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Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples:

Jin-Xing Liu, Dong Wang, Ying-Lian Gao

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|February 11, 2017

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Non-negative Matrix Factorization (NMF) effectively identifies differentially expressed genes and clusters samples in gene expression data. This survey summarizes NMF models and algorithms for data mining and machine learning applications.

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Non-negative Matrix Factorization (NMF) is a dimensionality reduction technique.
NMF assumes non-negativity for physical meaningfulness in data processing.
NMF has gained traction for complex data mining and machine learning, particularly in gene expression analysis.

Purpose of the Study:

To survey research on Non-negative Matrix Factorization (NMF) applications in gene expression data analysis.
To focus on NMF for identifying differentially expressed genes and clustering samples.
To summarize NMF models, properties, principles, algorithms, and their variations.

Main Methods:

Review of existing literature on Non-negative Matrix Factorization (NMF).

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Analysis of NMF models, including generalizations, extensions, and modifications.

Summarization of NMF algorithms and their theoretical underpinnings.

Main Results:

NMF demonstrates effectiveness in identifying differentially expressed genes.
NMF proves valuable for clustering samples in gene expression datasets.
Experimental results validate the performance of various NMF algorithms.

Conclusions:

Non-negative Matrix Factorization (NMF) is a powerful tool for gene expression data analysis.
NMF facilitates both the identification of gene expression patterns and sample classification.
The surveyed NMF approaches offer robust solutions for bioinformatics challenges.