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For each merging step, we first fit 5 different monotonic functions to the pairs of data sets.
Interestingly, these pairs of data sets (GSE4335 and Vijver or GSE4335 and GSE1992) were generated by different gene expression profiling technologies, indicating that platform-specific biases were not a major obstacle to data merging.
To formulate the hypothesis of the experiment we define convergent validity as the correlation shown by a pair of data sets obtained with different elicitation methods.
The results provided no reason against the initial hypothesis arguing that the distribution of statistical differences for each measured pair of data sets is statistically different from the average in each of the studied groups.
For each pair of data sets, the number of common genes with expression data after filtering is shown.
We outlined a correlation matrix by computing the Pearson correlation coefficient (r) for every pair of data sets.
In addition, no more than two bulls out of one half sib family were allowed to be in validation in order to reduce the dependency between validation bulls in each pair of data sets.
For a given pair of data sets, this angle can be determined by subtracting the angle of the best-fit line from 45 degrees.
However, there is a limit to how much information can be gleaned from just one pair of data sets, a point acknowledged by Feinberg.
We first validated our method by correctly recovering expected associations of subclasses using a pair of data sets comprised of multiple tissue types (Example 1).
The following pairs of data sets were compared using correlation analysis: exam grades versus percent clicker participation for each semester (Spring 2009 and Fall 2009), exam grades versus percent correct clicker responses for each semester (Spring 2009 and Fall 2009), and exam grades versus percent attendance for each semester (Spring 2008/Fall 2008 and Spring 2009/Fall 2009).
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