Name
Abstract | ||
|---|---|---|
Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering forces separation, reusing the same dataset generates artificially low p-values and hence false discoveries. In this work, we introduce a valid post-clustering differential analysis framework which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1101/463265 | research in computational molecular biology |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesse Zhang | 1 | 10 | 4.58 |
| Govinda M. Kamath | 2 | 141 | 8.44 |
| David N. C. Tse | 3 | 2078 | 246.17 |