fastbaps is currently available on github. It can be installed with
devtools
install.packages("devtools")
devtools::install_github("gtonkinhill/fastbaps")If you would like to also build the vignette with your installation run:
devtools::install_github("gtonkinhill/fastbaps", build_vignettes = TRUE)fastbaps can also be installed using Conda
conda install -c conda-forge -c bioconda -c defaults r-fastbaps
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.
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.
# 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")
#> [1] "Optimised hyperparameter: 0.02"
baps.hc <- fast_baps(sparse.data)
#> [1] "Calculating initial clustering..."
#> [1] "Calculating initial dk values..."
#> [1] "Clustering using hierarchical Bayesian clustering..."
clusters <- best_baps_partition(sparse.data, as.phylo(baps.hc))
#> [1] "Calculating node marginal llks..."
#> [1] "Finding best partition..."All these steps can be combined and the algorithm run over multiple levels by running
sparse.data <- optimise_prior(sparse.data, type = "optimise.symmetric")
#> [1] "Optimised hyperparameter: 0.02"
multi <- multi_res_baps(sparse.data)The fastbaps package now includes a command line script. The location of this script can be found by running
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.
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.
The fast BAPS algorithm is based on applying the hierarchical Bayesian clustering (BHC) algorithm of (Heller and Ghahramani 2005) to the problem of clustering genetic sequences using the same likelihood as BAPS (Cheng et al. 2013). The Bayesian hierarchical clustering can be initiated with sequences as individual clusters or by running a faster conventional hierarchical clustering initially followed by BHC of the resulting clusters.
The algorithm has been written to take advantage of fast sparse matrix libraries and is able to handle 1000’s of sequences and 100,000’s of SNPs in under an hour on a laptop using a single core.
Alternatively, we can condition on an initial phylogentic or hierarchical tree and provide the partition of the hierarchy that maximises the BAPS likelihood. This is useful if the user is mainly interested in partitioning an already calculated phylogeny. We have also noticed that partitioning a hierarchy built using ward.D2 distance gives very reasonable results, very quickly.
library(fastbaps)
library(ggtree)
library(phytools)
library(ggplot2)We first need to load a multiple sequence alignment into sparse format. We can choose between the original BAPS prior or a prior proportional to the mean frequency of each allele in the population.
fasta.file.name <- system.file("extdata", "seqs.fa", package = "fastbaps")
sparse.data <- import_fasta_sparse_nt(fasta.file.name)Here we make use of the ‘optimised symmetric’ prior, which empirically chooses the variance of the Dirichlet prior on the component mixtures.
sparse.data <- optimise_prior(sparse.data, type = "optimise.symmetric")
#> [1] "Optimised hyperparameter: 0.02"It is a good idea to choose k.init to be significantly larger than the
number of clusters you expect. By default it is set to the number of
sequences / 4.
baps.hc <- fast_baps(sparse.data)
#> [1] "Calculating initial clustering..."
#> [1] "Calculating initial dk values..."
#> [1] "Clustering using hierarchical Bayesian clustering..."This provides a Bayesian hierarchical clustering of the data. To obtain the partition of this hierarchy under Dirichlet Process Mixture model run
best.partition <- best_baps_partition(sparse.data, baps.hc)
#> [1] "Calculating node marginal llks..."
#> [1] "Finding best partition..."We can plot the output of the algorithm along with a pre-calculated tree using ggtree (Yu et al. 2017).
newick.file.name <- system.file("extdata", "seqs.fa.treefile", package = "fastbaps")
iqtree <- phytools::read.newick(newick.file.name)
plot.df <- data.frame(id = colnames(sparse.data$snp.matrix), fastbaps = best.partition,
stringsAsFactors = FALSE)
gg <- ggtree(iqtree)
f2 <- facet_plot(gg, panel = "fastbaps", data = plot.df, geom = geom_tile, aes(x = fastbaps),
color = "blue")
f2
We can compare this result to other priors, the un-optimised symmetric or BAPS prior similar to STRUCTURE and hierBAPS, an optimised BAPS prior or the population mean based prior of Heller et al.
sparse.data <- optimise_prior(sparse.data, type = "baps")
baps.hc <- fast_baps(sparse.data)
#> [1] "Calculating initial clustering..."
#> [1] "Calculating initial dk values..."
#> [1] "Clustering using hierarchical Bayesian clustering..."
best.partition <- best_baps_partition(sparse.data, baps.hc)
#> [1] "Calculating node marginal llks..."
#> [1] "Finding best partition..."
plot.df <- data.frame(id = colnames(sparse.data$snp.matrix), fastbaps = best.partition,
stringsAsFactors = FALSE)
gg <- ggtree(iqtree)
f2 <- facet_plot(gg, panel = "fastbaps", data = plot.df, geom = geom_tile, aes(x = fastbaps),
color = "blue")
f2
we can also use the same prior as used in the BHC algorithm of Heller et al. However this tends to overpartition population genetic data.
sparse.data <- optimise_prior(sparse.data, type = "hc")
#> [1] "Optimised hyperparameter: 0.286"
baps.hc <- fast_baps(sparse.data)
#> [1] "Calculating initial clustering..."
#> [1] "Calculating initial dk values..."
#> [1] "Clustering using hierarchical Bayesian clustering..."
best.partition <- best_baps_partition(sparse.data, baps.hc)
#> [1] "Calculating node marginal llks..."
#> [1] "Finding best partition..."
plot.df <- data.frame(id = colnames(sparse.data$snp.matrix), fastbaps = best.partition,
stringsAsFactors = FALSE)
gg <- ggtree(iqtree)
f2 <- facet_plot(gg, panel = "fastbaps", data = plot.df, geom = geom_tile, aes(x = fastbaps),
color = "blue")
f2
we can also investigate multiple levels
sparse.data <- import_fasta_sparse_nt(fasta.file.name)
multi <- multi_res_baps(sparse.data)
plot.df <- data.frame(id = colnames(sparse.data$snp.matrix), fastbaps = multi$`Level 1`,
fastbaps2 = multi$`Level 2`, stringsAsFactors = FALSE)
gg <- ggtree(iqtree)
f2 <- facet_plot(gg, panel = "fastbaps level 1", data = plot.df, geom = geom_tile,
aes(x = fastbaps), color = "blue")
f2 <- facet_plot(f2, panel = "fastbaps level 2", data = plot.df, geom = geom_tile,
aes(x = fastbaps2), color = "green")
f2
We can also partition an initial hierarchy or phylogeny.
sparse.data <- import_fasta_sparse_nt(fasta.file.name, prior = "baps")
iqtree.rooted <- phytools::midpoint.root(iqtree)
best.partition <- best_baps_partition(sparse.data, iqtree.rooted)
#> [1] "Calculating node marginal llks..."
#> [1] "Finding best partition..."
plot.df <- data.frame(id = iqtree.rooted$tip.label, fastbaps = best.partition, stringsAsFactors = FALSE)
gg <- ggtree(iqtree.rooted)
f2 <- facet_plot(gg, panel = "fastbaps", data = plot.df, geom = geom_tile, aes(x = fastbaps),
color = "blue")
f2
finally we can also look at the stability of the inferred clusters using the Bootstrap
sparse.data <- optimise_prior(sparse.data, type = "optimise.symmetric")
#> [1] "Optimised hyperparameter: 0.02"
boot.result <- boot_fast_baps(sparse.data)
dendro <- as.dendrogram(fast_baps(sparse.data))
#> [1] "Calculating initial clustering..."
#> [1] "Calculating initial dk values..."
#> [1] "Clustering using hierarchical Bayesian clustering..."
gplots::heatmap.2(boot.result, dendro, dendro, tracecol = NA)
Cheng, Lu, Thomas R Connor, Jukka Sirén, David M Aanensen, and Jukka Corander. 2013. “Hierarchical and Spatially Explicit Clustering of DNA Sequences with BAPS Software.” Mol. Biol. Evol. 30 (5): 1224–28. https://doi.org/10.1093/molbev/mst028.
Heller, Katherine A, and Zoubin Ghahramani. 2005. “Bayesian Hierarchical Clustering.” In Proceedings of the 22Nd International Conference on Machine Learning, 297–304. ICML ’05. New York, NY, USA: ACM. https://doi.org/10.1145/1102351.1102389.
Kalyaanamoorthy, Subha, Bui Quang Minh, Thomas K F Wong, Arndt von Haeseler, and Lars S Jermiin. 2017. “ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates.” Nat. Methods 14 (6): 587–89. https://doi.org/10.1038/nmeth.4285.
Paradis, Emmanuel, Julien Claude, and Korbinian Strimmer. 2004. “APE: Analyses of Phylogenetics and Evolution in R Language.” Bioinformatics 20 (2): 289–90. https://doi.org/10.1093/bioinformatics/btg412.
Revell, Liam J. 2012. “Phytools: An R Package for Phylogenetic Comparative Biology (and Other Things).” Methods Ecol. Evol. 3 (2): 217–23. https://doi.org/10.1111/j.2041-210X.2011.00169.x.
Tonkin-Hill, Gerry, John A Lees, Stephen D Bentley, Simon D W Frost, and Jukka Corander. 2019. “Fast Hierarchical Bayesian Analysis of Population Structure.” Nucleic Acids Res., May. https://doi.org/10.1093/nar/gkz361.
Yu, Guangchuang, David K Smith, Huachen Zhu, Yi Guan, and Tommy Tsan-Yuk Lam. 2017. “Ggtree: An r Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data.” Methods Ecol. Evol. 8 (1): 28–36. https://doi.org/10.1111/2041-210X.12628.