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This simplifies the differences between samples into principal components on which differences between samples can be more easily viewed.
The differences between samples can be explained by the Au nanoparticle size, which has been proved to be a crucial factor in the preparation of Au catalysts.
Finally, even when dozens of barcoded individual cDNA libraries are sequenced in a single lane, the vast majority of transcripts in each sample can be detected and numerous genes differentially expressed between samples can be identified.
A close relationship between samples can be seen.
By a quick inspection to Fig. 6, differences in the grey-scale brightness between samples can be seen.
However, (beta ) strongly depends on the magnitude, distribution and dimensionality of the data and on the location of clusters, so a general indication cannot be given, although empirical methods like analysing the histogram of pairwise distances between samples can be attempted.
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One example is to construct a network of samples from microarray data, where the nodes are samples and the similarity between two samples can be measured by the Pearson correlation coefficient between their gene expression profiles.
Quantitative treatment of these data, combined with data from analysis of other samples, demonstrating the limits within which differences between discrete samples can be considered significant are discussed further below.
Although there are some problems with using this ordering method (such as variations in efficiency of peptide ionization, variation in peptide resistance to proteolysis or poor peptide chromatograph) it has been demonstrated that abundance changes between related samples can be analyzed by this method[22].
In the HCA, not only the relationships between different samples can be classified, but also the genes with similar expression patterns can be grouped by visual inspection of the hierarchical cluster results.
The probability of gene A expressed equally between two samples can be calculated with: P (y | x ) = (N 2 N 1 ) y (x + y ) ! x ! y !
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