| Type: | Package |
| Title: | Network Reverse Engineering with Approximate Bayesian Computation |
| Version: | 0.8-1 |
| Date: | 2022-10-18 |
| Depends: | R (≥ 3.0.0) |
| Imports: | RColorBrewer, network, sna |
| Suggests: | ggplot2, knitr, markdown, rmarkdown |
| Author: | Frederic Bertrand 
[cre, aut],
Myriam Maumy-Bertrand
[aut],
Khadija Musayeva [ctb],
Nicolas Jung [ctb],
Université de Strasbourg [cph],
CNRS [cph] |
| Maintainer: | Frederic Bertrand <frederic.bertrand@utt.fr> |
| Description: | We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. |
| NeedsCompilation: | yes |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Classification/MSC: | 62E17, 62F15, 62J07, 62P10, 92C42 |
| LazyData: | true |
| VignetteBuilder: | knitr |
| URL: | https://fbertran.github.io/networkABC/, https://github.com/fbertran/networkABC/ |
| BugReports: | https://github.com/fbertran/networkABC/issues/ |
| RoxygenNote: | 7.2.1 |
| Packaged: | 2022-10-18 23:03:24 UTC; fbertran |
| Repository: | CRAN |
| Date/Publication: | 2022-10-19 00:02:35 UTC |
ABC algorithm for network reverse-engineering
Description
ABC algorithm for network reverse-engineering
Usage
abc(
data,
clust_coeffs = c(0.33, 0.66, 1),
tolerance = NA,
number_hubs = NA,
iterations = 10,
number_networks = 1000,
hub_probs = NA,
neighbour_probs = NA,
is_probs = 1
)
Arguments
data |
: Any microarray data in the form of a matrix (rows are genes and columns are time points) |
clust_coeffs |
: one dimensional array of size clust_size of clustering coefficients (these clustering coefficient are tested in the ABc algorithm). |
tolerance |
: a real value based for the tolerance between the generated networks and the reference network |
number_hubs |
: number of hubs in the network |
iterations |
: number of times to repeat ABC algorithm |
number_networks |
: number of generated networks in each iteration of the ABC algorithm |
hub_probs |
: one-dimensional array of size number_genes for the each label to be in the role of a hub |
neighbour_probs |
: this is the matrix of neighbour probabilities of size number_nodes*number_nodes |
is_probs |
: this needs to be set either to one (if you specify hub_probs and neighbour_probs) or to zero (if neither probabilities are specified). Warning: you should specify both hub_probs and neighbour_probs if is_probs is one. If is_prob is zero these arrays should simply indicate an array of a specified size.. |
Examples
M<-matrix(rnorm(30),10,3)
result<-abc(data=M)
Calculate the clustering coefficient
Description
Calculate the clustering coefficient for an adjacency matrix. By default, the local clustering coefficient is calculated. From the PCIT package after it was archived on the CRAN.
Usage
clusteringCoefficient(adj, FUN = "localClusteringCoefficient", ...)
Arguments
adj |
An adjacency matrix. Calculating the clustering coefficient only makes sense if some connections are zero i.e. no connection. |
FUN |
The function for calculating the clustering coefficient. |
... |
Arguments to pass to FUN |
Value
The clustering coefficient(s) for the adjacency matrix.
Author(s)
Nathan S. Watson-Haigh
See Also
Examples
clusteringCoefficient(network_gen(50,.33)$network)
Calculate the local clustering coefficient
Description
Calculate the local clustering coefficient for each node in an adjacency matrix. The clustering coefficient is defined as the proportion of existing connections from the total possible (Watts and Strogatz, 1998).
Usage
localClusteringCoefficient(adj)
Arguments
adj |
An adjacency matrix. Calculating the clustering coefficient only makes sense if some connections are zero i.e. no connection. |
Value
A vector of local clustering coefficients for each node/gene of the adjacency matrix.
Author(s)
Nathan S. Watson-Haigh
References
D.J. Watts and S.H. Strogatz. (1998) Collective dynamics of 'small-world' networks. Nature. 393(6684). 440-442.
See Also
Examples
localClusteringCoefficient(network_gen(50,.33)$network)
Simulated network
Description
Result of the use of the network_gen function.
Usage
netsimul
Format
A list of three objects :
- number_genes
The number of genes in the network
- clust_coef
The clustering coefficient
- network
The simulated network
networkABC
Description
An inference tool based on approximate Bayesian computations to decipher network data and assess the strength of their inferred links.
References
networkABC: An inference tool for networks based on approximate Bayesian computation, Myriam Maumy-Bertrand, Frédéric Bertrand, preprint.
Random scale-free network generation. This function is used intensively in the abc function.
Description
Generate random network topology
Usage
network_gen(number_genes, clust_coef)
Arguments
number_genes |
A number |
clust_coef |
A number |
Value
A list with the number of of genes, the targeted clustering coefficient and the resulting network
Examples
network_gen(10,1)
Result of an ABC inference
Description
Result for the reverse engineering of a simulated Cascade network
Usage
resabc
Format
A list of 14 objects :
- data
: The microarray data used, rows are genes and columns are time points.)
- ngenes
: The number of genes.)
- ntimes
: The number of timepoints)
- clust_size
: the size of clusters
- clust_coeffs
: the clustering coefficient
- tolerance
: the tolerance between the generated networks and the reference network
- number_hubs
: number of hubs in the network
- iterations
: number of times to repeat ABC algorithm
- number_networks
: number of generated networks in each iteration of the ABC algorithm
- hub_probs
: one-dimensional array of size number_genes for the each label to be in the role of a hub
- neighbour_probs
: matrix of neighbour probabilities of size number_nodes*number_nodes
- is_probs
: is equal to 1 since hub_probs and neighbour_probs were specified
Plot for the hub probabilities
Description
Plot for the hub probabilities ; there is one probability for each node in the network.
Usage
showHp(result)
Arguments
result |
: The result of the abc algorithm. |
Examples
data(resabc)
showHp(resabc)
Plot the final network.
Description
Plot the final network.
Usage
showNetwork(res, min_prob)
Arguments
res |
: The result of the abc algorithm. |
min_prob |
: numeric ; under this probabilitie value, the link between two genes is set to 0. |
Examples
data(resabc)
showNetwork(resabc,.2)
Plot for the neighbourhood probabilities
Description
Plot for the neighbourhood probabilities ; there is one probability for each pair of node in the network.
Usage
showNp(result)
Arguments
result |
: The result of the abc algorithm. |
Examples
data(resabc)
showNp(resabc)