Selected article for: "PCA principal component and principal component"

Author: Zapata, Juan Carlos; Carrion, Ricardo; Patterson, Jean L.; Crasta, Oswald; Zhang, Yan; Mani, Sachin; Jett, Marti; Poonia, Bhawna; Djavani, Mahmoud; White, David M.; Lukashevich, Igor S.; Salvato, Maria S.
Title: Transcriptome Analysis of Human Peripheral Blood Mononuclear Cells Exposed to Lassa Virus and to the Attenuated Mopeia/Lassa Reassortant 29 (ML29), a Vaccine Candidate
  • Document date: 2013_9_12
  • ID: 0epeljaf_18
    Snippet: Images from each hybridization, were inspected manually and percentage of present calls of each array was checked. Cluster [39] and Principal Component Analyses (PCA) [40] against all conditions using genes with normalized maximum value/minimum value .5 were also performed. Raw data from the arrays were normalized at probe level using a robust multichip average of G+C content algorithm (gcRMA) [41] , [42] and then log2 transformed. The detection .....
    Document: Images from each hybridization, were inspected manually and percentage of present calls of each array was checked. Cluster [39] and Principal Component Analyses (PCA) [40] against all conditions using genes with normalized maximum value/minimum value .5 were also performed. Raw data from the arrays were normalized at probe level using a robust multichip average of G+C content algorithm (gcRMA) [41] , [42] and then log2 transformed. The detection calls (Present, Marginal, Absent) for each probeset were obtained using Affymetrix GeneChip Operating Software (GCOS) (http://www.affymetrix.com/browse/products. jsp?productId = 131429&navMode = 34000&navAction = jump& aId = productsNav#1_1). Only genes with at least one present call across all compared hybridizations were selected for further statistical analysis. All data have been submitted under series record GSE41300.

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