Name
Title | ||
|---|---|---|
Ensemble learning for classifying single-cell data and projection across reference atlases. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btaa137 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 36 | 11 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Wang | 1 | 0 | 0.34 |
| Francisca Catalan | 2 | 0 | 0.34 |
| Karin Shamardani | 3 | 0 | 0.34 |
| Husam Babikir | 4 | 0 | 0.34 |
| Aaron Diaz | 5 | 7 | 1.64 |