nj acts logoPlease read Dr. Wei’s article in Laboratory Investigation titled, “An active learning approach for clustering single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for clustering scRNA-seq data use an unsupervised learning strategy. Since the clustering step is separated from the cell annotation and labeling step, it is not uncommon for a totally exotic clustering with poor biological interpretability to be generated—a result generally undesired by biologists. To solve this problem, we proposed an active learning (AL) framework for clustering scRNA-seq data. The AL model employed a learning algorithm that can actively query biologists for labels, and this manual labeling is expected to be applied to only a subset of cells. To read the full article.

An active learning approach for clustering single-cell RNA-seq data. Lin X, Liu H, Wei Z, Roy SB, Gao N. Lab Invest. 2021 Jul 9. PMID:34244616 DOI: 1038/s41374-021-00639-w Online ahead of print.