| Title: | ARACNe-inferred gene networks from TCGA tumor datasets |
|---|---|
| Description: | This package contains ARACNe-inferred networks from TCGA tumor datasets. It also contains a function to export them into plain-text format. |
| Authors: | Federico M. Giorgi |
| Maintainer: | Federico M. Giorgi <[email protected]>, Mariano Alvarez <[email protected]> |
| License: | file LICENSE |
| Version: | 1.38.0 |
| Built: | 2026-10-10 11:14:05 UTC |
| Source: | https://github.com/bioc/aracne.networks |
This package contains ARACNe-inferred networks from TCGA tumor datasets and functions to import new ones and export them into text form.
| Package: | aracne.networks |
| Type: | Package |
| License: | LGPL-3 |
| LazyLoad: | yes |
Federico M. Giorgi
Maintainer: Federico M. Giorgi <[email protected]>
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics 2016 Alvarez, M.J. et al. (2016) Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature Genetics 2016
### Create a random regulon with two hubs # The first hub will have 100 targets # The second hub will have 67 targets regulon<-list() regulon[["hub1"]]<-list( tfmode=setNames(runif(100,-1,1),paste0("target",sample(1:1000,100))), likelihood=runif(100,0,1) ) regulon[["hub2"]]<-list( tfmode=setNames(runif(67,-1,1),paste0("target",sample(1:1000,67))), likelihood=runif(67,0,1) ) class(regulon)<-"regulon" write.regulon(regulon,file="network.txt") ### Print a the Prostate Adenocarcinoma (prad) network to standard output # The gene ids are in Entrez format data(regulonprad) write.regulon(regulonblca,file="",n=10)### Create a random regulon with two hubs # The first hub will have 100 targets # The second hub will have 67 targets regulon<-list() regulon[["hub1"]]<-list( tfmode=setNames(runif(100,-1,1),paste0("target",sample(1:1000,100))), likelihood=runif(100,0,1) ) regulon[["hub2"]]<-list( tfmode=setNames(runif(67,-1,1),paste0("target",sample(1:1000,67))), likelihood=runif(67,0,1) ) class(regulon)<-"regulon" write.regulon(regulon,file="network.txt") ### Print a the Prostate Adenocarcinoma (prad) network to standard output # The gene ids are in Entrez format data(regulonprad) write.regulon(regulonblca,file="",n=10)
The interactome is a human Bladder Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonblca)data(regulonblca)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonblca) write.regulon(regulonblca,n=10)data(regulonblca) write.regulon(regulonblca,n=10)
The interactome is a human Breast Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonbrca)data(regulonbrca)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonbrca) write.regulon(regulonbrca,n=10)data(regulonbrca) write.regulon(regulonbrca,n=10)
The interactome is a human Cervical Squamous Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(reguloncesc)data(reguloncesc)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(reguloncesc) write.regulon(reguloncesc,n=10)data(reguloncesc) write.regulon(reguloncesc,n=10)
The interactome is a human Colon Adenocarcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(reguloncoad)data(reguloncoad)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(reguloncoad) write.regulon(reguloncoad,n=10)data(reguloncoad) write.regulon(reguloncoad,n=10)
The interactome is a human Esophageal Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonesca)data(regulonesca)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonesca) write.regulon(regulonesca,n=10)data(regulonesca) write.regulon(regulonesca,n=10)
The interactome is a human Glioblastoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulongbm)data(regulongbm)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulongbm) write.regulon(regulongbm,n=10)data(regulongbm) write.regulon(regulongbm,n=10)
The interactome is a human Head and Neck Squamous Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonhnsc)data(regulonhnsc)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonhnsc) write.regulon(regulonhnsc,n=10)data(regulonhnsc) write.regulon(regulonhnsc,n=10)
The interactome is a human Kidney Renal Clear Cell Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonkirc)data(regulonkirc)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonkirc) write.regulon(regulonkirc,n=10)data(regulonkirc) write.regulon(regulonkirc,n=10)
The interactome is a human Kidney Papillary Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonkirp)data(regulonkirp)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonkirp) write.regulon(regulonkirp,n=10)data(regulonkirp) write.regulon(regulonkirp,n=10)
The interactome is a human Acute Myeloid Leukemia context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonlaml)data(regulonlaml)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonlaml) write.regulon(regulonlaml,n=10)data(regulonlaml) write.regulon(regulonlaml,n=10)
The interactome is a human Liver Hepatocellular Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonlihc)data(regulonlihc)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonlihc) write.regulon(regulonlihc,n=10)data(regulonlihc) write.regulon(regulonlihc,n=10)
The interactome is a human Lung Adenocarcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonluad)data(regulonluad)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonluad) write.regulon(regulonluad,n=10)data(regulonluad) write.regulon(regulonluad,n=10)
The interactome is a human Lung Squamous Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonlusc)data(regulonlusc)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonlusc) write.regulon(regulonlusc,n=10)data(regulonlusc) write.regulon(regulonlusc,n=10)
The interactome is a human Neuroendocrine tumor context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonnet)data(regulonnet)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonnet) write.regulon(regulonnet, n=10)data(regulonnet) write.regulon(regulonnet, n=10)
The interactome is a human Ovarian Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonov)data(regulonov)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonov) write.regulon(regulonov,n=10)data(regulonov) write.regulon(regulonov,n=10)
The interactome is a human Pancreas Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonpaad)data(regulonpaad)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonpaad) write.regulon(regulonpaad,n=10)data(regulonpaad) write.regulon(regulonpaad,n=10)
The interactome is a human Pheochromocytoma and Paraganglioma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonpcpg)data(regulonpcpg)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonpcpg) write.regulon(regulonpcpg,n=10)data(regulonpcpg) write.regulon(regulonpcpg,n=10)
The interactome is a human Prostate Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonprad)data(regulonprad)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonprad) write.regulon(regulonprad,n=10)data(regulonprad) write.regulon(regulonprad,n=10)
The interactome is a human Rectal Adenocarcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonread)data(regulonread)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonread) write.regulon(regulonread,n=10)data(regulonread) write.regulon(regulonread,n=10)
The interactome is a human Sarcoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonsarc)data(regulonsarc)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonsarc) write.regulon(regulonsarc,n=10)data(regulonsarc) write.regulon(regulonsarc,n=10)
The interactome is a human Stomach Adenocarcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonstad)data(regulonstad)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonstad) write.regulon(regulonstad,n=10)data(regulonstad) write.regulon(regulonstad,n=10)
The interactome is a human Testicular Cancer context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulontgct)data(regulontgct)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulontgct) write.regulon(regulontgct,n=10)data(regulontgct) write.regulon(regulontgct,n=10)
The interactome is a human Thyroid Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonthca)data(regulonthca)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonthca) write.regulon(regulonthca,n=10)data(regulonthca) write.regulon(regulonthca,n=10)
The interactome is a human Thymoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonthym)data(regulonthym)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonthym) write.regulon(regulonthym,n=10)data(regulonthym) write.regulon(regulonthym,n=10)
The interactome is a human Utherine Corpus Endometroid Carcinoma context-specific regulatory network reverse engineered by the ARACNE-AP algorithm. The interactome is contained in a list object of S3 class ‘regulon’ where each element represent a transcriptional regulator (transcription factor) and contains two vectors: (1) a named numeric vector indicating the mode of regulation (MoR) for each target gene, whose ID is indicated by the names attribute of the vector. (2) a numeric vector indicating the confidence score for the TF-target interaction.
data(regulonucec)data(regulonucec)
Object of class regulon (regulon-class) containing network data generated by ARACNe-AP.
Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
data(regulonucec) write.regulon(regulonucec,n=10)data(regulonucec) write.regulon(regulonucec,n=10)
This function will print the network into an output stream. Four columns will be printed: the Regulator id, the Target id, the Mode of Action (MoA, based on Spearman correlation that indicates the sign of the connection and ranges between -1 and +1), the Likelihood (essentially an edge weight that indicates how strong the mutual information for an edge is when compared to the maximum observed MI in the network, it ranges between 0 and 1).
write.regulon( regulon, file="", sep="\t", header=TRUE, n=Inf, regulator=NULL )write.regulon( regulon, file="", sep="\t", header=TRUE, n=Inf, regulator=NULL )
regulon |
An object of class regulon |
file |
File name where the network will be printed |
sep |
String, a separator for the fields (default = "\t") |
header |
Logical. If a header should be printed. Default is TRUE |
n |
Numeric. How many interactions to print. Default is Inf |
regulator |
String. A particular regulator. Default is NULL |
Text output containing the network in tabular format.
### Create a random regulon with two hubs # The first hub will have 100 targets # The second hub will have 67 targets regulon<-list() regulon[["hub1"]]<-list( tfmode=setNames(runif(100,-1,1),paste0("target",sample(1:1000,100))), likelihood=runif(100,0,1) ) regulon[["hub2"]]<-list( tfmode=setNames(runif(67,-1,1),paste0("target",sample(1:1000,67))), likelihood=runif(67,0,1) ) class(regulon)<-"regulon" write.regulon(regulon,file="network.txt") ### Print a the Prostate Adenocarcinoma (prad) network to standard output # The gene ids are in Entrez format data(regulonprad) write.regulon(regulonprad,file="",n=10)### Create a random regulon with two hubs # The first hub will have 100 targets # The second hub will have 67 targets regulon<-list() regulon[["hub1"]]<-list( tfmode=setNames(runif(100,-1,1),paste0("target",sample(1:1000,100))), likelihood=runif(100,0,1) ) regulon[["hub2"]]<-list( tfmode=setNames(runif(67,-1,1),paste0("target",sample(1:1000,67))), likelihood=runif(67,0,1) ) class(regulon)<-"regulon" write.regulon(regulon,file="network.txt") ### Print a the Prostate Adenocarcinoma (prad) network to standard output # The gene ids are in Entrez format data(regulonprad) write.regulon(regulonprad,file="",n=10)