Using ANOVA to Analyze Microarray Data

Using ANOVA to analyze microarray data.

ANOVA provides a general approach to the analysis of single and multiple factor experiments on both one- and two-color microarray platforms. Mixed model ANOVA is important because in many microarray experiments there are multiple sources of variation that must be taken into consideration when constructing tests for differential expression of a gene. The genome is large, and the signals of expre...

Interpretation of ANOVA models for microarray data using PCA

MOTIVATION ANOVA is a technique, which is frequently used in the analysis of microarray data, e.g. to assess the significance of treatment effects, and to select interesting genes based on P-values. However, it does not give information about what exactly is causing the effect. Our purpose is to improve the interpretation of the results from ANOVA on large microarray datasets, by applying PCA o...

Using a Support Vector Machine to Analyze a DNA Microarray∗

A DNA microarray is a small silicon chip that is covered with thousands of spots of DNA of known sequence. Biologists use microarrays to study gene expression patterns in a wide range of medical and scientific applications. Analysis of microarrays has led to discovery of genomic patterns that distinguish cancerous from non-cancerous cells. Other researchers have used microarrays to identify the...

cDNA Microarray Data Normalization

Using SAS to Analyze the Summary Data

Most of the time, we have the raw data to conduct the necessary statistical analysis in SAS®. However, when there is only summary data available, some additional SAS coding is necessary in order to perform the hypothesis test. SAS procedures, as well as simplified macros (%SUM_TTEST, %SUM_ANOVA, %P_ANOVA, %SUM_CHI) with examples, will be discussed in this paper for chi-square test, two sample t...