Development of a knowledge-based multi-scheme cancer microarray data analysis system

2004 ◽  
Author(s):  
J.H. Phan ◽  
C.F. Quo ◽  
Kejiao Guo ◽  
Weimin Feng ◽  
G. Wang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Knowledge Based ◽  
Analysis System ◽  
Data Analysis System

scholarly journals 2HAPI: a microarray data analysis system

2003 ◽  
Vol 19 (11) ◽  
pp. 1443-1445 ◽  
Author(s):  
J. L. Fink ◽  
S. Drewes ◽  
H. Patel ◽  
J. B. Welsh ◽  
D. R. Masys ◽  
...  
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Analysis System ◽  
Data Analysis System

scholarly journals Can Replication Save Noisy Microarray Data?

2002 ◽  
Vol 3 (4) ◽  
pp. 372-374 ◽  
Author(s):  
Lorenz Wernisch
Keyword(s):  
Image Analysis ◽  
Data Analysis ◽  
Microarray Data ◽  
Mixed Models ◽  
Variance Components ◽  
Statistical Data ◽  
Statistical Data Analysis ◽  
Microarray Experiments ◽  
Analysis System ◽  
Data Analysis System

Microarray experiments are multi-step processes. At each step—the growth of cultures, extraction of mRNA, reverse transcription, labelling, hybridization, scanning, and image analysis—variation and error cannot be completely avoided. Estimating the amount of such noise and variation is essential, not only to test for differential expression but also to suggest at which level replication is most effective.Replication and averaging are the key to the estimation as well as the reduction of variability. Here I discuss the use of ANOVA mixed models and of analysis of variance components as a rigorous way to calculate the number of replicates necessary to detect a given target fold-change in expression levels. Procedures are available in the package YASMA (http://www.cryst.bbk.ac.uk/wernisch/yasma.html) for the statistical data analysis system R (http://www.R-project.org).

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

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