Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets.

ABSTRACT: Tumors are complex biological entities that comprise cell types of different origins, with different mutational profiles and different patterns of transcriptional dysregulation. The exploration of data related to cancer biology requires careful analytical methods to reflect the heterogeneity of cell populations in cancer samples. Single-cell techniques are now able to capture the transcriptional profiles of individual cells. However, the complexity of RNA-seq data, especially in cancer samples, makes it challenging to cluster single-cell profiles into groups that reflect the underlying cell types. We have developed a framework for a systematic examination of single-cell RNA-seq clustering algorithms for cancer data, which uses a range of well-established metrics to generate a unified quali

SUBMITTER: Mahalanabis A 

PROVIDER: S-EPMC9677128 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature