应用于微阵列数据分析的矩阵分解方法。
Matrix factorisation methods applied in microarray data analysis.
作者信息
Kossenkov Andrew V, Ochs Michael F
机构信息
The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104, USA.
出版信息
Int J Data Min Bioinform. 2010;4(1):72-90. doi: 10.1504/ijdmb.2010.030968.
Abstract
Numerous methods have been applied to microarray data to group genes into clusters that show similar expression patterns. These methods assign each gene to a single group, which does not reflect the widely held view among biologists that most, if not all, genes in eukaryotes are involved in multiple biological processes and therefore will be multiply regulated. Here, we review several methods of matrix factorisation that identify patterns of behaviour in transcriptional response and assign genes to multiple patterns. We focus on these methods rather than traditional clustering methods applied to microarray data, which assign one gene to one cluster.
摘要
众多方法已应用于微阵列数据，以将基因分组为显示相似表达模式的簇。这些方法将每个基因分配到单个组中，这并未反映生物学家中广泛持有的观点，即真核生物中的大多数（如果不是全部）基因都参与多个生物学过程，因此会受到多种调控。在这里，我们回顾了几种矩阵分解方法，这些方法可识别转录反应中的行为模式并将基因分配到多种模式中。我们关注这些方法，而不是应用于微阵列数据的传统聚类方法，传统方法将一个基因分配到一个簇中。
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本文引用的文献
1
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
IEEE Trans Pattern Anal Mach Intell. 1984 Jun;6(6):721-41. doi: 10.1109/tpami.1984.4767596.
2
Determining transcription factor activity from microarray data using Bayesian Markov chain Monte Carlo sampling.
Stud Health Technol Inform. 2007;129(Pt 2):1250-4.
3
Component retention in principal component analysis with application to cDNA microarray data.
Biol Direct. 2007 Jan 17;2:2. doi: 10.1186/1745-6150-2-2.
4
Inferential, robust non-negative matrix factorization analysis of microarray data.
Bioinformatics. 2007 Jan 1;23(1):44-9. doi: 10.1093/bioinformatics/btl550. Epub 2006 Nov 8.
5
Computational approaches to analysis of DNA microarray data.
Yearb Med Inform. 2006:91-103.
6
Independent component analysis reveals new and biologically significant structures in micro array data.
BMC Bioinformatics. 2006 Jun 8;7:290. doi: 10.1186/1471-2105-7-290.
7
LS-NMF: a modified non-negative matrix factorization algorithm utilizing uncertainty estimates.
BMC Bioinformatics. 2006 Mar 28;7:175. doi: 10.1186/1471-2105-7-175.
8
Determination of strongly overlapping signaling activity from microarray data.
BMC Bioinformatics. 2006 Feb 28;7:99. doi: 10.1186/1471-2105-7-99.
9
Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization.
BMC Bioinformatics. 2006 Feb 17;7:78. doi: 10.1186/1471-2105-7-78.
10
Improving molecular cancer class discovery through sparse non-negative matrix factorization.
Bioinformatics. 2005 Nov 1;21(21):3970-5. doi: 10.1093/bioinformatics/bti653.
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