USP Electronic Research Repository

Clustering of small - sample single - cell RNA - seq data via feature clustering and selection

Vans, Edwin and Sharma, Alokanand and Patil, Ashwini and Shigemizu, Daichi and Tsunoda, Tatsuhiko (2019) Clustering of small - sample single - cell RNA - seq data via feature clustering and selection. [Conference Proceedings]

PDF - Published Version
Download (405kB) | Preview


We present FeatClust, a software tool for clustering small sample size single-cell RNA-Seq datasets. The FeatClust approach is based on feature selection. It divides features into several groups by performing agglomerative hierarchical clustering and then iteratively clustering the samples and removing features belonging to groups with the least variance across samples. The optimal number of feature groups is selected based on silhouette analysis on the clustered data, i.e., selecting the clustering with the highest average silhouette coefficient. FeatClust also allows one to visually choose the number of clusters if it is not known, by generating silhouette plot for a chosen number of groupings of the dataset. We cluster five small sample single-cell RNA-seq datasets and use the adjusted rand index metric to compare the results with other clustering packages. The results are promising and show the effectiveness of FeatClust on small sample size datasets.

Item Type: Conference Proceedings
Additional Information: DOI:
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User: Alokanand Sharma
Date Deposited: 12 Sep 2019 00:57
Last Modified: 10 Jun 2020 01:26

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...