Figure 2.

Reproducible power of pathway inference methods. The reproducibility power of a pathway inference method in an inference-validation pair datasets is measured by <a onClick="popup('http://www.biomedcentral.com/1471-2105/13/12/mathml/M1','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2105/13/12/mathml/M1">View MathML</a>, where <a onClick="popup('http://www.biomedcentral.com/1471-2105/13/12/mathml/M2','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2105/13/12/mathml/M2">View MathML</a> is the ith PA in descending order in the inference dataset, <a onClick="popup('http://www.biomedcentral.com/1471-2105/13/12/mathml/M3','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2105/13/12/mathml/M3">View MathML</a> is its corresponding PA in the validation dataset, and N is the number of selected inferred pathways. The overall reproducibility is then defined as the average Cscore of selected top inferred pathway activities over all six inference-validation pairs. We compared CMI with five inference methods, including the CORG, mean, median, first component score of PCA, as well as no-inferring gene method. Comparing by different ranges of top inferred activities, the CMI showed significant better overall reproducibility over other methods.

Yang et al. BMC Bioinformatics 2012 13:12   doi:10.1186/1471-2105-13-12
Download authors' original image