Differential expression of the unique proteins within each experiment was assessed

Catharanthine sulfate proteins that only were identified in one of the three Dexlansoprazole experiments were excluded from further analysis. Differential expression of the unique proteins within each experiment was assessed by natural logarithm transformation of the ratios. For cluster analysis and for overview distribution charts, the level in a tissue type was divided by the average level across the five tissue types. For the estimation of differential protein levels, ratios from pairwise tissue comparisons were used, as described below. Two groups of differential level proteins were established based on the pairwise tissue comparisons: Group A: Proteins that were identified in all three experiments and showed two-tailed student��s t-test p values,0.05 and a mean fold change.26the standard error of the mean based on a global standard error that was calculated from all quantified proteins within a pairwise comparison; this combination of t-test and fold change criteria was applied to isolate the stronger differential level estimates and has been used in our previous work ; Group B: Proteins that were identified in two or three experiments with larger variations in fold changes but with every single fold change greater than 26the SEM. Only proteins that did not meet the criteria for group A were tested against the criteria for group B. Analyzing the protein level differences in the comparisons that included cholesteatoma, the observed minimum fold changes of proteins meeting the criteria ranged from 2.7�C7.9. The assumption of normality of the logarithmically-transformed ratios was checked using qq-plots of the residuals. In order to calculate ratios in cases of intensity values below the lower limit of detection, zero-intensity values were exchanged with a value of three times the lowest measured intensity value of the dataset. The identification and relative quantification of more than 2,400 unique proteins and the inclusion of five tissue types allowed for a broad bioinformatics analysis, combining groups of related proteins with differential levels into strong joint estimates.

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