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Advisor(s)
Abstract(s)
Microarray allow to monitoring simultaneously
thousands of genes, where the abundance of the transcripts under a same experimental condition at the same time can be quantified. Among various available array technologies, double channel cDNA microarray experiments have arisen in numerous technical protocols associated to genomic studies, which is the focus of this work. Microarray experiments involve many steps and each one can affect the quality of raw data. Background correction and normalization are preprocessing techniques to clean and correct the raw data when undesirable fluctuations arise from technical factors. Several recent studies showed that there is no preprocessing strategy that outperforms others in all circumstances and thus it seems difficult to provide general recommendations. In this work, it is proposed to use exploratory techniques to visualize the effects of preprocessing methods on statistical analysis of cancer two-channel microarray data sets, where the cancer types (classes) are known. For selecting differential expressed genes the arrow plot was used and the graph of profiles resultant from the correspondence analysis for visualizing the results. It was used 6 background methods and 6 normalization methods, performing 36 pre-processing methods and it was analyzed
in a published cDNA microarray database (Liver) available at http://genome-www5.stanford.edu/ which microarrays were already classified by cancer type. All statistical analyses were performed using the R statistical software.
Description
Keywords
Statistics Microarray Normalization Background Arrow plot CA maps
Citation
Silva C, Freitas A, Roque S, Sousa L. Arrow plot and CA maps on microarray preprocessing methods. In 14th International Conference on Computational Sciences and Applications (ICCSA 2014), Guimarães, 30th June to 3rd July 2014. IEEE; 2014. p. 229-32.