Study
Ensemble learning for classifying single-cell data and projection across reference atlases
| Study ID | Alternative Stable ID | Type |
|---|---|---|
| EGAS00001004283 | Other |
Study Description
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.
Study Datasets 4 datasets.
Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data
| Dataset ID | Description | Technology | Samples |
|---|---|---|---|
| EGAD00001006017 |
Single Cell RNA-Seq of Primary GBM. Gender Female, Age, 57.
|
Illumina NovaSeq 6000 | 1 |
| EGAD00001006018 |
Mixed Sample of scRNA-Seq primary low grade glioma. Genders: Male, Age: 34, 44.
|
Illumina NovaSeq 6000 | 1 |
| EGAD00001006019 |
Single Cell-RNA Seq of Wildtype Primary GBM for Female, Age 50.
|
Illumina NovaSeq 6000 | 1 |
| EGAD00001006020 |
Primary diffuse astrocytoma G3 Male, 74
|
Illumina NovaSeq 6000 | 1 |
Who archives the data?
