README.Rmd

---
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

[![DOI](https://zenodo.org/badge/83733732.svg)](https://zenodo.org/badge/latestdoi/83733732)

# hmmpickr

## Overview

ChromHMM (Ernst & Kellis, 2012), an implementation of a hidden Markov model 
(HMM), uses epigenetic features such as histone modifications to represent 
observed (or output) states and unobserved (or hidden) states to represent 
chromatin states. Due to the nature of hidden states, the number of states will
need to be specified programmatically and oftentimes numerous candidate models 
are generated. The goal of hmmpickr is to quantitatively help pick the model 
whose states are the most well-defined.

## Installation

```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("csiu/hmmpickr")
```

## Getting started

```{r}
library(hmmpickr)

# List of ChromHMM models
model_files <- c(
  system.file("extdata", "model_roadmap15.txt", package="hmmpickr"),
  system.file("extdata", "model_roadmap18.txt", package="hmmpickr"))

# Calculate homogeneity cost
hmmpick(model_files, full_path = FALSE)

```

The list output of `hmmpick()` is sorted by ascending homogeneity cost. 
Therefore, the model in the first position is the model whose states are the 
most well-defined.


## Acknowledgement
- Model files are downloaded from (Roadmap Epigenomics Consortium, et al., 2015)