期刊
CURRENT BIOINFORMATICS
卷 17, 期 7, 页码 578-585出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1574893617666220608123804
关键词
Feature ranking; bioinformatics; machine learning; python; feature selection; visualization
资金
- National Nature Scien-tific Foundation of China
- Key technology research program of Inner Mongolia Autonomous Region [62171241, 62061034, 61861036]
- Sci-ence and Technology Major Project of Inner Mongolia Au-tonomous Region of China [2021GG0398]
- [2019ZD031]
Feature-scML is an effective toolkit for analyzing single-cell RNA omics datasets, automating the machine learning process, and customizing visual analysis of the results.
Background: Inferring feature importance is both a promise and challenge in bioinformatics and computational biology. While multiple biological computation methods exist to identify decisive factors of single cell subpopulation, there is a need for a comprehensive toolkit that presents an intuitive and custom view of the feature importance.Objective: We developed a Feature-scML, a scalable and friendly toolkit that allows the users to visualize and reveal decisive factors for single-cell omics analysis.Methods: Feature-scML incorporates the following three main functions: (i) There are seven feature selection algorithms to comprehensively score and rank every feature. (ii) Four machine learning approaches and increment feature selection (IFS) strategy jointly determine the number of selected features. (iii) The Feature-scML supports the visualized feature importance, model performance evaluation, and model interpretation. The source code is available at .Results: We systematically compared the performance of seven feature selection algorithms from Feature-scML on two single cell transcriptome datasets. It demonstrates the effectiveness and power of the Feature-scML.Conclusion: Feature-scML is effective for analyzing single-cell RNA omics datasets to automate the machine learning process and customize the visual analysis from the results.
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