Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interp...
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| Veröffentlicht in: | Nature methods Jg. 16; H. 10; S. 1007 - 1015 |
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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United States
Nature Publishing Group
01.10.2019
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| ISSN: | 1548-7091, 1548-7105, 1548-7105 |
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| Abstract | Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma. |
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| AbstractList | Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma. Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma. |
| Author | McPherson, Andrew Mottok, Anja Wiens, Matt Chan, Tim Walters, Pascale Aoki, Tomohiro Lim, Jamie L P Steidl, Christian Campbell, Kieran R Shah, Sohrab P Chavez, Elizabeth A Hewitson, Brittany Weng, Andrew P Sarkozy, Clementine Ceglia, Nicholas O'Flanagan, Ciara Aparicio, Samuel Wang, Xuehai McAlpine, Jessica N Chong, Lauren Zhang, Allen W Lai, Daniel |
| Author_xml | – sequence: 1 givenname: Allen W orcidid: 0000-0002-7606-089X surname: Zhang fullname: Zhang, Allen W organization: BC Children's Hospital Research, Vancouver, British Columbia, Canada – sequence: 2 givenname: Ciara surname: O'Flanagan fullname: O'Flanagan, Ciara organization: Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 3 givenname: Elizabeth A surname: Chavez fullname: Chavez, Elizabeth A organization: Centre for Lymphoid Cancer, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 4 givenname: Jamie L P surname: Lim fullname: Lim, Jamie L P organization: Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA – sequence: 5 givenname: Nicholas surname: Ceglia fullname: Ceglia, Nicholas organization: Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA – sequence: 6 givenname: Andrew surname: McPherson fullname: McPherson, Andrew organization: Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 7 givenname: Matt surname: Wiens fullname: Wiens, Matt organization: Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 8 givenname: Pascale surname: Walters fullname: Walters, Pascale organization: Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 9 givenname: Tim surname: Chan fullname: Chan, Tim organization: Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 10 givenname: Brittany surname: Hewitson fullname: Hewitson, Brittany organization: Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 11 givenname: Daniel orcidid: 0000-0001-9203-6323 surname: Lai fullname: Lai, Daniel organization: Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 12 givenname: Anja surname: Mottok fullname: Mottok, Anja organization: Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany – sequence: 13 givenname: Clementine surname: Sarkozy fullname: Sarkozy, Clementine organization: Centre for Lymphoid Cancer, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 14 givenname: Lauren surname: Chong fullname: Chong, Lauren organization: Centre for Lymphoid Cancer, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 15 givenname: Tomohiro orcidid: 0000-0001-6782-8361 surname: Aoki fullname: Aoki, Tomohiro organization: Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada – sequence: 16 givenname: Xuehai surname: Wang fullname: Wang, Xuehai organization: Terry Fox Laboratory, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 17 givenname: Andrew P surname: Weng fullname: Weng, Andrew P organization: Terry Fox Laboratory, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 18 givenname: Jessica N surname: McAlpine fullname: McAlpine, Jessica N organization: Department of Obstetrics & Gynaecology, University of British Columbia, Vancouver, British Columbia, Canada – sequence: 19 givenname: Samuel orcidid: 0000-0002-0487-9599 surname: Aparicio fullname: Aparicio, Samuel organization: Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada – sequence: 20 givenname: Christian surname: Steidl fullname: Steidl, Christian organization: Centre for Lymphoid Cancer, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada – sequence: 21 givenname: Kieran R orcidid: 0000-0003-1981-5763 surname: Campbell fullname: Campbell, Kieran R email: kieran.campbell@stat.ubc.ca, kieran.campbell@stat.ubc.ca, kieran.campbell@stat.ubc.ca organization: UBC Data Science Institute, University of British Columbia, Vancouver, British Columbia, Canada. kieran.campbell@stat.ubc.ca – sequence: 22 givenname: Sohrab P orcidid: 0000-0001-6402-523X surname: Shah fullname: Shah, Sohrab P email: shahs3@mskcc.org, shahs3@mskcc.org, shahs3@mskcc.org organization: Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada. shahs3@mskcc.org |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31501550$$D View this record in MEDLINE/PubMed |
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| Snippet | Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to... |
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| SubjectTerms | Annotations Automation Cancer Clustering Computer simulation Datasets Gene expression Gene Expression Profiling Gene sequencing Humans Laboratories Lymphoma Lymphoma, Follicular - immunology Lymphoma, Follicular - pathology Mapping Medical research Ovarian cancer Probabilistic models Probability Research centers Ribonucleic acid RNA Sequence Analysis, RNA - methods Single-Cell Analysis - methods Stem cells Tumor Microenvironment Tumors |
| Title | Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling |
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