| 摘要 |
In various embodiments of the present invention, initial gene-expression data is initially partitioned into classes by patient, subject, or other identifier of a source of samples, expression-level-differences are computed for each gene with respect to each initial partition, and a rank consistency score or fold-change consistency score is computed for each gene from the expression-level difference metrics computed for each initial partition. In other words, rather than partitioning gene-expression-level data directly into two or more classes relative to an event of interest, the gene-expression-level data is first partitioned according to sample source, and then each sample-source partition is partitioned into two or more classes relative to an event of interest. Levels of significance, or p-values, can be straightforwardly computed for both rank consistency scores and fold-change consistency scores.
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