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README.qmd
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---
format: gfm
knitr:
opts_chunk:
fig.path: "man/figures/"
---
<!--[](https://www.tidyverse.org/lifecycle/#maturing)-->
[](https://github.com/USCbiostats/aphylo/actions/workflows/ci.yml)
[](https://ci.appveyor.com/project/gvegayon/phylogenetic)
[](https://codecov.io/gh/USCbiostats/aphylo) [](https://image.usc.edu)
[](https://CRAN.R-project.org/package=aphylo)
[](https://CRAN.R-project.org/package=aphylo)
[](https://cran.r-project.org/package=aphylo)
# aphylo: Statistical Inference of Annotated Phylogenetic Trees <img src="man/figures/logo.svg" align="right" width="180px"/>
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
The `aphylo` R package implements estimation and data imputation methods for Functional Annotations in Phylogenetic Trees. The core function consists of the log-likelihood computation of observing a given phylogenetic tree with functional annotation on its leaves and the probabilities associated to gain and loss of function, including probabilities of experimental misclassification. The log-likelihood is computed using peeling algorithms, which required developing and implementing efficient algorithms for re-coding and preparing phylogenetic tree data to be used with the package. Finally, `aphylo` works smoothly with popular tools for analysis of phylogenetic data such as `ape` R package, "Analyses of Phylogenetics and Evolution."
The package is under MIT License and is developed by the Computing and Software Cores of the Biostatistics Division's NIH Project Grant (P01) at the Department of Preventive Medicine at the University of Southern California.
## Citation
```{r, comment = ""}
citation(package="aphylo")
```
## Install
This package depends on another on-development R package, the [`fmcmc`](https://github.com/USCbiostats/fmcmc). So first, you need to install it:
```r
devtools::install_github("USCbiostats/fmcmc")
```
Then you can install the `aphylo` package
```r
devtools::install_github("USCbiostats/aphylo")
```
## Reading data
```{r Loading the package}
library(aphylo)
```
```{r Reading Data}
# This datasets are included in the package
data("fakeexperiment")
data("faketree")
head(fakeexperiment)
head(faketree)
```
```{r get-offspring}
O <- new_aphylo(
tip.annotation = fakeexperiment[,2:3],
tree = as.phylo(faketree)
)
O
as.phylo(O)
# We can visualize it
plot(O)
plot_logLik(O)
```
## Simulating annotated trees
```{r}
set.seed(198)
dat <- raphylo(
50,
P = 1,
psi = c(0.05, 0.05),
mu_d = c(0.8, 0.3),
mu_s = c(0.1, 0.1),
Pi = .4
)
dat
```
## Likelihood
```{r Computing likelihood}
# Parameters and data
psi <- c(0.020,0.010)
mu_d <- c(0.40,.10)
mu_s <- c(0.04,.01)
eta <- c(.7, .9)
pi_root <- .05
# Computing likelihood
str(LogLike(dat, psi = psi, mu_d = mu_d, mu_s = mu_s, eta = eta, Pi = pi_root))
```
# Estimation
```{r MLE}
# Using L-BFGS-B (MLE) to get an initial guess
ans0 <- aphylo_mle(dat ~ psi + mu_d + Pi + eta)
# MCMC method
ans2 <- aphylo_mcmc(
dat ~ mu_d + mu_s + Pi,
prior = bprior(c(9, 1, 1, 1, 5), c(1, 9, 9, 9, 5)),
control = list(nsteps=5e3, burnin=500, thin=10, nchains=2))
ans2
plot(
ans2,
nsample = 200,
loo = TRUE,
ncores = 2L
)
```
```{r MCMC}
# MCMC Diagnostics with coda
library(coda)
gelman.diag(ans2$hist)
plot(ans2$hist)
```
# Prediction
```{r Predict}
pred <- prediction_score(ans2, loo = TRUE)
pred
plot(pred)
```
# Misc
During the development process, we decided to allow the user to choose what 'tree-reader' function he would use, particularly between using either the **rncl** R package or ape. For such, we created a short benchmark that compares both functions [here](playground/ape_now_supports_singletons.md).