docs/index.md

layout: default

HoneyBADGER

Build Status

HoneyBADGER (hidden Markov model integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data) identifies and infers the presence of CNV and LOH events in single cells and reconstructs subclonal architecture using allele and expression information from single-cell RNA-sequencing data.

The overall approach is detailed in the following publication: Fan J*, Lee HO*, Lee S, et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 2018;

Benefits and Capabilities

(1) Iterative HMM approach detects CNVs

![]({{ site.baseurl }}/assets/img/approach.png)

(2) Bayesian hierarchical model uses allele and expression data to infer probability of CNVs in single cells

![]({{ site.baseurl }}/assets/img/example.png)

(3) CNV inference from transcriptional data enables transcriptional characterization of subclones and other integrative analyses

![]({{ site.baseurl }}/assets/img/integration.png)

Installation

To install HoneyBADGER, we recommend using devtools:

require(devtools)
devtools::install_github('JEFworks/HoneyBADGER')

HoneyBADGER uses JAGS (Just Another Gibbs Sampler) through rjags. Therefore, JAGS must be installed per your operating system requirements. Please see this R-bloggers tutorial for additional tips for installing JAGS and rjags.

Additional dependencies may need to be installed from Bioconductor such as GenomicRanges and others:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GenomicRanges")

Tutorials

Contributing

We welcome any bug reports, enhancement requests, and other contributions. To submit a bug report or enhancement request, please use the HoneyBADGER GitHub issues tracker. For more substantial contributions, please fork this repo, push your changes to your fork, and submit a pull request with a good commit message.



JEFworks/HoneyBADGER documentation built on July 24, 2021, 3:01 p.m.