Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

Soumita Seth, Saurav Mallik (), Atikul Islam, Tapas Bhadra, Arup Roy, Pawan Kumar Singh, Aimin Li and Zhongming Zhao ()
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Soumita Seth: Department of Computer Science and Engineering, Future Institute of Engineering and Management, Narendrapur, Kolkata 700150, West Bengal, India
Saurav Mallik: Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA
Atikul Islam: Department of Computer Science and Engineering, University of Kalyani, Kalyani 741235, West Bengal, India
Tapas Bhadra: Department of Computer Science and Engineering, Aliah University, Kolkata 700160, West Bengal, India
Arup Roy: Department of Computer Science and Engineering, Budge Budge Institute of Technology, Kolkata 700137, West Bengal, India
Pawan Kumar Singh: Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Kolkata 700106, West Bengal, India
Aimin Li: Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Zhongming Zhao: Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA

Mathematics, 2023, vol. 11, issue 20, 1-26

Abstract: In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, C y p 2 b 10 , M t 1 , A l p i , along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

Keywords: single-cell sequencing; gene signature; data imputation; feature selection; maximum relevance and minimum redundancy; shrinkage clustering; differentially expressed markers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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