Package: BICORN 0.1.0

Xi Chen

BICORN: Integrative Inference of De Novo Cis-Regulatory Modules

Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.

Authors:Xi Chen [aut, cre], Jianhua Xuan [aut]

BICORN_0.1.0.tar.gz
BICORN_0.1.0.tar.gz(r-4.5-noble)BICORN_0.1.0.tar.gz(r-4.4-noble)
BICORN_0.1.0.tgz(r-4.4-emscripten)BICORN_0.1.0.tgz(r-4.3-emscripten)
BICORN.pdf |BICORN.html✨
BICORN/json (API)

# Install 'BICORN' in R:

install.packages('BICORN', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

Binding_TFs - TFs in the prior binding network
Binding_genes - Genes in the prior binding network
Binding_matrix - Prior TF-gene binding network
Exp_data - Gene expression data
Exp_genes - Genes in the expression data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.30 score 4 scripts 167 downloads 2 mentions 2 exports 0 dependencies

Last updated 7 years agofrom:acf2011f4a. Checks:OK: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 01 2024
R-4.5-linux	OK	Dec 01 2024

Exports:BICORN data_integration

Dependencies:

An Introduction to BICORN

Xi Chen

Rendered frommy-vignette.Rmdusingknitr::rmarkdownon Dec 01 2024.

Last update: 2018-06-06
Started: 2018-06-06

Citation

Readme and manuals

Help Manual

Help page	Topics
TF-gene regulation strength matrix	A
TF-gene regulation strength matrix sampled from the previous round	A_old
Regulation Strength Sampling Function	A_sampling
Inverse-gamma distribution hyper-parameter alpha	alpha
Gene baseline expression	base_line
Gene baseline expression sampled from the previous round.	base_line_old
Gene Baseline Expression Sampling Function	baseline_sampling
Inverse-gamma distribution hyper-parameter beta	beta
BICORN Algorithm Function	BICORN
Genes in the prior binding network	Binding_genes
Prior TF-gene binding network	Binding_matrix
TFs in the prior binding network	Binding_TFs
TF-gene binding network	C
TF-gene binding network sampled from the previous round	C_old
Prior TF-gene binding network	C_prior
cis-Regulatory Module Sampling Function	C_sampling_cluster
Data Initialization for BICORN	data_integration
Gene expression data	Exp_data
Genes in the expression data	Exp_genes
Regulation strength variance	sigma_A
Variance of baseline gene expression.	sigma_baseline
Variance of gene expression fitting residuals.	sigma_noise
Transcription factor activity variance	sigma_X
Fitting Residule Variance Sampling Function	sigmanoise_sampling
Transcription factr activity matrix	X
Transcription factr activity matrix sampled from the previous round	X_old
Transcription Factor Activity Sampling Function	X_sampling
Gene expression data used for module inference	Y