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scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising

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Abstract

Recently developed single-cell RNA-seq (scRNA-seq) technology has given researchers the chance to investigate single-cell level of disease development. Clustering is one of the most essential strategies for analyzing scRNA-seq data. Choosing high-quality feature sets can significantly enhance the outcomes of single-cell clustering and classification. But computationally burdensome and highly expressed genes cannot afford a stabilized and predictive feature set for technical reasons. In this study, we introduce scFED, a feature-engineered gene selection framework. scFED identifies prospective feature sets to eliminate the noise fluctuation. And fuse them with existing knowledge from the tissue-specific cellular taxonomy reference database (CellMatch) to avoid the influence of subjective factors. Then present a reconstruction approach for noise reduction and crucial information amplification. We apply scFED on four genuine single-cell datasets and compare it with other techniques. According to the results, scFED improves clustering, decreases dimension of the scRNA-seq data, improves cell type identification when combined with clustering algorithms, and has higher performance than other methods. Therefore, scFED offers certain benefits in scRNA-seq data gene selection.

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Data availability

We downloaded these datasets from databases provided by the National Biotechnology Information Retrieval Database (NCBI) and the European Institute for Bioinformatics (EMBL-EBI).

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Funding

This work was supported by the National Natural Science Foundation of China (61902216, 61972226, 62172254 and 62172253).

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Authors and Affiliations

  1. School of Computer Science, Qufu Normal University, Rizhao, 276826, China

    Yang Liu, Feng Li, Junliang Shang, Jinxing Liu, Juan Wang & Daohui Ge

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  1. Yang Liu
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  2. Feng Li
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Correspondence to Feng Li.

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Liu, Y., Li, F., Shang, J. et al. scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising. Interdiscip Sci Comput Life Sci 15, 590–601 (2023). https://doi.org/10.1007/s12539-023-00574-y

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