openalex.org/C86803240 |
| concepts[7].level | 0 |
| concepts[7].score | 0.44241228699684143 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[7].display_name | Biology |
| concepts[8].id | https://openalex.org/C189206191 |
| concepts[8].level | 4 |
| concepts[8].score | 0.4324193596839905 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q222046 |
| concepts[8].display_name | Genomics |
| concepts[9].id | https://openalex.org/C104317684 |
| concepts[9].level | 2 |
| concepts[9].score | 0.39122650027275085 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[9].display_name | Gene |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3356097936630249 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C124101348 |
| concepts[11].level | 1 |
| concepts[11].score | 0.33107244968414307 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[11].display_name | Data mining |
| concepts[12].id | https://openalex.org/C153180895 |
| concepts[12].level | 2 |
| concepts[12].score | 0.32514381408691406 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[12].display_name | Pattern recognition (psychology) |
| concepts[13].id | https://openalex.org/C501734568 |
| concepts[13].level | 3 |
| concepts[13].score | 0.2782210111618042 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q42918 |
| concepts[13].display_name | Mutation |
| concepts[14].id | https://openalex.org/C54355233 |
| concepts[14].level | 1 |
| concepts[14].score | 0.2223643958568573 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[14].display_name | Genetics |
| concepts[15].id | https://openalex.org/C16671776 |
| concepts[15].level | 4 |
| concepts[15].score | 0.14777326583862305 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q5420592 |
| concepts[15].display_name | Exome sequencing |
| concepts[16].id | https://openalex.org/C141231307 |
| concepts[16].level | 3 |
| concepts[16].score | 0.14598557353019714 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7020 |
| concepts[16].display_name | Genome |
| concepts[17].id | https://openalex.org/C108583219 |
| concepts[17].level | 2 |
| concepts[17].score | 0.10177075862884521 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[17].display_name | Deep learning |
| concepts[18].id | https://openalex.org/C138885662 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[18].display_name | Philosophy |
| concepts[19].id | https://openalex.org/C199360897 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[19].display_name | Programming language |
| concepts[20].id | https://openalex.org/C41895202 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[20].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/autoencoder |
| keywords[0].score | 0.8416503071784973 |
| keywords[0].display_name | Autoencoder |
| keywords[1].id | https://openalex.org/keywords/computational-biology |
| keywords[1].score | 0.5462803244590759 |
| keywords[1].display_name | Computational biology |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5285961627960205 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/dimensionality-reduction |
| keywords[3].score | 0.5216569900512695 |
| keywords[3].display_name | Dimensionality reduction |
| keywords[4].id | https://openalex.org/keywords/feature |
| keywords[4].score | 0.47311922907829285 |
| keywords[4].display_name | Feature (linguistics) |
| keywords[5].id | https://openalex.org/keywords/software |
| keywords[5].score | 0.45566314458847046 |
| keywords[5].display_name | Software |
| keywords[6].id | https://openalex.org/keywords/single-cell-sequencing |
| keywords[6].score | 0.44994276762008667 |
| keywords[6].display_name | Single cell sequencing |
| keywords[7].id | https://openalex.org/keywords/biology |
| keywords[7].score | 0.44241228699684143 |
| keywords[7].display_name | Biology |
| keywords[8].id | https://openalex.org/keywords/genomics |
| keywords[8].score | 0.4324193596839905 |
| keywords[8].display_name | Genomics |
| keywords[9].id | https://openalex.org/keywords/gene |
| keywords[9].score | 0.39122650027275085 |
| keywords[9].display_name | Gene |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.3356097936630249 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/data-mining |
| keywords[11].score | 0.33107244968414307 |
| keywords[11].display_name | Data mining |
| keywords[12].id | https://openalex.org/keywords/pattern-recognition |
| keywords[12].score | 0.32514381408691406 |
| keywords[12].display_name | Pattern recognition (psychology) |
| keywords[13].id | https://openalex.org/keywords/mutation |
| keywords[13].score | 0.2782210111618042 |
| keywords[13].display_name | Mutation |
| keywords[14].id | https://openalex.org/keywords/genetics |
| keywords[14].score | 0.2223643958568573 |
| keywords[14].display_name | Genetics |
| keywords[15].id | https://openalex.org/keywords/exome-sequencing |
| keywords[15].score | 0.14777326583862305 |
| keywords[15].display_name | Exome sequencing |
| keywords[16].id | https://openalex.org/keywords/genome |
| keywords[16].score | 0.14598557353019714 |
| keywords[16].display_name | Genome |
| keywords[17].id | https://openalex.org/keywords/deep-learning |
| keywords[17].score | 0.10177075862884521 |
| keywords[17].display_name | Deep learning |
| language | en |
| locations[0].id | doi:10.1101/183863 |
| 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 | |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2018/04/19/183863.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/183863 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5112422862 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Sabrina Mohd Rashid |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I74973139 |
| authorships[0].affiliations[0].raw_affiliation_string | Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA |
| authorships[0].institutions[0].id | https://openalex.org/I74973139 |
| authorships[0].institutions[0].ror | https://ror.org/05x2bcf33 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I74973139 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Carnegie Mellon University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sabrina Rashid |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA |
| authorships[1].author.id | https://openalex.org/A5033441782 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6402-523X |
| authorships[1].author.display_name | Sohrab P. Shah |
| authorships[1].countries | CA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I141945490 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I141945490 |
| authorships[1].affiliations[1].raw_affiliation_string | Department of Computer Science, University of British Columbia, Vancouver, Canada |
| authorships[1].affiliations[2].institution_ids | https://openalex.org/I1289530486 |
| authorships[1].affiliations[2].raw_affiliation_string | Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada |
| authorships[1].institutions[0].id | https://openalex.org/I1289530486 |
| authorships[1].institutions[0].ror | https://ror.org/03sfybe47 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I1289530486 |
| authorships[1].institutions[0].country_code | CA |
| authorships[1].institutions[0].display_name | BC Cancer Agency |
| authorships[1].institutions[1].id | https://openalex.org/I141945490 |
| authorships[1].institutions[1].ror | https://ror.org/03rmrcq20 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I141945490, https://openalex.org/I4210128534, https://openalex.org/I4210135497, https://openalex.org/I4387154919 |
| authorships[1].institutions[1].country_code | CA |
| authorships[1].institutions[1].display_name | University of British Columbia |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sohrab Shah |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, University of British Columbia, Vancouver, Canada, Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada |
| authorships[2].author.id | https://openalex.org/A5065673127 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3430-6051 |
| authorships[2].author.display_name | Ziv Bar‐Joseph |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I74973139 |
| authorships[2].affiliations[0].raw_affiliation_string | Machine Learning Department and Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA |
| authorships[2].institutions[0].id | https://openalex.org/I74973139 |
| authorships[2].institutions[0].ror | https://ror.org/05x2bcf33 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I74973139 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Carnegie Mellon University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ziv Bar-Joseph |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Machine Learning Department and Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA |
| authorships[3].author.id | https://openalex.org/A5055091245 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-0258-4604 |
| authorships[3].author.display_name | Ravi Pandya |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I1290206253 |
| authorships[3].affiliations[0].raw_affiliation_string | Microsoft Research, Redmond, USA |
| authorships[3].institutions[0].id | https://openalex.org/I1290206253 |
| authorships[3].institutions[0].ror | https://ror.org/00d0nc645 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I1290206253 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Microsoft (United States) |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Ravi Pandya |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Microsoft Research, Redmond, 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/2018/04/19/183863.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic 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 | 0.9998999834060669 |
| 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/W2566616303, https://openalex.org/W2159052453, https://openalex.org/W3131327266, https://openalex.org/W2734887215, https://openalex.org/W2803255133, https://openalex.org/W4297051394, https://openalex.org/W4310873165, https://openalex.org/W2355395139, https://openalex.org/W4285596704 |
| cited_by_count | 8 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2021 |
| counts_by_year[2].cited_by_count | 1 |
| counts_by_year[3].year | 2020 |
| counts_by_year[3].cited_by_count | 3 |
| counts_by_year[4].year | 2019 |
| counts_by_year[4].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/183863 |
| 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 | |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2018/04/19/183863.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | |
| 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/10.1101/183863 |
| primary_location.id | doi:10.1101/183863 |
| 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 | |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2018/04/19/183863.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/183863 |
| publication_date | 2017-09-04 |
| publication_year | 2017 |
| referenced_works | https://openalex.org/W1971294322, https://openalex.org/W2182664595, https://openalex.org/W1977352336, https://openalex.org/W2093211369, https://openalex.org/W2108371618, https://openalex.org/W2342016684, https://openalex.org/W2273101790, https://openalex.org/W4237564926, https://openalex.org/W2269040222, https://openalex.org/W2075593917, https://openalex.org/W2614253029, https://openalex.org/W2215765065, https://openalex.org/W1968641405, https://openalex.org/W2217442075, https://openalex.org/W107619411, https://openalex.org/W2030566362, https://openalex.org/W2919115771, https://openalex.org/W2746374269, https://openalex.org/W2741564801, https://openalex.org/W1891956616, https://openalex.org/W2139465853, https://openalex.org/W1968940896, https://openalex.org/W2030017878, https://openalex.org/W2152692691, https://openalex.org/W2058879373, https://openalex.org/W2053186076, https://openalex.org/W1964302896, https://openalex.org/W2344887288, https://openalex.org/W2332292689, https://openalex.org/W2546514099, https://openalex.org/W1984883254, https://openalex.org/W2000553618, https://openalex.org/W2603523955, https://openalex.org/W2953251392, https://openalex.org/W2149681218, https://openalex.org/W2109890799, https://openalex.org/W2589483699, https://openalex.org/W2571353615, https://openalex.org/W2064977507, https://openalex.org/W1555168845, https://openalex.org/W2148694408, https://openalex.org/W2129472406, https://openalex.org/W2187089797, https://openalex.org/W1959608418, https://openalex.org/W2999729612 |
| referenced_works_count | 45 |
| abstract_inverted_index.. | 231 |
| abstract_inverted_index.6 | 148 |
| abstract_inverted_index.a | 31, 45, 88, 99 |
| abstract_inverted_index.We | 141 |
| abstract_inverted_index.as | 44 |
| abstract_inverted_index.be | 120 |
| abstract_inverted_index.in | 11, 19, 62, 108, 216 |
| abstract_inverted_index.is | 4, 105, 116, 226 |
| abstract_inverted_index.of | 6, 38, 80, 126, 156, 167, 174, 206 |
| abstract_inverted_index.on | 145, 228 |
| abstract_inverted_index.to | 64, 73, 98, 122, 161, 185 |
| abstract_inverted_index.we | 85 |
| abstract_inverted_index.250 | 160 |
| abstract_inverted_index.All | 212 |
| abstract_inverted_index.Our | 114 |
| abstract_inverted_index.The | 180 |
| abstract_inverted_index.and | 25, 77, 118, 135, 147, 176, 203, 210, 222, 234 |
| abstract_inverted_index.are | 182, 219 |
| abstract_inverted_index.can | 119 |
| abstract_inverted_index.due | 72 |
| abstract_inverted_index.for | 48, 163, 200 |
| abstract_inverted_index.has | 41 |
| abstract_inverted_index.key | 8 |
| abstract_inverted_index.one | 5 |
| abstract_inverted_index.the | 7, 50, 74, 81, 143, 154, 168, 187, 213, 217 |
| abstract_inverted_index.6000 | 162 |
| abstract_inverted_index.Here | 84 |
| abstract_inverted_index.able | 184 |
| abstract_inverted_index.also | 183 |
| abstract_inverted_index.cell | 36, 59, 95, 150 |
| abstract_inverted_index.copy | 23, 130 |
| abstract_inverted_index.data | 61, 97, 128 |
| abstract_inverted_index.each | 164 |
| abstract_inverted_index.even | 27 |
| abstract_inverted_index.from | 30, 57, 133, 138, 159 |
| abstract_inverted_index.gene | 21, 136 |
| abstract_inverted_index.info | 233 |
| abstract_inverted_index.more | 106 |
| abstract_inverted_index.such | 207 |
| abstract_inverted_index.that | 104 |
| abstract_inverted_index.upon | 196 |
| abstract_inverted_index.used | 215 |
| abstract_inverted_index.when | 28 |
| abstract_inverted_index.cell. | 34 |
| abstract_inverted_index.cells | 16, 40, 157, 175, 193 |
| abstract_inverted_index.data. | 82, 208 |
| abstract_inverted_index.infer | 65, 186 |
| abstract_inverted_index.noisy | 76 |
| abstract_inverted_index.order | 63 |
| abstract_inverted_index.paper | 218 |
| abstract_inverted_index.prior | 197 |
| abstract_inverted_index.space | 103, 171 |
| abstract_inverted_index.their | 20, 66, 177 |
| abstract_inverted_index.tumor | 13, 39, 52, 112 |
| abstract_inverted_index.types | 125 |
| abstract_inverted_index.where | 153 |
| abstract_inverted_index.which | 92 |
| abstract_inverted_index.Github | 229 |
| abstract_inverted_index.Single | 35 |
| abstract_inverted_index.and/or | 189 |
| abstract_inverted_index.cancer | 151 |
| abstract_inverted_index.genes. | 179 |
| abstract_inverted_index.marker | 178 |
| abstract_inverted_index.method | 91, 115, 144 |
| abstract_inverted_index.nature | 79 |
| abstract_inverted_index.number | 131, 155 |
| abstract_inverted_index.option | 47 |
| abstract_inverted_index.ranges | 158 |
| abstract_inverted_index.single | 32, 58, 94, 149 |
| abstract_inverted_index.sparse | 78 |
| abstract_inverted_index.tested | 142 |
| abstract_inverted_index.viable | 46 |
| abstract_inverted_index.Results | 83 |
| abstract_inverted_index.applied | 121 |
| abstract_inverted_index.between | 110, 192 |
| abstract_inverted_index.emerged | 43 |
| abstract_inverted_index.exhibit | 17 |
| abstract_inverted_index.factors | 10 |
| abstract_inverted_index.feature | 102, 170, 201 |
| abstract_inverted_index.general | 117 |
| abstract_inverted_index.genomic | 60, 96, 127 |
| abstract_inverted_index.greatly | 194 |
| abstract_inverted_index.lineage | 188 |
| abstract_inverted_index.methods | 198 |
| abstract_inverted_index.package | 225 |
| abstract_inverted_index.reduced | 100 |
| abstract_inverted_index.remains | 69 |
| abstract_inverted_index.sample. | 165 |
| abstract_inverted_index.several | 123 |
| abstract_inverted_index.(hidden) | 111 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Analysis | 166 |
| abstract_inverted_index.However, | 54 |
| abstract_inverted_index.datasets | 152, 214 |
| abstract_inverted_index.describe | 86 |
| abstract_inverted_index.features | 56, 181 |
| abstract_inverted_index.mutation | 26 |
| abstract_inverted_index.numbers, | 24 |
| abstract_inverted_index.publicly | 220 |
| abstract_inverted_index.recently | 42 |
| abstract_inverted_index.revealed | 172 |
| abstract_inverted_index.software | 224 |
| abstract_inverted_index.Malignant | 15 |
| abstract_inverted_index.Software: | 235 |
| abstract_inverted_index.available | 221, 227 |
| abstract_inverted_index.developed | 223 |
| abstract_inverted_index.different | 124 |
| abstract_inverted_index.dimension | 101 |
| abstract_inverted_index.efficient | 107 |
| abstract_inverted_index.extremely | 75 |
| abstract_inverted_index.improving | 195 |
| abstract_inverted_index.including | 129 |
| abstract_inverted_index.reduction | 205 |
| abstract_inverted_index.resulting | 169 |
| abstract_inverted_index.scDNA-Seq | 134 |
| abstract_inverted_index.scRNA-Seq | 139 |
| abstract_inverted_index.suggested | 199 |
| abstract_inverted_index.synthetic | 146 |
| abstract_inverted_index.unmasking | 49 |
| abstract_inverted_index.variation | 132 |
| abstract_inverted_index.Motivation | 1 |
| abstract_inverted_index.Supporting | 232 |
| abstract_inverted_index.evolution. | 14 |
| abstract_inverted_index.expression | 137 |
| abstract_inverted_index.extracting | 55 |
| abstract_inverted_index.extraction | 202 |
| abstract_inverted_index.progenitor | 33 |
| abstract_inverted_index.sequencing | 37 |
| abstract_inverted_index.trajectory | 68, 191 |
| abstract_inverted_index.transforms | 93 |
| abstract_inverted_index.underlying | 51 |
| abstract_inverted_index.variations | 18 |
| abstract_inverted_index.Intra-tumor | 2 |
| abstract_inverted_index.autoencoder | 90 |
| abstract_inverted_index.challenging | 71 |
| abstract_inverted_index.confounding | 9 |
| abstract_inverted_index.deciphering | 12 |
| abstract_inverted_index.expression, | 22 |
| abstract_inverted_index.originating | 29 |
| abstract_inverted_index.variational | 89 |
| abstract_inverted_index.Availability | 209 |
| abstract_inverted_index.evolutionary | 67 |
| abstract_inverted_index.experiments. | 140 |
| abstract_inverted_index.‘Dhaka’, | 87 |
| abstract_inverted_index.heterogeneity | 3 |
| abstract_inverted_index.Implementation | 211 |
| abstract_inverted_index.dimensionality | 204 |
| abstract_inverted_index.heterogeneity. | 53 |
| abstract_inverted_index.subpopulations | 173 |
| abstract_inverted_index.computationally | 70 |
| abstract_inverted_index.differentiating | 109 |
| abstract_inverted_index.differentiation | 190 |
| abstract_inverted_index.subpopulations. | 113 |
| abstract_inverted_index.https://github.com/MicrosoftGenomics/Dhaka | 230, 236 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5055091245 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I1290206253 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.65517794 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |