Paper Info
Title
Bayesian integrated functional analysis of microarray data.
Abstract
The statistical analysis of microarray data usually proceeds in a sequential manner, with the output of the previous step always serving as the input of the next one. However, the methods currently used in such analyses do not properly account for the fact that the intermediate results may not always be correct, then leading to cumulating error in the inferences drawn based on such steps.Here we show that, by an application of hierarchical Bayesian methodology, this sequential procedure can be replaced by a single joint analysis, while systematically accounting for the uncertainties in this process. Moreover, we can also integrate relevant functional information available from databases into such an analysis, thereby increasing the reliability of the biological conclusions that are drawn. We illustrate these points by analysing real data and by showing that the genes can be divided into categories of interest, with the defining characteristic depending on the biological question that is considered. We contend that the proposed method has advantages at two levels. First, there are gains in the statistical and biological results from the analysis of this particular dataset. Second, it opens up new possibilities in analysing microarray data in general.
Year
DOI
Venue
2004
10.1093/bioinformatics/bth338
Bioinformatics
Keywords
Field
DocType
relevant functional information,bayesian integrated functional analysis,statistical analysis,sequential procedure,sequential manner,biological question,biological result,microarray data,single joint analysis,biological conclusion,cumulant
Data mining,Computer science,Microarray analysis techniques,Bioinformatics,Gene chip analysis,Statistical analysis,Bayesian probability
Journal
Volume
Issue
ISSN
20
17
1367-4803
Citations 
PageRank 
References 
7
0.86
2
Authors
4
Name
Order
Citations
PageRank
Madhuchhanda Bhattacharjee171.54
Colin C. Pritchard281.92
Peter S. Nelson3174.18
E. Arjas44311.58