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README.Rmd
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README.Rmd
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---
bibliography: vignettes/bibliography.bib
output:
github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "inst/vignette-supp/",
echo=TRUE,
warning=FALSE,
message=FALSE,
tidy=TRUE
)
```
<!-- badges: start -->
[](https://github.com/gtonkinhill/fastbaps/actions)
[](https://zenodo.org/badge/latestdoi/137083307)
<!-- badges: end -->
# fastbaps
## Installation
`fastbaps` is currently available on github. It can be installed with `devtools`
```{r, eval = FALSE}
install.packages("devtools")
devtools::install_github("gtonkinhill/fastbaps")
```
If you would like to also build the vignette with your installation run:
```{r, eval=FALSE}
devtools::install_github("gtonkinhill/fastbaps", build_vignettes = TRUE)
```
### Conda
`fastbaps` can also be installed using Conda
```
conda install -c conda-forge -c bioconda -c defaults r-fastbaps
```
## Choice of Prior
Fastbaps includes a number of options for the Dirichlet prior hyperparamters. These range in order from most conservative to least as `symmetric`, `baps`, `optimised.symmetric` and `optimised.baps`. The choice of prior can be set using the `optimise_prior` function.
It is also possible to condition on a pre-existing phylogeny, which allows a user to partition the phylogeny using the fastbaps algorithm. This is described in more detail further down in the introduction.
## Quick Start
Run fastbaps.
**NOTE:** You need to replace the variable `fasta.file.name` with the path to your fasta file. The system.file function is only used in this example vignette.
```{r, fig.width =8, fig.height=6, fig.align='center'}
# devtools::install_github("gtonkinhill/fastbaps")
library(fastbaps)
library(ape)
fasta.file.name <- system.file("extdata", "seqs.fa", package = "fastbaps")
sparse.data <- import_fasta_sparse_nt(fasta.file.name)
sparse.data <- optimise_prior(sparse.data, type = "optimise.symmetric")
baps.hc <- fast_baps(sparse.data)
clusters <- best_baps_partition(sparse.data, as.phylo(baps.hc))
```
All these steps can be combined and the algorithm run over multiple levels by running
```{r}
sparse.data <- optimise_prior(sparse.data, type = "optimise.symmetric")
multi <- multi_res_baps(sparse.data)
```
## Command Line Script
The fastbaps package now includes a command line script. The location of this script can be found by running
```{r, eval=FALSE}
system.file("run_fastbaps", package = "fastbaps")
```
This script can then be copied to a location on the users path. If you have installed fastbaps using conda, this will already have been done for you.
## Citation
To cite fastbaps please use
> Tonkin-Hill,G., Lees,J.A., Bentley,S.D., Frost,S.D.W. and Corander,J. (2019) Fast hierarchical Bayesian analysis of population structure. Nucleic Acids Res., 10.1093/nar/gkz361.
## Introduction
```{r, echo = FALSE}
intro_rmd <- 'vignettes/introduction.Rmd'
raw_rmd <- readLines(intro_rmd)
# remove yaml
yaml_lines <- grep("---", raw_rmd)
# remove appendix (session info)
appendix <- grep("Appendix", raw_rmd)
compressed_rmd <- raw_rmd[c(-seq(yaml_lines[1], yaml_lines[2], by = 1),
-seq(appendix, length(raw_rmd)))]
writeLines(compressed_rmd, "child.Rmd")
```
```{r, child = 'child.Rmd'}
```
```{r cleanup, echo=FALSE, include=FALSE}
if (file.exists("child.Rmd")) {
file.remove("child.Rmd")
}
```