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A systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data by Cao, Xiaowen; Xing, Li; Majd, Elham; He, Hua; Gu, Junhua; Zhang, Xuekui is a scholarly article available to read on EtoBox.

What is A systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data about?

Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible. Garnett is a cell-type annotation software based the on elastic net method. Besides cell-type annotation, supervised machine learning methods can also be applied to predict other cell phenotypes from genomic data. Despite the popularity of such applications, there is no existing study to systematically investigate the performance of those supervised algorithms in various sizes of scRNA-seq data sets. Methods and Results: This study evaluates 13 popular supervised machine learning algorithms to classify cell phenotypes, using published real and simulated data sets with diverse cell sizes. The benchmark contained two parts. In the first part, we used real data sets to assess the popular supervised algorithms' computing speed and cell phenotype classification performance. The classification performances were evaluated using AUC statistics, F1-score, precision, recall, a

Author

Cao, Xiaowen; Xing, Li; Majd, Elham; He, Hua; Gu, Junhua; Zhang, Xuekui

Published

2021

Language

EN