Multiplexed Component Analysis to identify genes contributing to the inflammatory responses during acute SIV infection

ABSTRACT: Expression profiling by NanoString nCounter gene expression system Inflammatory response genes play an important role during acute HIV and SIV infection. Using an SIV macaque model for AIDS and CNS disease, we measured mRNA expression of 92 genes before and at multiple time points during acute SIV infection in three tissues: Peripheral blood mononuclear cells, mesenteric lymph nodes (MLN) and peripheral blood mononuclear cells (PBMC). Studying the overall changes of mRNA expressions over time or analyzing the correlation between the gene measurements and SIV RNA in plasma can result in limited interpretations. This is due to several reasons including but not limited to: 1) lack of prior information on how cells react to changes in gene expressions; and 2) biological responses typically involve many genes working together. To approach this problem, we combine multiple preprocessing methods with two multivariate analysis methods, namely principal component analysis and partial least square regression, to create a multiplexed set of 12 “judges”. Each of the judges simultaneously observes all the variables and emphasizes a unique type of gene expression response that could be significant, for example, depending on whether the cell responds to the absolute or relative size of gene expression changes. By incorporating multiple potential biological models of response, it is possible to identify genes that are consistently ranked as high “contributing” genes in different scenarios, i.e., genes that have a higher weight when we classify the data based on different classification schemes. We then use statistical analysis to verify that the consistently high-ranking genes are also statistically significant. We also investigate whether genes are tissue-specific, identify clusters of genes that co-vary together and study their correlation with regard to other gene clusters. The multiplex component analysis method introduced in this paper is a powerful tool to analyze complex gene datasets, identify significant genes, and generate testable hypotheses.

ORGANISM(S): Macaca nemestrina

PROVIDER: GSE51488 | GEO | 2015/05/22

SECONDARY ACCESSION(S): PRJNA223294

REPOSITORIES: GEO