Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Abstract/Contents
- Abstract
- Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical to identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. Here, we propose a novel similarity-learning framework, SIMLR (single-cell interpretation via multiple kernel learning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization applications. Benchmarking against state-of-the-art methods for these applications, we used SIMLR to re-analyse seven representative single-cell data sets, including high-throughput droplet-based data sets with tens of thousands of cells. We show that SIMLR greatly improves clustering sensitivity and accuracy, as well as the visualization and interpretability of the data.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wang, Bo, (Artificial intelligence scientist) |
|---|---|
| Primary advisor | Batzoglou, Serafim |
| Thesis advisor | Batzoglou, Serafim |
| Thesis advisor | Curtis, Christina |
| Thesis advisor | Kundaje, Anshul, 1980- |
| Advisor | Curtis, Christina |
| Advisor | Kundaje, Anshul, 1980- |
| Associated with | Stanford University, Computer Science Department. |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bo Wang. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Bo Wang
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