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## Figure 9.
Average number of motifs and matches at 15% FDR recovered by the continuous and discrete
hypergeometric-distribution scoring functions. The same optimization and filtering procedure was applied on both versions (seed
FDR < 0.001, α = 0.75, γ = 0.75). (Left) Average results for the 24 yeast datasets.
The leftmost column shows the results obtained by the continuous version of the scoring
function implemented in RED^{2}. The 'k = i' columns show the results obtained by the discrete version and i clusters. The 'best k' column shows the results obtained when the number of clusters that yields the highest
number of motifs is selected a posteriori for each dataset. (Right) Comparison of the number of predicted motifs that match
a known motif in the ScerTF database for the 24 yeast datasets at 15% FDR. The y-axis
corresponds to the number achieved by RED^{2 }and the x-axis to the number achieved by the discrete version and the best k. Superimposed points are indicated by shading. RED^{2 }found more motifs than the best clustering for 17 datasets and fewer for three datasets,
which gives a sign test P value of 0.003.
Lajoie |