Name
Abstract | ||
|---|---|---|
Motivation: Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of 'drop-out' events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells.Results: scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btaa283 | BIOINFORMATICS |
Keywords | DocType | Volume |
single cell,RNA-seq,drop-outs,cell-to-cell similarity,imputation | Journal | 36 |
Issue | ISSN | Citations |
15 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Ran | 1 | 0 | 0.34 |
| Shanshan Zhang | 2 | 0 | 0.68 |
| Nicholas Lytal | 3 | 0 | 0.34 |
| Lingling An | 4 | 9 | 1.61 |