Comments on selected fundamental aspects of microarray analysis

Authors:
Alessandra Riva;Anne-Sophie Carpentier;Bruno Torrésani;Alain Hénaut
Affiliations:
Laboratoire Génome et Informatique UMR 8116 Tour Evry2, 523 Place des Terrasses, 91034 Evry Cedex, France;Laboratoire Génome et Informatique UMR 8116 Tour Evry2, 523 Place des Terrasses, 91034 Evry Cedex, France;LATP, CMI, Université de Provence, 39 rue Joliot-Curie, 13453 Marseille Cedex 13, France;Laboratoire Génome et Informatique UMR 8116 Tour Evry2, 523 Place des Terrasses, 91034 Evry Cedex, France
Venue:
Computational Biology and Chemistry
Year:
2005

Citing 14
Cited 2

Independent component analysis: algorithms and applications

Neural Networks
Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis

Bioinformatics
Reducing the variability in cDNA microarray image processing by Bayesian inference

Bioinformatics
Gaussian mixture clustering and imputation of microarray data

Bioinformatics
Analysing microarray data using modular regulation analysis

Bioinformatics
Analysis of variance components in gene expression data

Bioinformatics
Normalization of single-channel DNA array data by principal component analysis

Bioinformatics
Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data

Bioinformatics
A novel approach for increasing sensitivity and correcting saturation artifacts of radioactively labeled cDNA arrays

Bioinformatics
Analyzing time series gene expression data

Bioinformatics
The Baumgartner-Weiß-Schindler test for the detection of differentially expressed genes in replicated microarray experiments

Bioinformatics
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Bioinformatics
The operons, a criterion to compare the reliability of transcriptome analysis tools: ICA is more reliable than ANOVA, PLS and PCA

Computational Biology and Chemistry
The difficult interpretation of transcriptome data: the case of the GATC regulatory network

Computational Biology and Chemistry

Generalized Power Method for Sparse Principal Component Analysis

The Journal of Machine Learning Research
Interpreting microarray experiments via co-expressed gene groups analysis (CGGA)

DS'06 Proceedings of the 9th international conference on Discovery Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Microarrays are becoming a ubiquitous tool of research in life sciences. However, the working principles of microarray-based methodologies are often misunderstood or apparently ignored by the researchers who actually perform and interpret experiments. This in turn seems to lead to a common over-expectation regarding the explanatory and/or knowledge-generating power of microarray analyses. In this note we intend to explain basic principles of five (5) major groups of analytical techniques used in studies of microarray data and their interpretation: the principal component analysis (PCA), the independent component analysis (ICA), the t-test, the analysis of variance (ANOVA), and self organizing maps (SOM). We discuss answers to selected practical questions related to the analysis of microarray data. We also take a closer look at the experimental setup and the rules, which have to be observed in order to exploit microarrays efficiently. Finally, we discuss in detail the scope and limitations of microarray-based methods. We emphasize the fact that no amount of statistical analysis can compensate for (or replace) a well thought through experimental setup. We conclude that microarrays are indeed useful tools in life sciences but by no means should they be expected to generate complete answers to complex biological questions. We argue that even well posed questions, formulated within a microarray-specific terminology, cannot be completely answered with the use of microarray analyses alone.