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The Miller dataset [ 35] is a well-annotated gene expression dataset profiling 251 primary human breast tumors.
The threshold value was optimized using cross-platform, cross-species gene expression data from a panel of human and mouse tissue samples [ 15] and an independent dataset profiling brain sub-regions in human, mouse, and rat [ 39].
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The 'Golub' cancer dataset profiled the expression of 7129 genes in 47 subjects with acute lymphoblastic leukemia (ALL) and 25 subjects with acute myeloid leukemia (AML) to develop a class predictor [5].
were only in the combined dataset profile.
The combined dataset profile included two known molecular markers of breast cancer: BCL2 and ESR1.
This dataset profiled mRNA expression on days 0, 2, 4, and 10 after birth.
The extensive dataset profiled some 2000 genes, which limits detailed comparisons with the present study within this manuscript [ 33].
While both profiles performed better when they were applied to ER-positive patients only, the combined dataset profile further outperformed the Rosetta profile.
We generated two types of source tree dataset profiles: those containing only scaffolds, and those containing one scaffold and several clade-based datasets (as described earlier).
Respectively 17 and 37 of 51 genes in combined dataset profile had one-sided p values less than 0.01 and 0.1.
The DAVID functional annotation tool [ 24] was applied to the combined dataset profile while all 5,569 Unigene clusters were used as the genomic background.
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