Resolution:
standard / ## Figure 6.
(Both) Statistics of the sign-inference process on the regulatory network of The signed interaction graph is used to generate sets of E. coli from complete expression profiles.N random artificial expression profiles which cover the whole network. Then, each set of N profiles is used with the unsigned interaction graph to recover regulatory roles.
X-axis: number N of expression profiles in the dataset. Y-axis: percentage of recovered signs in the
unsigned interaction graph. Each set of N random profiles was generated 100 times; the distribution of the recovered signs is
plotted as a boxplot. The continuous line corresponds to the theoretical formula Y = M_{1 }+ M_{2}(1 - (1 - p)^{X}); M_{1 }denotes the number of single incoming regulations inferred with probability one from
any complete profile (using the naive inference algorithm), and M_{2 }denotes the number of signs inferred with a probability p (0 <p < 1) per experiment. (Left) Statistics using the whole E. coli regulatory network. We estimated that at most 37.3% of the network can be inferred
from a small number of different complete profiles. Among the inferred regulations,
we estimated to M_{1 }= 609 the number of signs inferred with probability one from any complete expression
profile. The remaining M_{2 }= 811 signs are inferred with a probability whose average is p = 0.049 per experiment. Hence, 30 perturbation experiments are enough to infer 33%
of the network. (Right) Statistics using only the core of the former graph (see definition of a core in the text). We estimated M_{1 }= 18 and M_{2 }= 9, implying that the maximum rate of inference is 47.4%. Since p = 0.0011, the number of expression profiles required to obtain a given percentage
of inference is greater than in the case using the whole network (N = 100 to infer 33% of the network).
Veber |