A mixed factors model for dimension reduction and extraction of a group structure in gene expression data

When we cluster tissue samples on the basis of genes, the number of observations to be grouped is much smaller than the dimension of feature vector. In such a case, the applicability of conventional model-based clustering is limited since the high dimensionality of feature vector leads to overfilling during the density estimation process. To overcome such difficulty, we attempt a methodological extension of the factor analysis. Our approach enables us not only to prevent from the occurrence of overfilling, but also to handle the issues of clustering, data compression and extracting a set of genes to be relevant to explain the group structure. The potential usefulness are demonstrated with the application to the leukemia dataset.

[1]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[2]  H. Akaike.  A new look at the statistical model identification , 1974 .

[3]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[4]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[5]  Debashis Ghosh,et al.  Mixture modelling of gene expression data from microarray experiments , 2002, Bioinform..

[6]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[7]  E. Lehmann.  Testing Statistical Hypotheses , 1960 .

[8]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[9]  Geoffrey J. McLachlan,et al.  A mixture model-based approach to the clustering of microarray expression data , 2002, Bioinform..

[10]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[11]  I. Mian,et al.  Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. , 2001, Physiological genomics.

[12]  Nir Friedman,et al.  Context-Specific Bayesian Clustering for Gene Expression Data , 2002, J. Comput. Biol..

[13]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[14]  N. Sampas,et al.  Molecular classification of cutaneous malignant melanoma by gene expression profiling , 2000, Nature.

[15]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[16]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[17]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[18]  Wei-Chien Chang.  On using Principal Components before Separating a Mixture of Two Multivariate Normal Distributions , 1983 .

[19]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[20]  Ian Holmes,et al.  Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data , 2000, ISMB.