4.7 Article Proceedings Paper

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

期刊

BIOINFORMATICS
卷 37, 期 -, 页码 I358-I366

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab273

关键词

-

资金

  1. National Science Foundation [DBI-1846216]
  2. NIH/NIGMS [R01GM120507]
  3. Johnson & Johnson WiSTEM2D Award
  4. Sloan Research Fellowship
  5. UCLA David Geffen School of Medicine W.M. Keck Foundation Junior Faculty Award
  6. NIH/NINDS [R01NS117148]

向作者/读者索取更多资源

The study introduces a new method, scPNMF, for selecting informative genes from scRNA-seq data, which can better distinguish cell types and facilitate cell type prediction in new data. Experimental comparisons demonstrate that scPNMF outperforms existing gene selection methods and can guide the design of targeted gene profiling experiments and cell type annotation.
Motivation: Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Results: Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据