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Sparse Principal Component Analysis (SPCA) of Wheat Microarray Data Identifies Co-Expressed Genes Differentially Regulated by Cold Acclimation

DNA microarray technology is a powerful tool for high-throughput analysis that has been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The high dimensionality of microarray data, the expression of thousands of genes in a much smaller number of samples, presents challenges that affect the validity of analytical results. A main issue in microarray transcription profiling is data mining and analysis. Statistical methods are vital for these scientific endeavors. We utilized data obtained from a transcriptome analysis of cold acclimation effects on two winter wheat mutant lines varying in freeze survival. The line with 75 % survival was designated freeze resistant (FR) and the line with 30 % survival was designated freeze susceptive (FS). After preprocessing of the microarray data, we compared the results obtained with and without Sparse Principal Component Analysis (SPCA) on the annotated gene sets. From a starting dataset of 61,115 genes, the significantly differentially expressed genes (DEGs) obtained without using SPCA were 15 in FR, 246 in both (FR and FS), and 36 in FS. However, the significantly DEGs identified with SPCA were 14 in FR, 226 in both (FR and FS), and 36 in FS. Given the small dataset, SPCA was still able to reduce the starting annotated dataset from 1321 to only 1211.