Micro-array data analysis is the complicated procedure involving preprocessing, Statistical analysis and finally giving biological meaning to the observed changes. R programming makes Micro-array data analysis much simpler and analyst should know which point he has to stop. In R, all the steps of Micro-array data analysis can be completed starting from preprocessing, hierarchical clustering, Heat maps, Statistical analysis and mapping to GO ontologies or KEGG pathways etc. Data generated from different platform such as Affymetrix, Agilent, and Illumina can be analyzed in R. The important libraries required for the Micro-array data analysis are affy, limma, simpleaffy, amap, KEGG, geneListPie etc.
The following are screen shots of the analysis of Micro-array data analysis of high-throughput data Affymetrix platform
Chip QC
RNA degradation plot
Intensity Plot
HeatMap
KEGG Pathway mapping
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