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|Title:||Investigation of variation in gene expression profiling of human blood by extended principle component analysis||Authors:||Xu Q.
gene expression profiling
principal component analysis
Gene Expression Profiling
Principal Component Analysis
|Issue Date:||2011||Publisher:||Public Library of Science||Citation:||Xu Q., Ni S., Wu F., Liu F., Ye X., Mougin B., Meng X., Du X. (2011). Investigation of variation in gene expression profiling of human blood by extended principle component analysis. PLoS ONE 6 (10) : e26905. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0026905||Abstract:||Background: Human peripheral blood is a promising material for biomedical research. However, various kinds of biological and technological factors result in a large degree of variation in blood gene expression profiles. Methodology/Principal Findings: Human peripheral blood samples were drawn from healthy volunteers and analysed using the Human Genome U133Plus2 Microarray. We applied a novel approach using the Principle Component Analysis and Eigen-R 2 methods to dissect the overall variation of blood gene expression profiles with respect to the interested biological and technological factors. The results indicated that the predominating sources of the variation could be traced to the individual heterogeneity of the relative proportions of different blood cell types (leukocyte subsets and erythrocytes). The physiological factors like age, gender and BMI were demonstrated to be associated with 5.3% to 9.2% of the total variation in the blood gene expression profiles. We investigated the gene expression profiles of samples from the same donors but with different levels of RNA quality. Although the proportion of variation associated to the RNA Integrity Number was mild (2.1%), the significant impact of RNA quality on the expression of individual genes was observed. Conclusions: By characterizing the major sources of variation in blood gene expression profiles, such variability can be minimized by modifications to study designs. Increasing sample size, balancing confounding factors between study groups, using rigorous selection criteria for sample quality, and well controlled experimental processes will significantly improve the accuracy and reproducibility of blood transcriptome study. © 2011 Xu et al.||Source Title:||PLoS ONE||URI:||https://scholarbank.nus.edu.sg/handle/10635/165586||ISSN:||19326203||DOI:||10.1371/journal.pone.0026905|
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