Analysis of the impact of the method of cell selection on the gene expression profile of human CD4 and CD8 T cells
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE14926 | GEO | 2009/05/20
SECONDARY ACCESSION(S): PRJNA111917
REPOSITORIES: GEO
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Project description:Negative immunomagnetic selection has become the method of choice for isolating T cell subsets for functional studies due to concerns that directly binding antibody to the surface of a cell, as occurs with positive selection, results in cross-linking of surface antigens, altered gene transcription and subsequent cellular activation. However there is little data to support this. We therefore examined the impact of the method of immunomagnetic cell selection on the gene expression profile of healthy human CD4 and CD8 T cells in a total of 21 cases. Experiment Overall Design: This study was composed of 4 groups of samples: CD4 Negative Selection (n=5), CD4 Positive Selection (n=5), CD8 Negative Selection (n=5), CD8 Positive Selection (n=6). Two samples (PS6 CD8 and PS9 CD4) were removed from the analysis due to discrepant normalised unscaled standard errors
2009-06-01 | E-GEOD-14926 | biostudies-arrayexpress
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Project description:There are many toxic chemicals to contaminate the world and cause harm to human and other organisms. How to quickly discriminate these compounds and characterize their potential molecular mechanism and toxicity is essential. High through put transcriptomics profiles such as microarray have been proven useful to identify biomarkers for different classification and toxicity prediction purposes. Here we aim to investigate how to use microarray to predict chemical contaminants and their possible mechanisms. In this study, we divided 105 compounds plus vehicle control into 14 compound classes. On the basis of gene expression profiles of in vitro primary cultured hepatocytes, we comprehensively compared various normalization, feature selection and classification algorithms for the classification of these 14 class compounds. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine methods LibSVM and SMO had better classification performance. When feature sizes were smaller, LibSVM outperformed other classification methods. Simple logistic algorithm also performed well. At the training stage, usually the feature selection method SVM-RFE performed the best, and PCA was the poorest feature selection algorithm. But overall, SVM-RFE had the highest overfitting rate when an independent dataset used for a prediction in this case. Therefore, we developed a new feature selection algorithm called gradient method which had a pretty high training classification as well as prediction accuracy with the lowest over-fitting rate. Through the analysis of biomarkers that distinguished 14 class compounds, we found a goup of genes that mainly invovled in cell cylce were significanly downregulated by the metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. For in vitro experiment, primary cultured rat hepatocytes were treated one of 105 compounds with relative controls. At least three biological replicates were used for each unique condition. In total 531 arrays were used.
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