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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
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Network analysis based on low-rank method for mining information on integrated data of multi-cancers
Mi-Xiao Hou1, Ying-Lian Gao2, Jin-Xing Liu3
1School of Information Science and Engineering, Qufu Normal University, Rizhao, China.
Computational Biology and Chemistry
|December 20, 2018
Summary
Robust Principal Component Analysis (RPCA) effectively reduces noise in multi-cancer genomic data. This enables more accurate gene co-expression network construction and identification of cancer-associated genes and pathways.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Cancer sequencing data often contains significant noise, hindering accurate analysis.
- Gene co-expression network analysis is crucial for understanding cancer mechanisms but is sensitive to data noise.
Purpose of the Study:
- To address noise in multi-cancer genomic data from The Cancer Genome Atlas (TCGA).
- To construct robust gene co-expression networks after noise reduction.
- To identify cancer-associated genes and pathways using denoised network data.
Main Methods:
- Application of Robust Principal Component Analysis (RPCA) for noise reduction.
- Construction of gene co-expression networks using denoised TCGA multi-cancer data.
- Node and pathway enrichment analysis on the denoised networks.
Main Results:
- RPCA significantly improved the orderliness and neatness of gene expression data.
- Denoised networks revealed richer pathway enrichment information compared to raw data.
- Analysis of node betweenness centrality identified abnormally expressed genes and pathways linked to multiple cancers.
Conclusions:
- RPCA is an effective method for denoising cancer genomic data and improving network analysis.
- The denoised networks facilitate the discovery of novel cancer-related genes and pathways.
- The study identified candidate genes potentially associated with multiple cancer types.
Keywords:
Abnormally expressed genesGene co-expression networkIntegrated dataMulti-cancersNoise reductionMore Related Videos
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