Phylter, a tool for analyzing, visualizing and filtering phylogenomics datasets.

Phylter is a tool that allows detecting, removing and visualizing outliers in phylogeneomics dataset by iteratively removing taxa in genes and optimizing a score of concordance between individual matrices.
Phylter relies on Distatis (Abdi et al, 2005), an extension of multidimensional scaling to 3 dimensions to compare multiple distance matrices at once.
Phylter takes as input either a collection of phylogenetic trees (that are converted to distance matrices by Phylter), or a collection of pairwise distance matrices (obtained from multiple sequence alignements, for instance).
Phylter accepts data with missing values (missing taxa in some genes).
Phylter does not accept that the same taxa is present multiple times in the same gene.

Phylter is written in R language.

Installation

Phylter is not yet on CRAN. To install the development version:

1. Install the release version of devtools from CRAN with install.packages("devtools").
2. Launch R and type:

library(devtools)
install_github("damiendevienne/phylter")

3. Once installed the package can be loaded using:

library("phylter")

Usage

1. Read trees from external file and save as a list called trees

trees<-read.tree("treefile.tre")

2. (optional) Read or get gene names somewhere (same order as the trees) and save it as a vector called names
3. Run phylter on your trees (see details below for possible options)

results<-phylter(trees, gene.names=names)

Options

The phylter() function is called as follows by default:

phylter(X, bvalue=0, distance="patristic", k=3, thres=0.3, Norm=TRUE, keep.species=TRUE, gene.names=NULL, test.island=TRUE, verbose=TRUE, stop.criteria=1e-5)

Possible options are:
bvalue: If X is a list of trees, nodes with a support below 'bvalue' will be collapsed prior to the outlier detection.
distance If X is a list of trees, type of distance used to compute the pairwise matrices for each tree. Can be "patristic" (sum of branch lengths separating tips, the default) or nodal (number of nodes separating tips).
k Strength of outlier detection. The higher this value the less outliers detected.
thres For the detection of complete outliers. Threshold above which genes or species are considered as complete outliers. 0.3 means that a gene (resp. species) is a complete outlier if it is detected as outlier for more than 30% of its species (resp. genes).
Norm Should the matrices be normalized. If TRUE (the default), each matrix is normalized such that its first eigenvalie is equal to one.
keep.species If TRUE, species are protected from being detected as complete outliers and filtered out.
gene.names List of gene names used to rename elements in X. If NULL (the default), elements are named 1,2,..,length(X).
test.island This should not be modified. If TRUE (the default), only the highest value in an 'island' of outliers is considered an outlier. This prevents non-outliers hitchhiked by outliers to be considered outliers themselves.
verbose If TRUE (the default), messages are written during the filtering process to get information on what is happening
stop.criteria The optimization stops when the gain (quality of compromise) between round n and round n+1 is smaller than this value. Default to 1e-5.

4. Analyze the results Many functions allow looking at the outliers detected and comparing before and after:

summary(results) # Get a summary: nb of outliers, gain in concordance, etc.
plot(results, "genes") # show the number of species in each gene, and how many per gene are outliers 
plot(results, "species") # show the number of genes where each species is found, and how many are outliers
plot2WR(results) # compare before and after genes x species matrices, highlighting missing data and outliers identified. 
plotDispersion(results) # plot the dispersion of data before and after outlier removal. One dot represents one gene x species association.
plotRV(results) # plot the genes x genes matrix showing pairwise correlation between genes. 
plotopti(results) #plot optimization scores during optimization.

5. Save the results Save the results of the analysis to an external file, for example to perform cleaning on raw alignments based the results from phylter.

write.phylter(results, file="phylter.out")

Example

A fungal dataset comprised of 246 genes for 21 species (Aguileta et al. 2008) is included in the package. To load it and test phylter on it:

data(fungi)
results<-phylter(fungi, distance="nodal", thres=0.2) #for example

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references

Abdi, H., Valentin, D., O’Toole, A.J., & Edelman, B. (2005). DISTATIS: The analysis of multiple distance matrices. Proceedings of the IEEE Computer Society: International Conference on Computer Vision and Pattern Recognition. (San Diego, CA, USA). pp. 42–47. https://www.utdallas.edu/~herve/abdi-distatis2005.pdf

G Aguileta, S Marthey, H Chiapello, M.-H Lebrun, F Rodolphe, E Fournier, A Gendrault-Jacquemard, T Giraud, Assessing the Performance of Single-Copy Genes for Recovering Robust Phylogenies, Systematic Biology, Volume 57, Issue 4, August 2008, Pages 613–627, https://doi.org/10.1080/10635150802306527

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For comments, suggestions and bug reports, please open an issue on this github repository.