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      The non-negative matrix factorization toolbox for biological data mining

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          Abstract

          Background

          Non-negative matrix factorization (NMF) has been introduced as an important method for mining biological data. Though there currently exists packages implemented in R and other programming languages, they either provide only a few optimization algorithms or focus on a specific application field. There does not exist a complete NMF package for the bioinformatics community, and in order to perform various data mining tasks on biological data.

          Results

          We provide a convenient MATLAB toolbox containing both the implementations of various NMF techniques and a variety of NMF-based data mining approaches for analyzing biological data. Data mining approaches implemented within the toolbox include data clustering and bi-clustering, feature extraction and selection, sample classification, missing values imputation, data visualization, and statistical comparison.

          Conclusions

          A series of analysis such as molecular pattern discovery, biological process identification, dimension reduction, disease prediction, visualization, and statistical comparison can be performed using this toolbox.

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          Missing value estimation methods for DNA microarrays.

          Annette Hastie, Allison Altman, John P. Brown … (2001)
          Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
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            Biclustering algorithms for biological data analysis: a survey.

            Sara Madeira, Arlindo L Oliveira (2006)
            A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.
            Article 0 comments Cited 253 times – based on 0 reviews     Review now
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              Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method

              Hyunsoo Kim, Haesun Park (2008)
              Article 0 comments Cited 81 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Yifeng Li
                Alioune Ngom
                Journal
                Journal ID (nlm-ta): Source Code Biol Med
                Journal ID (iso-abbrev): Source Code Biol Med
                Title: Source Code for Biology and Medicine
                Publisher: BioMed Central
                ISSN (Electronic): 1751-0473
                Publication date Collection: 2013
                Publication date (Electronic): 16 April 2013
                Volume: 8
                Page: 10
                Affiliations
                [1 ]School of Computer Science, University of Windsor, Windsor, Ontario, Canada
                Article
                Publisher ID: 1751-0473-8-10
                DOI: 10.1186/1751-0473-8-10
                PMC ID: 3736608
                PubMed ID: 23591137
                SO-VID: 697007ea-5be2-4467-90ad-41aa648cc74f
                Copyright © Copyright © 2013 Li and Ngom; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 30 November 2012
                Date accepted : 10 April 2013
                Categories
                Subject: Methodology

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: non-negative matrix factorization,clustering,bi-clustering,feature extraction,feature selection,classification,missing values
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: non-negative matrix factorization, clustering, bi-clustering, feature extraction, feature selection, classification, missing values

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