Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0026905
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dc.titleInvestigation of variation in gene expression profiling of human blood by extended principle component analysis
dc.contributor.authorXu Q.
dc.contributor.authorNi S.
dc.contributor.authorWu F.
dc.contributor.authorLiu F.
dc.contributor.authorYe X.
dc.contributor.authorMougin B.
dc.contributor.authorMeng X.
dc.contributor.authorDu X.
dc.date.accessioned2020-03-18T05:48:46Z
dc.date.available2020-03-18T05:48:46Z
dc.date.issued2011
dc.identifier.citationXu 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
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/165586
dc.description.abstractBackground: 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.
dc.publisherPublic Library of Science
dc.sourceUnpaywall 20200320
dc.subjectDNA
dc.subjectRNA
dc.subjecttranscriptome
dc.subjectplasma protein
dc.subjecttranscriptome
dc.subjectaccuracy
dc.subjectadult
dc.subjectarticle
dc.subjectblood sampling
dc.subjectbody mass
dc.subjectcell type
dc.subjectconfounding variable
dc.subjectcontrolled study
dc.subjectDNA microarray
dc.subjecterythrocyte
dc.subjectfemale
dc.subjectgender
dc.subjectgene expression
dc.subjectgene expression profiling
dc.subjectgenetic variability
dc.subjecthuman
dc.subjecthuman cell
dc.subjecthuman genome
dc.subjectleukocyte
dc.subjectmale
dc.subjectprincipal component analysis
dc.subjectreproducibility
dc.subjectRNA isolation
dc.subjectblood cell
dc.subjectgenetic variability
dc.subjectgenetics
dc.subjectmetabolism
dc.subjectmethodology
dc.subjectstandard
dc.subjectBlood Cells
dc.subjectBlood Proteins
dc.subjectGene Expression Profiling
dc.subjectGenetic Variation
dc.subjectHumans
dc.subjectPrincipal Component Analysis
dc.subjectResearch Design
dc.subjectTranscriptome
dc.typeArticle
dc.contributor.departmentBIOINFORMATICS CENTRE
dc.description.doi10.1371/journal.pone.0026905
dc.description.sourcetitlePLoS ONE
dc.description.volume6
dc.description.issue10
dc.description.pagee26905
dc.published.statePublished
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