Selected article for: "biological sample and standard deviation"

Author: Tchitchek, Nicolas; Eisfeld, Amie J; Tisoncik-Go, Jennifer; Josset, Laurence; Gralinski, Lisa E; Bécavin, Christophe; Tilton, Susan C; Webb-Robertson, Bobbie-Jo; Ferris, Martin T; Totura, Allison L; Li, Chengjun; Neumann, Gabriele; Metz, Thomas O; Smith, Richard D; Waters, Katrina M; Baric, Ralph; Kawaoka, Yoshihiro; Katze, Michael G
Title: Specific mutations in H5N1 mainly impact the magnitude and velocity of the host response in mice
  • Document date: 2013_7_29
  • ID: 1qc72ovc_71
    Snippet: Quality control processing was performed to identify and remove contaminant proteins, redundant peptides, peptides with an insufficient amount of data across the set of samples [62] , and LC-MS datasets that showed significant deviation from the standard behavior of all LC-MS analyses [63] . The peptide abundance values were normalized across the technical replicates using a global median centering of the data. [64] . The normalized log10 abundan.....
    Document: Quality control processing was performed to identify and remove contaminant proteins, redundant peptides, peptides with an insufficient amount of data across the set of samples [62] , and LC-MS datasets that showed significant deviation from the standard behavior of all LC-MS analyses [63] . The peptide abundance values were normalized across the technical replicates using a global median centering of the data. [64] . The normalized log10 abundance values were averaged across the technical replicates within each biological sample. Peptide level significance patterns were used for protein roll-up to select appropriate peptides for protein quantification. Proteins were quantified using a standard R-Rollup method using the most abundant reference peptide [65] , after filtering the peptides that were redundant, had low data content, or were outside the dominant significance pattern.

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