An Open Source Microarray Data Analysis System with GUI: Quintet

2005 ◽  
pp. 392-401 ◽  
Author(s):  
Jun-kyoung Choe ◽  
Tae-Hoon Chung ◽  
Sunyong Park ◽  
Hwan Gue Cho ◽  
Cheol-Goo Hur
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
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).

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