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LIFE01200604005 Shri Somnath Ghosh - Homi Bhabha National ...

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CHAPTER 2<br />

MATERIALS AND METHODS<br />

Array G2500A scanner (Agilent) and the expression data on human Affymetrix chip was<br />

generated. Since the numbers of samples in each treatment type were sparse, we used a bivariate<br />

simulation approach to identify differentially expressed (DE) genes for the specified threshold<br />

fold change (FC). The DE candidates across each comparison were further evaluated for their<br />

biological relevance through Gene Ontology and Pathways studies. The statistical analysis was<br />

carried out using R package and the biological analysis was carried out using Genowiz TM<br />

software.<br />

For comparative evaluation, an un-irradiated control cell was used. The expression data on<br />

Affymetrix Human 133AB chip was obtained for each sample. The primary objective of the<br />

study was to obtain differentially expressed (DE) probe sets (genes) across various comparisons.<br />

Also, the interest was to study the clustering of genes and samples and the functional relevance<br />

of the differentially expressed genes.<br />

Upon identifying the DE genes, the next interest was to study the biological relevance of selected<br />

marker genes through GO and Pathway analysis.<br />

The data analysis process involves Robust Multichip Average (RMA) normalization, quality<br />

check analysis, differential expression analysis and the gene enrichment analysis.<br />

The correlation between the expression values for samples was studied through Pearson's<br />

coefficient. A pairwise correlation analysis was performed to generate a correlation matrix<br />

indicating how the samples are related with each other. The coefficient tells about the similarity<br />

of expression trend between the two arrays. A coefficient value close to 1.0 indicates the linear<br />

85

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