---
title: "Phylogenetic Methods for Multiple Gene Data"
author: "Thibaut Jombart"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{apex: Phylogenetic Methods for Multiple Gene Data.}
  \usepackage[utf8]{inputenc}
---


```{r setup, echo=FALSE}
# set global chunk options: images will be 7x5 inches
knitr::opts_chunk$set(fig.width=7, fig.height=10, fig.path="figs/")
old <- options(digits = 4)
```

*apex*: Phylogenetic Methods for Multiple Gene Data
=================================================
*apex* implements new classes and methods for analysing DNA sequences from multiple genes.
It implements new classes extending object classes from *ape* and *phangorn* to store multiple gene data, and some useful wrappers mimicking existing functionalities of these packages for multiple genes.
This document provides an overview of the package's content.


Installing *apex*
-------------
To install the development version from github:
```{r install, eval=FALSE}
library(devtools)
install_github("thibautjombart/apex")
```

The stable version can be installed from CRAN using:
```{r install2, eval=FALSE}
install.packages("apex")
```

Then, to load the package, use:
```{r load}
library("apex")
```

Importing data
--------------
### *ape* wrappers
Two simple functions permit to import data from multiple alignements into `multidna` objects:
* **read.multidna:** reads multiple DNA alignments with various formats
* **read.multiFASTA:** same for FASTA files

Both functions rely on the single-gene counterparts in *ape* and accept the same arguments.
Each file should contain data from a given gene, where sequences should be named after individual labels only.
Here is an example using a dataset from *apex*:
```{r readfiles}
## get address of the file within apex
files <- dir(system.file(package="apex"),patter="patr", full=TRUE)
files # this will change on your computer

## read these files
x <- read.multiFASTA(files)
x
names(x@dna) # names of the genes
oldpar <-par(mar=c(6,11,4,1))
plot(x)
par(oldpar)
```

### *phangorn* wrappers
In addition to the above functions for importing data:
* **read.multiphyDat:** reads multiple DNA alignments with various formats.
The arguments are the same as the single-gene `read.phyDat` in *phangorn*:
```{r readfiles phyDat}
z <- read.multiphyDat(files, format="fasta")
z
```

New object classes
------------------
Two new classes of object extend existing data structures for multiple genes:
* **multidna:** based on *ape*'s `DNAbin` class, useful for distance-based trees.
* **multiphyDat:** based on *phangorn*'s `phyDat` class, useful for likelihood-based and parsimony trees.
Conversion between these classes can be done using `multidna2multiPhydat` and `multiPhydat2multidna`.

###  multidna
This formal (S4) class can be seen as a multi-gene extension of *ape*'s `DNAbin` class.
Data is stored as a list of `DNAbin` objects, with additional slots for extra information.
The class definition can be obtained by:
```{r multidnaDef}
getClassDef("multidna")
```
* **@dna**: list of `DNAbin` matrices, each corresponding to a given gene/locus, with matching rows (individuals)
* **@labels**: labels of the individuals (rows of the matrices in `@dna`)
* **@n.ind**: the number of individuals
* **@n.seq**: the total number of sequences in the dataset, including gaps-only sequences
* **@n.seq.miss**: the total number of gaps-only (i.e., missing) sequences in the dataset
* **@ind.info**: an optional dataset storing individual metadata
* **@gene.info**: an optional dataset storing gene metadata

Any of these slots can be accessed using `@`, however accessor functions are available for most and are preferred (see examples below).

New `multidna` objects can be created via different ways:

1. using the constructor `new("multidna", ...)`
2. reading data from files (see section on 'importing data' below)
3. converting a `multiphyDat` object using `multidna2multiphyDat`

We illustrate the use of the constructor below (see `?new.multidna`) for more information.
We use *ape*'s dataset *woodmouse*, which we artificially split in two 'genes', keeping the first 500 nucleotides for the first gene, and using the rest as second gene. Note that the individuals need not match across different genes: matching is handled by the constructor.

```{r multidnaclass}
## empty object
new("multidna")

## using a list of genes as input
data(woodmouse)
woodmouse
genes <- list(gene1=woodmouse[,1:500], gene2=woodmouse[,501:965])
x <- new("multidna", genes)
x

## access the various slots
getNumInd(x) # The number of individuals
getNumLoci(x) # The number of loci
getLocusNames(x) # The names of the loci
getSequenceNames(x) # A list of the names of the sequences at each locus

getSequences(x) # A list of all loci
getSequences(x, loci = 2, simplify = FALSE) # Just the second locus (a single element list)
getSequences(x, loci = "gene1") # Just the first locus as a DNAbin object

## compare the input dataset and the new multidna
oldpar <- par(mfrow=c(3,1), mar=c(6,6,2,1))
image(woodmouse)
image(as.matrix(getSequences(x, 1)))
image(as.matrix(getSequences(x, 2)))
par(oldpar)
## same but with missing sequences and wrong order
genes <- list(gene1=woodmouse[,1:500], gene2=woodmouse[c(5:1,14:15),501:965])
x <- new("multidna", genes)
x
oldpar <- par(mar=c(6,6,2,1))
plot(x)
par(oldpar)
```


###  multiphyDat
Like `multidna` and *ape*'s `DNAbin`, the formal (S4) class `multiphyDat` is a multi-gene extension of *phangorn*'s `phyDat` class.
Data is stored as a list of `phyDat` objects, with additional slots for extra information.
The class definition can be obtained by:
```{r multiphyDatDef}
getClassDef("multiphyDat")
```
* **@seq**: list of `phyDat` objects, each corresponding to a given gene/locus, with matching rows (individuals); unlike `multidna` which is retrained to DNA sequences, this class can store any characters, including amino-acid sequences 
* **@type**: a character string indicating the type of sequences stored
* **@labels**: labels of the individuals (rows of the matrices in `@dna`)
* **@n.ind**: the number of individuals
* **@n.seq**: the total number of sequences in the dataset, including gaps-only sequences
* **@n.seq.miss**: the total number of gaps-only (i.e., missing) sequences in the dataset
* **@ind.info**: an optional dataset storing individual metadata
* **@gene.info**: an optional dataset storing gene metadata

Any of these slots can be accessed using `@` (see example below).

As for `multidna`, `multiphyDat` objects can be created via different ways:

1. using the constructor `new("multiphyDat", ...)`
2. reading data from files (see section on 'importing data' below)
3. converting a `multidna` object using `multiphyDat2multidna`

As before, we illustrate the use of the constructor below (see `?new.multiphyDat`) for more information.
```{r multiphyDatclass}
data(Laurasiatherian)
Laurasiatherian

## empty object
new("multiphyDat")

## simple conversion after artificially splitting data into 2 genes
genes <- list(gene1=subset(Laurasiatherian,,1:1600, FALSE),
      	 gene2=subset(Laurasiatherian,,1601:3179, FALSE))
x <- new("multiphyDat", genes, type="DNA")
x
```


Handling data
--------------
Several functions facilitate data handling:
* **concatenate:** concatenate several genes into a single `DNAbin` or `phyDat` matrix
* **x[i,j]:** subset x by individuals (i) and/or genes (j)
* **multidna2multiphyDat:** converts from `multidna` to `multiphyDat`
* **multiphyDat2multidna:** converts from `multiphyDat` to `multidna`


Example code:
```{r handling}
files <- dir(system.file(package="apex"),patter="patr", full=TRUE)
files

## read files
x <- read.multiFASTA(files)
x
oldpar <- par(mar=c(6,11,4,1))
plot(x)

## subset
plot(x[1:3,2:4])
par(oldpar)
```

```{r concat, fig.width=12, fig.height=7}
## concatenate
y <- concatenate(x)
y
oldpar <- par(mar=c(5,8,4,1))
image(y)
par(oldpar)

## concatenate multiphyDat object
z <- multidna2multiphyDat(x)
u <- concatenate(z)
u

tree <- pratchet(u, trace=0, all = FALSE)
oldpar <- par(mar=c(1,1,1,1))
plot(tree, "u")
par(oldpar)
```

Building trees
---------------
### Distance-based trees
Distance-based trees (e.g. Neighbor Joining) can be obtained for each gene in a `multidna` object using `getTree`
```{r gettree}
## make trees, default parameters
trees <- getTree(x)
trees
```
```{r hidePlotMultiPhylo, echo=TRUE,eval=FALSE}
plot(trees, 4, type="unrooted")
```
```{r plotMultiPhylo, echo=FALSE,eval=TRUE}
oldpar <- par(mfrow=c(2,2)) 
for(i in 1:length(trees))plot(trees[[i]], type="unr")
par(oldpar)
```

As an alternative, all genes can be pooled into a single alignment to obtain a single tree using:
```{r plotPhyloSingle, echo=FALSE,eval=TRUE}
## make one single tree based on concatenated genes
tree <- getTree(x, pool=TRUE)
tree
plot(tree, type="unrooted")
```

### Likelihood-based trees
It is also possible to use functions from `phangorn` to estimate with maximum likelihood trees.
Here is an example using the `multiphyDat` object `z` created in the previous section:
```{r pmlPart, eval=FALSE}
## input object
z
## build trees
pp <- pmlPart(bf ~ edge + nni, z, control = pml.control(trace = 0))
pp
## convert trees for plotting
trees <- pmlPart2multiPhylo(pp)
```
```{r hidePlotMultiPhylo2, echo=TRUE,eval=FALSE}
plot(trees, 4)
```
```{r plotPmlPart, echo=FALSE,eval=FALSE}
par(mfrow=c(2,2)); for(i in 1:length(trees))plot(trees[[i]])
```


Exporting data
---------------
The following functions enable the export from *apex* to other packages:
* **multidna2genind:** concatenates genes and export SNPs into a `genind` object; alternatively, Multi-Locus Sequence Type (MLST) can be used to treat genes as separate locus and unique sequences as alleles.
* **multiphyDat2genind:** does the same for multiphyDat object

This is illustrated below:
```{r export}
## find source files in apex
library(adegenet)
files <- dir(system.file(package="apex"),patter="patr", full=TRUE)

## import data
x <- read.multiFASTA(files)
x

## extract SNPs and export to genind
obj1 <- multidna2genind(x)
obj1

```

The MLST option can be useful for a quick diagnostic of diversity amongst individuals.
While it is best suited to clonal organisms, we illustrate this procedure using our toy dataset:
```{r mlst}
obj3 <- multidna2genind(x, mlst=TRUE)
obj3

## alleles of the first locus (=sequences)
alleles(obj3)[[1]]
```

```{r reset defaults, echo=FALSE}
options(old)
```
