Diboun, I.; (2010) Bioinformatics protocols for analysis of functional genomics data applied to neuropathy microarray datasets. Doctoral thesis , UCL (University College London).

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Abstract

Microarray technology allows the simultaneous measurement of the abundance of thousands of transcripts in living cells. The high-throughput nature of microarray technology means that automatic analytical procedures are required to handle the sheer amount of data, typically generated in a single microarray experiment. Along these lines, this work presents a contribution to the automatic analysis of microarray data by attempting to construct protocols for the validation of publicly available methods for microarray. At the experimental level, an evaluation of amplification of RNA targets prior to hybridisation with the physical array was undertaken. This had the important consequence of revealing the extent to which the significance of intensity ratios between varying biological conditions may be compromised following amplification as well as identifying the underlying cause of this effect. On the basis of these findings, recommendations regarding the usability of RNA amplification protocols with microarray screening were drawn in the context of varying microarray experimental conditions. On the data analysis side, this work has had the important outcome of developing an automatic framework for the validation of functional analysis methods for microarray. This is based on using a GO semantic similarity scoring metric to assess the similarity between functional terms found enriched by functional analysis of a model dataset and those anticipated from prior knowledge of the biological phenomenon under study. Using such validation system, this work has shown, for the first time, that ‘Catmap’, an early functional analysis method performs better than the more recent and most popular methods of its kind. Crucially, the effectiveness of this validation system implies that such system may be reliably adopted for validation of newly developed functional analysis methods for microarray.

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