Abstract

The use of microarrays to measure the expression of large numbers of genes simultaneously is increasing in agriculture research. Statisticians are expected to help biologists analyze these large data sets to identify biologically important genes that are differentially regulated in the samples under investigation. However, molecular biologists are often unfamiliar with the statistical methods used to analyze microarrays. Presented here are methods developed to graphically represent microarray data and various types of errors commonly associated with microarrays to help visualize sources of error. Two case studies were used. In case study one, genes differentially regulated when two corn lines, one resistant and one sensitive, were treated with Aspergillus flavus isolate NRRL 3357 or left untreated were investigated. Analyses and images showing 3 types of variation are shown. Genes were ranked according to fold change and re-ranked after adjusting for potential sources of error. In case two, cotton genes differentially regulated in 1-day-old fiber compared to whole ovules or older fibers were investigated. Data and sources of error were imaged as described for case one and genes with significant changes in gene expression were identified.

Creative Commons License


This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS
 
Apr 30th, 9:00 AM

A VISUAL AID FOR STATISTICIANS AND MOLECULAR BIOLOGISTS WORKING WITH MICROARRAY EXPERIMENTS

The use of microarrays to measure the expression of large numbers of genes simultaneously is increasing in agriculture research. Statisticians are expected to help biologists analyze these large data sets to identify biologically important genes that are differentially regulated in the samples under investigation. However, molecular biologists are often unfamiliar with the statistical methods used to analyze microarrays. Presented here are methods developed to graphically represent microarray data and various types of errors commonly associated with microarrays to help visualize sources of error. Two case studies were used. In case study one, genes differentially regulated when two corn lines, one resistant and one sensitive, were treated with Aspergillus flavus isolate NRRL 3357 or left untreated were investigated. Analyses and images showing 3 types of variation are shown. Genes were ranked according to fold change and re-ranked after adjusting for potential sources of error. In case two, cotton genes differentially regulated in 1-day-old fiber compared to whole ovules or older fibers were investigated. Data and sources of error were imaged as described for case one and genes with significant changes in gene expression were identified.