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Differentially expressed genes selection via Laplacian regularized low-rank representation method.

Ya-Xuan Wang1, Jin-Xing Liu1, Ying-Lian Gao2

  • 1School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.

Computational Biology and Chemistry
|October 4, 2016
PubMed
Summary

A new Laplacian regularized Low-Rank Representation (LLRR) method effectively identifies differentially expressed genes from genomic data. This approach captures non-linear geometric information, outperforming existing methods in gene selection.

Keywords:
Differentially expressed genesGenomic dataGraph regularizationLow-rank representation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic data generation is rapidly increasing due to DNA microarray and next-generation sequencing technologies.
  • Extracting differentially expressed genes from large genomic datasets presents a significant challenge.
  • Existing Low-Rank Representation (LRR) methods excel at subspace analysis but neglect intrinsic data geometry.

Purpose of the Study:

  • To propose a novel method, Laplacian regularized Low-Rank Representation (LLRR), for enhanced differentially expressed gene selection.
  • To integrate graph regularization into LRR to capture non-linear geometric structures within genomic data.
  • To identify significant genes by decomposing genomic data matrices into low-rank and sparse components.

Main Methods:

  • Developed the Laplacian regularized Low-Rank Representation (LLRR) method by incorporating graph regularization into LRR.
  • Applied LLRR to decompose genomic data observation matrices into low-rank and sparse matrices via an optimization problem.
  • Identified differentially expressed genes as sparse perturbation signals within the computed sparse matrix.
  • Utilized the Gene Ontology (GO) tool for functional analysis of selected genes and compared p-values with other methods.

Main Results:

  • The LLRR method successfully captures intrinsic non-linear geometric information in genomic data.
  • Differentially expressed genes were effectively selected based on the sparse matrix derived from the LLRR decomposition.
  • Comparative analysis on simulation and real genomic datasets demonstrated superior performance of LLRR over other methods in gene selection.

Conclusions:

  • LLRR offers an improved approach for identifying differentially expressed genes by leveraging data's geometric properties.
  • The method provides a robust framework for analyzing complex genomic datasets and extracting biologically relevant signals.
  • LLRR shows significant potential for advancing gene expression analysis in bioinformatics and computational biology.