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).
References
Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, Bodenmiller B, Campbell P, Carninci P, Clatworthy M et al (2017) The human cell atlas. Elife 6:e27041. https://doi.org/10.7554/eLife.27041
Tian Y, Zhang MY, Zhao AH, Kong L, Wang JJ, Shen W, Li L (2021) Single-cell transcriptomic profiling provides insights into the toxic effects of Zearalenone exposure on primordial follicle assembly. Theranostics 11(11):5197–5213. https://doi.org/10.7150/thno.58433
Potter SS (2018) Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol 14(8):479–492. https://doi.org/10.1038/s41581-018-0021-7
Su K, Yu T, Wu H (2021) Accurate feature selection improves single-cell RNA-seq cell clustering. Brief Bioinform. https://doi.org/10.1093/bib/bbab034
He X, Deng C, Niyogi P (2005) Laplacian score for feature selection. In: Advances in neural information processing systems 18 [Neural Information Processing Systems, NIPS 2005, December 5–8, 2005, Vancouver, British Columbia, Canada]. pp 507–514. https://dl.acm.org/doi/https://doi.org/10.5555/2976248.2976312
Wang L, Li J, Qin H, Xu J, Zhang X, Huang L (2019) Selecting near-infrared hyperspectral wavelengths based on one-way ANOVA to identify the origin of Lycium barbarum. In: 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). IEEE: 122–125. https://ieeexplore.ieee.org/abstract/document/8735444
Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al (2021) Integrated analysis of multimodal single-cell data. Cell 184(13):3573.e3529-3587.e3529. https://doi.org/10.1016/j.cell.2021.04.048
Chen H, Ryu J, Vinyard ME, Lerer A, Pinello L (2022) SIMBA: SIngle-cell eMBedding Along with features. bioRxiv. https://doi.org/10.1101/2021.10.17.464750
Lall S, Ray S, Bandyopadhyay S (2021) RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data. PLoS Comput Biol 17(10):e1009464. https://doi.org/10.1371/journal.pcbi.1009464
Li L, Tang H, Xia R, Dai H, Liu R, Chen L (2022) Intrinsic entropy model for feature selection of scRNA-seq data. J Mol Cell Biol 14(2):2. https://doi.org/10.1093/jmcb/mjac008
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36(5):411–420. https://doi.org/10.1038/nbt.4096
Cui H, Zhou C, Dai X, Liang Y, Paffenroth R, Korkin D (2017) Boosting gene expression clustering with system-wide biological information: a robust autoencoder approach. bioRxiv. https://doi.org/10.1504/IJCBDD.2020.105113
Yip SH, Wang P, Kocher JA, Sham PC, Wang J (2017) Corrigendum: Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Res 45(22):13097. https://doi.org/10.1093/nar/gkx1189
Shao X, Liao J, Lu X, Xue R, Ai N, Fan X (2020) scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. iScience 23(3):100882. https://doi.org/10.1016/j.isci.2020.100882
Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR et al (2017) SC3: consensus clustering of single-cell RNA-seq data. Nat Methods 14(5):483–486. https://doi.org/10.1038/nmeth.4236
Lall S, Ghosh A, Ray S, Bandyopadhyay S (2022) sc-REnF: an entropy guided robust feature selection for single-cell RNA-seq data. Brief Bioinform. https://doi.org/10.1093/bib/bbab517
Lall S, Sinha D, Ghosh A, Sengupta D, Bandyopadhyay S (2021) Stable feature selection using copula based mutual information. Pattern Recognit 112(1):107697. https://doi.org/10.1016/j.patcog.2020.107697
Radanliev P, De Roure D (2023) New and emerging forms of data and technologies: literature and bibliometric review. Multimed Tools Appl 82(2):2887–2911. https://doi.org/10.1007/s11042-022-13451-5
Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, Luo T, Xu L, Liao G, Yan M et al (2019) Cell Marker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res 47(D1):D721–D728. https://doi.org/10.1093/nar/gky900
Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, Saadatpour A, Zhou Z, Chen H, Ye F et al (2018) Mapping the mouse cell atlas by microwell-seq. Cell 172(5):1091.e1017-1107.e1017. https://doi.org/10.1016/j.cell.2018.02.001
Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, Xu L, Luo T, Yan H, Long Z et al (2019) CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res 47(D1):D900–D908. https://doi.org/10.1093/nar/gky939
Wang LF, Shi CY, Lin SZ, Qin PL, Wang YL (2020) Convolutional sparse representation and local density peak clustering for medical image fusion. Int J Pattern Recognit Artif Intell 34(7):575–592. https://doi.org/10.1142/S0218001420570037
Oller-Moreno S, Kloiber K, Machart P, Bonn S (2021) Algorithmic advances in machine learning for single-cell expression analysis. Curr Opin Syst Biol 25:27–33. https://doi.org/10.1016/j.coisb.2021.02.002
Lin C, Jain S, Kim H, Bar-Joseph Z (2017) Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res 45(17):e156. https://doi.org/10.1093/nar/gkx681
Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, Li N, Szpankowski L, Fowler B, Chen P et al (2014) Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 32(10):1053–1058. https://doi.org/10.1038/nbt.2967
Yan L, Yang M, Guo H, Yang L, Wu J, Li R, Liu P, Lian Y, Zheng X, Yan J et al (2013) Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 20(9):1131–1139. https://doi.org/10.1038/nsmb.2660
Engel I, Seumois G, Chavez L, Samaniego-Castruita D, White B, Chawla A, Mock D, Vijayanand P, Kronenberg M (2016) Innate-like functions of natural killer T cell subsets result from highly divergent gene programs. Nat Immunol 17(6):728–739. https://doi.org/10.1038/ni.3437
Do VH, Canzar S (2021) A generalization of t-SNE and UMAP to single-cell multimodal omics. Genome Biol 22(1):130. https://doi.org/10.1186/s13059-021-02356-5
Jiahu Q, Weiming F, Huijun G, Wei Xing Z (2017) Distributed k -means algorithm and fuzzy c-means algorithm for sensor networks based on multiagent consensus theory. IEEE Trans Cybern 47(3):772–783. https://doi.org/10.1109/TCYB.2016.2526683
Gárate-Escamila AK, Hajjam El Hassani A, Andrès E (2020) Classification models for heart disease prediction using feature selection and PCA. Inform Med Unlocked. https://doi.org/10.1016/j.imu.2020.100330
Jiang H, Sohn LL, Huang H, Chen L (2018) Single cell clustering based on cell-pair differentiability correlation and variance analysis. Bioinformatics 34(21):3684–3694. https://doi.org/10.1093/bioinformatics/bty390
Strehl A, Ghosh J (2003) Cluster ensembles –- a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617. https://doi.org/10.1162/153244303321897735
Meila M (2007) Comparing clusterings - an information based distance. J Multivar Anal 98(5):873–895. https://doi.org/10.1016/j.jmva.2006.11.013
Zhang SH, Wong HS, Shen Y (2012) Generalized adjusted rand indices for cluster ensembles. Pattern Recognit 45(6):2214–2226. https://doi.org/10.1016/j.patcog.2011.11.017
Zhang NN, Liu JX, Zheng CH, Wang J (2022) SLRRSC: single-cell type recognition method based on similarity and graph regularization constraints. IEEE J Biomed Health Inform 26(7):3556–3566. https://doi.org/10.1109/JBHI.2022.3148286
Ren X, Zheng L, Zhang Z (2019) SSCC: a novel computational framework for rapid and accurate clustering large-scale single cell RNA-seq data. Genom Proteom Bioinform 17(2):201–210. https://doi.org/10.1016/j.gpb.2018.10.003
Zhang DJ, Gao YL, Zhao JX, Zheng CH, Liu JX (2022) A new graph autoencoder-based consensus-guided model for scRNA-seq cell type detection. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3190289
Petegrosso R, Li Z, Kuang R (2020) Machine learning and statistical methods for clustering single-cell RNA-sequencing data. Brief Bioinform 21(4):1209–1223. https://doi.org/10.1093/bib/bbz063
Belgiu M, Dragut L (2016) Random forest in remote sensing: a review of applications and future directions. Isprs J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Chen P-H, Lin C-J, Schölkopf B (2005) A tutorial onν-support vector machines. Appl Stoch Model Bus Ind 21(2):111–136. https://doi.org/10.1002/asmb.537
Jiang L, Cai Z, Wang D, Jiang S (2007) Survey of improving K-nearest-neighbor for classification. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007): 24–27 Aug. 679–683. https://ieeexplore.ieee.org/document/4406010
Kordzakhia N, Mishra GD, Reiersølmoen L (2001) Robust estimation in the logistic regression model. J Stat Plan Inference 98(1–2):211–223. https://doi.org/10.1016/s0378-3758(00)00312-8
Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238. https://doi.org/10.1109/TPAMI.2005.159
Bennasar M, Hicks Y, Setchi R (2015) Feature selection using joint mutual information maximisation. Expert Syst Appl 42(22):8520–8532. https://doi.org/10.1016/j.eswa.2015.07.007
Brown G, Pocock A, Zhao MJ, Lujan M (2012) Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res 13(1):27–66. https://doi.org/10.5555/2503308.2188387
Meyer PE, Schretter C, Bontempi G (2008) Information-theoretic feature selection in microarray data using variable complementarity. IEEE J Sel Top Signal Process 2(3):261–274. https://doi.org/10.1109/Jstsp.2008.923858
Zhong S, Zhang S, Fan X, Wu Q, Yan L, Dong J, Zhang H, Li L, Sun L, Pan N et al (2018) A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555(7697):524–528. https://doi.org/10.1038/nature25980
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This work was supported by the National Natural Science Foundation of China (61902216, 61972226, 62172254 and 62172253).
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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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DOI: https://doi.org/10.1007/s12539-023-00574-y