A systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data
Autor: | Hua He, Li Xing, Xiaowen Cao, Xuekui Zhang, Elham Majd, Junhua Gu |
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Rok vydání: | 2021 |
Předmět: |
Elastic net regularization
Genomics (q-bio.GN) FOS: Computer and information sciences Computer Science - Machine Learning Cell phenotype business.industry Computer science Sequencing data RNA Machine learning computer.software_genre Statistics - Applications Machine Learning (cs.LG) Annotation Text mining Software ComputingMethodologies_PATTERNRECOGNITION FOS: Biological sciences Benchmark (computing) Quantitative Biology - Genomics Applications (stat.AP) Artificial intelligence business computer |
DOI: | 10.48550/arxiv.2110.00681 |
Popis: | 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, and false-positive rate. In the second part, we evaluated gene selection performance using published simulated data sets with a known list of real genes. Conclusion: The study outcomes showed that ElasticNet with interactions performed best in small and medium data sets. NB was another appropriate method for medium data sets. In large data sets, XGB works excellent. Ensemble algorithms were not significantly superior to individual machine learning methods. Adding interactions to ElasticNet can help, and the improvement was significant in small data sets. Comment: 21 pages, 4 figures, 1 table |
Databáze: | OpenAIRE |
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