https://www.selleckchem.com/pr....oducts/usp25-28-inhi
It achieves a 22% improvement in clustering and more accurately estimates the number of clusters when compared with other tools. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data. The installation instructions and documentation of FEATS is available at https//edwinv87.github.io/feats/. Supplemen
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