3.4.9. Multi-plex iTRAQ quality control by correlation plots and dendrograms Quality control of the multi-plex iTRAQ protocol was performed by constructing correlation (or scatter) plots, and dendrograms (cluster analysis) to determine how closely the biological replicates matched, and how the different treatments clustered relative to each other. The correlation plots gave pair-wise comparisons between the biological replicates by plotting log2 fold-changes for each dataset against another. Ideally these should follow a straight line with a gradient of 1 but in reality there is always an element of correlation coefficient is frequently used to scatter. Pearson’s measure the strength of linear correlation between two sets of data when data is parametric, i.e. both measurements are continuous and normally distributed, and this was the method adopted here. This gives a value between +1 and -1, where +1 is maximum positive correlation (dependence) and -1 is maximum independence. Correlations were performed between each pair of data (117:118 and 119:121), each having a total of 331 rows. A second correlation was performed between all four replicates in a two-dimensional array, calculated by comparing each replicate against the others, giving a total of 1986 rows, as follows: P117:P118 P117:P119 P117:P121 P118:P119 P118:P121 P119:P121 The dendrograms showed how the reporter ions, and therefore the peptides, from the treated and untreated biological replicates clustered. High correlation between replicates gives maximum overlap indicating high reproducibility. Ideally there should
3.4.9. Multi-plex iTRAQ quality control by correlation plots and dendrograms Quality control of the multi-plex iTRAQ protocol was performed by constructing correlation (or scatter) plots, and dendrograms (cluster analysis) to determine how closely the biological replicates matched, and how the different treatments clustered relative to each other. The correlation plots gave pair-wise comparisons between the biological replicates by plotting log2 fold-changes for each dataset against another. Ideally these should follow a straight line with a gradient of 1 but in reality there is always an element of scatter. Pearson’s correlation coefficient is frequently used to measure the strength of linear correlation between two sets of data when data is parametric, i.e. both measurements are continuous and normally distributed, and this was the method adopted here. This gives a value between +1 and -1, where +1 is maximum positive correlation (dependence) and -1 is maximum independence. Correlations were performed between each pair of data (117:118 and 119:121), each having a total of 331 rows. A second correlation was performed between all four replicates in a two-dimensional array, calculated by comparing each replicate against the others, giving a total of 1986 rows, as follows: P117:P118 P117:P119 P117:P121 P118:P119 P118:P121 P119:P121 The dendrograms showed how the reporter ions, and therefore the peptides, from the treated and untreated biological replicates clustered. High correlation between replicates gives maximum overlap indicating high reproducibility. Ideally there should
PhD_Thesis_%28KilnerJul13%29.docx