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| Status |
Public on Mar 01, 2020 |
| Title |
Ensemble learning for classifying single-cell data and projection across reference atlases |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms.
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| Overall design |
We developed an ensemble classifier of scRNA-seq, single-nuclei RNA-sequencing (snRNA-seq), and bulk-extraction RNA-sequencing (RNA-seq) data: Ensemble Learning for classifying Single-cell data and projection across reference Atlases (ELSA; https://github.com/diazlab/ELSA). We trained ELSA on public atlases and tested it on published single-cell data, novel scRNA-seq and snRNA-seq of human glioma tissues (4 patients, >11K cells, Table S1 and S2).
Note: Submitter did not submit the raw data files due to privacy concerns for patients.
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| Contributor(s) |
Wang L, Catalan FL, Babikir H, Shamardani K, Diaz A |
| Citation(s) |
32105316 |
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| Submission date |
Dec 13, 2019 |
| Last update date |
Jun 01, 2020 |
| Contact name |
Aaron Diaz |
| E-mail(s) |
aaron.diaz@ucsf.edu
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| Organization name |
University of California, San Francisco
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| Department |
Neurological Surgery
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| Lab |
Diaz Lab
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| Street address |
1450 3rd St
|
| City |
San Francisco |
| State/province |
CA |
| ZIP/Postal code |
94158 |
| Country |
USA |
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| Platforms (1) |
| GPL24676 |
Illumina NovaSeq 6000 (Homo sapiens) |
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| Samples (4)
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| Relations |
| BioProject |
PRJNA595499 |
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