| Title: | Parallel Mutual Information Estimation for Gene Network Reconstruction |
|---|---|
| Description: | Parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>). |
| Authors: | Gabriele Sales [aut, cre], Chiara Romualdi [aut] |
| Maintainer: | Gabriele Sales <[email protected]> |
| License: | AGPL-3 |
| Version: | 1.1.1 |
| Built: | 2024-11-21 06:52:31 UTC |
| Source: | CRAN |
A function that implements the ARACNE algorithm for the reconstruction of gene interaction networks (additive model).
aracne.a(mi, eps=0.05)aracne.a(mi, eps=0.05)
mi |
matrix of the mutual information. |
eps |
a positive numeric value used to remove the weakest edge of each triple of nodes. |
This algorithm considers each triple of edges independently and removes the weakest one if
and
By default, the function uses all the available cores. You can
set the actual number of threads used to N by exporting the
environment variable OMP_NUM_THREADS=N.
A square weighted adjacency matrix of the inferred network.
Adam A. Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano. Aracne : An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 2006.
mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- aracne.a(mi, 0.05)mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- aracne.a(mi, 0.05)
A function that implements the ARACNE algorithm for the reconstruction of gene interaction networks (multiplicative model).
aracne.m(mi, tau=0.15)aracne.m(mi, tau=0.15)
mi |
matrix of the mutual information. |
tau |
a positive numeric value used to remove the weakest edge of each triple of nodes. |
This algorithm considers each triple of edges independently and removes the weakest one if
and
By default, the function uses all the available cores. You can
set the actual number of threads used to N by exporting the
environment variable OMP_NUM_THREADS=N.
A square weighted adjacency matrix of the inferred network.
Adam A. Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano. Aracne : An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 2006.
mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- aracne.m(mi, 0.15)mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- aracne.m(mi, 0.15)
A function that infers the interaction network using the CLR algorithm.
clr(mi)clr(mi)
mi |
matrix of the mutual information. |
CLR computes the score
for each pair of variables , where
and and are the mean
and the standard deviation of the mutual information values
for all .
By default, the function uses all the available cores. You can
set the actual number of threads used to N by exporting the
environment variable OMP_NUM_THREADS=N.
A square weighted adjacency matrix of the inferred network.
Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy S. Gardner. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology, 2007.
mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- clr(mi)mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- clr(mi)
A function to perform a parallel estimation of the mutual information
of vectors x and y using entropy estimates from K-nearest neighbor
distances.
knnmi(x, y, k=3, noise=1e-12)knnmi(x, y, k=3, noise=1e-12)
x |
a numeric vector. |
y |
a numeric vector with the same length of |
k |
the number of nearest neighbors to be considered to estimate the
mutual information. Must be less than the number of elements of |
noise |
the magnitude of the random noise added to break ties. |
The function adds a small random noise to the data in order to break ties due to limited numerical precision.
By default, the function uses all available cores. You can
set the actual number of threads used to N by exporting the
environment variable OMP_NUM_THREADS=N.
Kraskov, Alexander and Stogbauer, Harald and Grassberger, Peter. Estimating mutual information. Phys. Rev. E, 2004.
x <- rnorm(100) y <- rnorm(100) knnmi(x, y, 5)x <- rnorm(100) y <- rnorm(100) knnmi(x, y, 5)
A function that computes the mutual information between all pairs
of rows of matrix mat using entropy estimates from K-nearest neighbor distances.
knnmi.all(mat, k=3, noise=1e-12)knnmi.all(mat, k=3, noise=1e-12)
mat |
a numeric matrix (for the reconstruction of gene regulatory networks, genes on rows and samples on columns). |
k |
the number of nearest neighbors to consider to estimate the
mutual information. Must be less than the number of columns of |
noise |
the magnitude of the random noise added to break ties. |
The function adds a small random noise to the data in order to break ties due to limited numerical precision.
By default, the function uses all the available cores. You can
set the actual number of threads used to N by exporting the
environment variable OMP_NUM_THREADS=N.
Kraskov, Alexander and Stogbauer, Harald and Grassberger, Peter. Estimating mutual information. Phys. Rev. E, 2004.
mat <- matrix(rnorm(1000), nrow=10) knnmi.all(mat, 5)mat <- matrix(rnorm(1000), nrow=10) knnmi.all(mat, 5)
A function that estimates the mutual information between all pairs of
rows of matrices mat1 and mat2 using entropy estimates
from K-nearest neighbor distances.
knnmi.cross(mat1, mat2, k=3, noise=1e-12)knnmi.cross(mat1, mat2, k=3, noise=1e-12)
mat1 |
a numeric matrix (for the reconstruction of gene regulatory networks, genes on rows and samples on columns). |
mat2 |
a numeric matrix with the same number of columns as |
k |
the number of nearest neighbors to consider to estimate the
mutual information. Must be less than the number of columns of |
noise |
the magnitude of the random noise added to break ties. |
The function adds a small random noise to the data in order to break ties due to limited numerical precision.
By default, the function uses all the available cores. You can
set the actual number of threads used to N by exporting the
environment variable OMP_NUM_THREADS=N.
Kraskov, Alexander and Stogbauer, Harald and Grassberger, Peter. Estimating mutual information. Phys. Rev. E, 2004.
mat1 <- matrix(rnorm(1000), nrow=10) mat2 <- matrix(rnorm(1000), nrow=10) knnmi.cross(mat1, mat2, 5)mat1 <- matrix(rnorm(1000), nrow=10) mat2 <- matrix(rnorm(1000), nrow=10) knnmi.cross(mat1, mat2, 5)
A function that infers the interaction network using the MRNET algorithm.
mrnet(mi)mrnet(mi)
mi |
matrix of the mutual information. |
The MRNET approach starts by selecting the variable
having the highest mutual information with the target Y.
Then, it repeatedly enlarges the set of selected variables by
taking the that maximizes
for all already in S.
The procedure stops when the score becomes negative.
By default, the function uses all the available cores. You can
set the actual number of threads used to N by exporting the
environment variable OMP_NUM_THREADS=N.
A square weighted adjacency matrix of the inferred network.
H. Peng, F.long and C.Ding. Feature selection based on mutual information: Criteria of max-dependency, max relevance and min redundancy. IEEE transaction on Pattern Analysis and Machine Intelligence, 2005.
mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- mrnet(mi)mat <- matrix(rnorm(1000), nrow=10) mi <- knnmi.all(mat) grn <- mrnet(mi)