Package 'rhierbaps'

calc_change_in_ml

Description

Calculate the change in the log marginal likelihood after moving index to each possible cluster

Usage

calc_change_in_ml(snp.object, partition, indexes)

Arguments

snp.object	A snp.object containing the processed SNP data.
partition	An integer vector indicating a partition of the isolates.
indexes	Indexes of the isolates to be moved (must come from one cluster.)

Value

the best cluster to move indexes to.

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calc_log_ml

Description

Calculate the log marginal likelihood assuming a Multinomial-Dirichlet distribution

Usage

calc_log_ml(snp.object, partition)

Arguments

snp.object	A snp.object containing the processed SNP data.
partition	An integer vector indicating a partition of the isolates.

Value

The log marginal likelihood of the given partition.

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hierBAPS

Description

Runs the hierBAPS algorithm of Cheng et al. 2013

Usage

hierBAPS(
  snp.matrix,
  max.depth = 2,
  n.pops = floor(nrow(snp.matrix)/5),
  quiet = FALSE,
  n.extra.rounds = 0,
  assignment.probs = FALSE,
  n.cores = 1
)

Arguments

snp.matrix	Character matrix of aligned sequences produced by load_fasta.
max.depth	Maximum depth of hierarchical search (default = 2).
n.pops	Maximum number of populations in the data (default = number of isolates/5)
quiet	Whether to suppress progress information (default=FALSE).
n.extra.rounds	The number of additional rounds to perform after the default hierBAPS settings (default=0). If set to Inf it will run until a local optimum is reached (this might take a long time).
assignment.probs	whether or not to calculate the assignment probabilities to each cluster (default=FALSE)
n.cores	The number of cores to use.

Value

A list containing a dataframe indicating an assignment of each sequence to hierarchical clusters as well as the log marginal likelihoods for each level.

Author(s)

Gerry Tonkin-Hill

References

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.” Molecular Biology and Evolution 30 (5): 1224–28.

Examples

snp.matrix <- load_fasta(system.file("extdata", "small_seqs.fa", package = "rhierbaps"))
hb <- hierBAPS(snp.matrix, max.depth=2, n.pops=20, quiet=FALSE)


snp.matrix <- load_fasta(system.file("extdata", "seqs.fa", package = "rhierbaps"))
system.time({hb <- hierBAPS(snp.matrix, max.depth=2, n.pops=20, quiet=FALSE)})

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join_units_2

Description

Peform an iteration of the second move in the algorithm. That is combine two clusters to improve the marginal likelihood.

Usage

join_units_2(
  snp.object,
  partition,
  threshold = 1e-05,
  n.cores = 1,
  comb.chache = NULL
)

Arguments

snp.object	A snp.object containing the processed SNP data.
partition	An integer vector indicating an initial partition of the isolates.
threshold	The increase in marginal log likelihood required to accept a move.
n.cores	The number of cores to use.
comb.chache	a matrix recording previous marginal llks of combining clusters

Value

The best partition after combining two clusters as well as a boolean value indicating whether a move increased the marginal likelihood.

---

load_fasta

Description

Loads a fasta file into matrix format ready for running the hierBAPS algorithm.

Usage

load_fasta(msa, keep.singletons = FALSE)

Arguments

msa	Either the location of a fasta file or ape DNAbin object containing the multiple sequence alignment data to be clustered
keep.singletons	A logical indicating whether to consider singleton mutations in calculating the clusters

Value

A character matrix with filtered SNP data

Examples

msa <- system.file("extdata", "seqs.fa", package = "rhierbaps")
snp.matrix <- load_fasta(msa)

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model_search_parallel

Description

Clusters DNA alignment using independent loci model

Usage

model_search_parallel(
  snp.object,
  partition,
  round.types,
  quiet = FALSE,
  n.extra.rounds = 0,
  n.cores = 1
)

Arguments

snp.object	A snp.object containing the processed SNP data.
partition	An integer vector indicating an initial starting partition.
round.types	A vector indicating which series of moves to make.
quiet	Whether to suppress progress information (default=FALSE).
n.extra.rounds	The number of additional rounds to perform after the default hierBAPS settings (default=0). If set to Inf it will run until a local optimum is reached (this might take a long time).
n.cores	The number of cores to use.

Value

an optimised partition and marginal llk

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move_units_1

Description

Peform an iteration of the first move in the algorithm. That is move units from one cluster to another to improve the marginal likelihood

Usage

move_units_1(
  snp.object,
  partition,
  threshold = 1e-05,
  frac.clust.searched = 0.3,
  min.clust.size = 20,
  n.cores = 1
)

Arguments

snp.object	A snp.object containing the processed SNP data.
partition	An integer vector indicating an initial partition of the isolates.
threshold	The increase in marginal log likelihood required to accept a move.
frac.clust.searched	The percentage of a large cluster that will be moved.
min.clust.size	All isolates in clusters less than or equal to min.clus.size will be searched.
n.cores	The number of cores to use.

Value

The best partition after moving units from one cluster to another as well as a boolean value indicating whether a move increased the marginal likelihood.

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plot_sub_cluster

Description

Creates a zoom plot using ggtree focusing on a cluster.

Usage

plot_sub_cluster(hb.object, tree, level, sub.cluster)

Arguments

hb.object	The resulting object from running hierBAPS
tree	A phylo tree object to plot
level	The level of the subcluster to be considered.
sub.cluster	An integer representing the subcluster to be considered.

Examples

snp.matrix <- load_fasta(system.file("extdata", "seqs.fa", package = "rhierbaps"))
newick.file.name <- system.file("extdata", "seqs.fa.treefile", package = "rhierbaps")
tree <- phytools::read.newick(newick.file.name)
hb.result <- hierBAPS(snp.matrix, max.depth=2, n.pops=20)
plot_sub_cluster(hb.result, tree, level = 1, sub.cluster = 9)

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preproc_alignment

Description

Preprocessed the snp matrix for hierBAPS.

Usage

preproc_alignment(snp.matrix)

Arguments

snp.matrix

A matrix containing SNP data. Rows indicate isolates and columns loci.

Value

an snp.object

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reallocate_units_4

Description

Peform an iteration of the fourth move in the algorithm. That is split cluster into n subclusters and re-allocate one sub-cluster.

Usage

reallocate_units_4(
  snp.object,
  partition,
  threshold = 1e-05,
  min.clust.size = 20,
  split = FALSE,
  n.cores = 1
)

Arguments

snp.object	A snp.object containing the processed SNP data.
partition	An integer vector indicating an initial partition of the isolates.
threshold	The increase in marginal log likelihood required to accept a move.
min.clust.size	Clusters smaller than min.clust.size will not be split.
split	Whether to split only into two clusters (for move type 3).
n.cores	The number of cores to use.

Value

The best partition after splitting a cluster and re-allocating as well as a boolean value indicating whether a move increased the marginal likelihood.

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save_lml_logs

Description

Saves the log marginal likelihoods to a text file.

Usage

save_lml_logs(hb.object, file)

Arguments

hb.object	The resulting object from runnign hierBAPS
file	The file you would like to save the log output to.

Examples

snp.matrix <- load_fasta(system.file("extdata", "small_seqs.fa", package = "rhierbaps"))
hb.result <- hierBAPS(snp.matrix, max.depth=2, n.pops=20)
save_lml_logs(hb.result,  file.path(tempdir(), "output_file.txt"))

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split_clusters_3

Description

Peform an iteration of the third move in the algorithm. That is split cluster in two and re-allocate one sub-cluster.

Usage

split_clusters_3(
  snp.object,
  partition,
  threshold = 1e-05,
  min.clust.size = 20,
  n.cores = 1
)

Arguments

snp.object	A snp.object containing the processed SNP data.
partition	An integer vector indicating an initial partition of the isolates.
threshold	The increase in marginal log likelihood required to accept a move.
min.clust.size	Clusters smaller than min.clust.size will not be split.
n.cores	The number of cores to use.

Value

The best partition after splitting a cluster and re-allocating as well as a boolean value indicating whether a move increased the marginal likelihood.

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