Računarski alat za funkcionalnu analizu genetskih mikronizova

Microarray technology allows measuring expression level of thousands genes at once. Such data can in principle be used to identify genes that differentiate various groups of subjects. However, reducing uncertainty when analyzing such a high dimensional sample collected over a small number of subjects requires developing novel statistical techniques and also exploiting prior domain knowledge. In this talk we will present our novel software tool aimed at functional characterization of gene expression profiles. The tool consisting of profiles ranking and clustering modules is used to explore a hypothesis that genes with same or similar function are likely to have similar expression profiles. When analyzing 1,051 Gene Ontology (GO) terms represented by at least two genes in microarray data set of Plasmodium Falciparum (a parasite that causes malaria) Intraerythrocytic Developmental Cycle, we found that gene expression profiles in 550 of them are significantly (P<0.05) correlated. We represented each of the 550 significant GO terms with the functional expression profile defined as average expression profile of all genes annotated with a given GO term. Using Kmeans clustering, we clustered 199 profiles corresponding to GO molecular functions into 4 groups. This was repeated on 228 profiles corresponding to GO biological process. We quantified the clustering quality by introducing a measure of GO term similarity defined as the minimal distance between two GO terms in GO direct acyclic graph. The results based on this measure showed that the obtained clustering is biologically relevant which supports our hypothesis that genes with similar functions have similar expressions. Consistent findings were obtained when applying this fairly simple tool for microarray data analysis of Caccharomyces Cerevisiae, Mus Musculus and Home Sapiens cell cycle.

Results were obtained through a collaboration with Hongbo Xie, Slobodan Vucetic, Hao Sun and Pooja Hedge

Isti predavač će u petak, 2. septembra, održati još jedno predavanje u SANU:
Integration Of Deterministic And Statistical Algorithms For Retrieval And Analysis Of Geophysical Parameters

U petak, 2. septembra, u 14 časova, u Srpskoj akademiji nauka i Umetnosti, Knez Mihajlova 35, soba 2 (prvi sprat)

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