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HMM-integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data
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JEFworks-Lab/HoneyBADGER
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HoneyBADGER (hidden Markov model integratedBayesianapproach fordetecting CNV and LOH events from single-cellRNA-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;
(2) Bayesian hierarchical model uses allele and expression data to infer probability of CNVs in single cells
(3) CNV inference from transcriptional data enables transcriptional characterization of subclones and other integrative analyses
Please see theHoneyBADGERwebsite for more tutorials and information.
To installHoneyBADGER, we recommend usingdevtools:
require(devtools)devtools::install_github('JEFworks/HoneyBADGER')HoneyBADGER usesJAGS (Just Another Gibbs Sampler) throughrjags. Therefore,JAGS must be installed per your operating system requirements. Please see thisR-bloggers tutorial for additional tips for installingJAGS andrjags.
Additional dependencies may need to be installed fromBioconductor such asGenomicRanges and others:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")BiocManager::install("GenomicRanges")We welcome any bug reports, enhancement requests, and other contributions. To submit a bug report or enhancement request, please use theHoneyBADGERGitHub 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.