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Item response theory modeling for microarray gene expression data
Kastrin, Andrej (Author)

URLURL - Presentation file, Visit http://mrvar.fdv.uni-lj.si/pub/mz/mz6.1/kastrin.pdf New window

Abstract
The high dimensionality of global gene expression profiles, where number of variables (genes) is very large compared to the number of observations (samples), presents challenges that affect generalizability and applicability of microarray analysis. Latent variable modeling offers a promising approach to deal with high-dimensional microarray data. The latent variable model is based on a few latent variables that capture most of the gene expression information. Here, we describe how to accomplish a reduction in dimension by alatent variable methodology, which can greatly reduce the number of features used to characterize microarray data. We propose a general latent variable framework for prediction of predefined classes of samples using gene expression profiles from microarray experiments. The framework consists of (i) selection of smaller number of genes that are most differentially expressed between samples, (ii) dimension reduction using hierarchical clustering, where each cluster partition is identified as latent variable, (iii) discretization of gene expression matrix, (iv) fitting the Rasch item response model for genes in each cluster partition to estimate the expression of latent variable, and (v) construction of prediction model with latent variables as covariates to study the relationship between latent variables and phenotype. Two different microarray data sets are used to illustrate a general framework of the approach. We show that the predictive performance of our method is comparable to the current best approach based on an all-gene space. The method is general and can be applied to the other high-dimensional data problems.

Language:English
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FDV - Faculty of Social Sciences
Year:2009
Number of pages:str. 51-67
Numbering:Vol. 6, no. 1
UDC:519.7
ISSN on article:1854-0023
COBISS.SI-ID:28668253 Link is opened in a new window
Views:573
Downloads:87
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Record is a part of a journal

Title:Metodološki zvezki
Shortened title:Metodol. zv.
Publisher:Fakulteta za družbene vede
ISSN:1854-0023
COBISS.SI-ID:215795712 New window

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