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Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data.

Jin-Xing Liu¹, Chuan-Yuan Wang¹, Ying-Lian Gao²

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

Interdisciplinary Sciences, Computational Life Sciences

|June 2, 2021

Summary

This summary is machine-generated.

This study introduces Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR), a novel model for single-cell RNA sequencing data analysis. ATV-LRR effectively identifies cell subtypes and functions by reducing noise and preserving crucial cellular details.

Keywords:

Clustering Low rank Sample subspace Single-cell RNA sequencing data Total variation

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Identifying cell subtypes and functions through clustering is a key goal in scRNA-seq data analysis.
Existing methods face challenges with high noise and cellular heterogeneity in scRNA-seq data.

Purpose of the Study:

To introduce a novel computational model, Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR), for scRNA-seq data analysis.
To enhance cell clustering accuracy and stability in the presence of noise and heterogeneity.
To enable automatic identification of cell populations without prior information.

Main Methods:

Developed the ATV-LRR model integrating low-rank representation and adaptive total variation.
Utilized low-rank representation to learn cell similarity and segment subspace structures.
Incorporated adaptive total variation to denoise data and preserve essential cell features.
Integrated the maximum eigenvalue method for automatic cell population identification.

Main Results:

The ATV-LRR model effectively reconstructs the low-rank subspace structure of scRNA-seq data.
Adaptive total variation successfully removes noise while retaining critical cellular details.
The maximum eigenvalue method enables robust, automatic cell population detection.
ATV-LRR demonstrates superior effectiveness and stability in cell type detection compared to existing methods.

Conclusions:

ATV-LRR provides a powerful and stable approach for analyzing complex scRNA-seq data.
The model enhances the identification of cell subtypes and functions by addressing noise and heterogeneity.
ATV-LRR offers a significant advancement in computational tools for single-cell genomics research.