dream4: DREAM 4 (Stolovitsky et al. 2007) 'gold standard' reference...
In networkBMA: Regression-based network inference using Bayesian Model Averaging

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Contains six objects: dream4gold10, dream4gold100, dream4ts10, dream4ts100, dream4wild10, dream4wild100, each in the form of a list of matrices, in which each element corresponds to one of 5 different datasets.
dream4gold10 and dream4gold100 are lists of gold standard network specifications for the 10-gene and 100-gene simulated networks. The list elements are three-column data frames, in which the first and second column represent all possible regulator-gene pairs in the network, excluding self edges. The third column is a boolean variable indicating whether or not the genes in the first two columns are a regulatory pair and thus consitute an edge in the network.
dream4ts10 and dream4ts100 are lists of time course datasets showing how the simulated network responds to a perturbation and how it relaxes upon removal of the perturbation. For both size 10 and size 100, 5 different datasets are available. For networks of size 10, each of the 5 datasets consists of 5 different time series replicates, and for networks of size 100, each of the 5 datasets consists of 10 time series replicates. Each time series has 21 time points. In both cases, there are 5 list components corresponding to the datasets, each of which is a data frame in which the first two columns give the replicate and time, and the remaining columns give the measured expression levels.
The initial condition always corresponds to a steady-state measurement of the wild-type. At t=0, a perturbation is applied to the network as described below. The first half of the time series (until t=500) shows the response of the network to the perturbation. At t=500, the perturbation is removed (the wild-type network is restored). The second half of the time series (until t=1000) shows how the gene expression levels go back from the perturbed to the wild-type state.
dream4wild10 and dream4wild100 are lists giving the steady state (wild-type) expression levels for the genes in each dataset.

1	data(dream4)

The perturbations applied here only affect about a third of all genes, but basal activation of these genes can be strongly increased or decreased. For example, these experiments could correspond to physical or chemical perturbations applied to the cells, which would cause (via regulatory mechanisms not explicitly modeled here) some genes to have an increased or decreased basal activation. The genes that are directly targeted by the perturbation may then cause a change in the expression level of their downstream target genes.
The time series datasets included in this package are a subset of the DREAM 4 data available for the reference networks.

Stolovitzky G, Monroe D and Califano A. (2007), Dialogue on Reverse-Engineering Assessment and Methods. Annals of the New York Academy of Sciences,1115(1),1–22.

Stolovitzky G, Califano A, Prill RJ and Saez Rodriguez J.(2009), DREAM: Dialogue for Reverse Engineering Assessments and Methods, http://wiki.c2b2.columbia.edu/dream/

Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, and Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. PNAS, 107(14):6286-6291, 2010.

Marbach D, Schaffter T, Mattiussi C, and Floreano D. Generating Realistic in silico Gene Networks for Performance Assessment of Reverse Engineering Methods. Journal of Computational Biology, 16(2) pp. 229-239, 2009.

Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, Xue X, Clarke ND, Altan-Bonnet G, and Stolovitzky G. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE, 5(2):e9202, 2010.

networkBMA

networkBMA documentation built on Jan. 28, 2021, 2:02 a.m.