Evaluating Single-Cell Cluster Stability Using The Jaccard Similarity Index.
Makaleler > Tam Makale · 2020
Yazarlar: Ming Tang, Yasin KAYMAZ, Brandon L Logeman, Stephen Eichhorn, Zhengzheng S Liang, Catherine Dulac, Timothy B Sackton
Bioinformatics (Oxford, England), SCI Expanded İndekslerine Giren Dergi, Uluslararası, 2020, cilt 37, ss 2212-2214
DOI: 10.1093/bioinformatics/btaa956
Özet
Motivation: One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor and the resolution parameters, among others.