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Clustering.jl

PopGen.jl/src/KMeans.jl​

📦 not exported🔵 exported by PopGen.jl

🔵 cutree​

cutree(::PopData, hcres::Hclust; krange::UnitRange{Int64}, height::Union{Int64, Nothing} = nothing)
cutree(::PopData, hcres::Hclust; krange::Vector{Int64}, height::Union{Int64, Nothing} = nothing)

An expansion to the Clustering.cutree method (from Clustering.jl) that performs cluster assignments over krange on the Hclust output from hclust(). Returns a DataFrame of sample names and columns corresponding to assignments per k in krange. The PopData object is used only for retrieving the sample names.

Keyword Arguments

  • krange: the number of desired clusters, given as a vector (ex. [2,4,5]) or range (2:5)
  • h::Integer: the height at which the tree is cut (optional)

🔵 cluster​

cluster(::PopData, method::Function ; kwargs)

A convenience wrapper to perform clustering on a PopData object determined by a designated method (see below). The chosen method must also be supplied with the appropriate keyword arguments for that method. For more information on a specific method, see its docstring with ?methodname

Clustering Methods

  • kmeans: K-means++ clustering
    • kwargs: k, iterations, matrixtype
  • kmedoids: K-medoids clustering
    • kwargs: k, iterations, distance, matrixtype
  • hclust: Hierarchical clustering
    • kwargs: linkage, branchorder, distance, matrixtype
  • fuzzycmeans: Fuzzy C-means lustering
    • kwargs: c, fuzziness, iterations, matrixtype
  • dbscan: Density-based Spatial Clustering of Applications with Noise (DBSCAN)
    • kwargs: radius, minpoints, distance, matrixtype

🔵 dbscan​

dbscan(::PopData; radius::Float64, minpoints::Int64 = 2, distance::PreMetric = euclidean, matrixtype::Symbol = :pca)

An expansion of Clustering.dbscan (from Clustering.jl) to perform Density-based Spatial Clustering of Applications with Noise (DBSCAN) on a PopData object. This is a convenience method which converts the PopData object to either an allele frequency or PCA matrix, and performs DBSCAN clustering on the distance matrix of that. Returns a DbscanResult object, which contains the assignments in the .assignments field.

Keyword Arguments

  • radius::Float64: the radius of a point neighborhood
  • minpoints::Int: the minimum number of a core point neighbors (default: 2)
  • distance: type of distance matrix to calculate on matrixtype (default: euclidean)
    • see Distances.jl for a list of options (e.g. sqeuclidean, etc.)
  • matrixtype: type of input matrix (default: :pca)
    • :pca: matrix of Principal Components
    • :freq: matrix of allele frequencies

🔵 fuzzycmeans​

fuzzycmeans(data::PopData; c::Int64, fuzziness::Int64 = 2, iterations::Int64 = 100, matrixtype::Symbol = :pca)

An expansion of Clustering.fuzzy_cmeans (from Clustering.jl) to perform Fuzzy C-means clustering on a PopData object. This is a convenience method which converts the PopData object to either an allele frequency or PCA matrix, and performs Fuzzy C-means clustering on the Euclidean distance matrix of that. Returns a FuzzyCMeansResult object, which contains the assignment weights in the .weights field.

Keyword Arguments

  • c: the number of desired clusters, given as an Integer
  • fuzziness::Integer: clusters' fuzziness, must be >1 (default: 2)
    • a fuzziness of 2 is common for systems with unknown numbers of clusters
  • iterations::Int64: the maximum number of iterations to attempt to reach convergence (default: 100)
  • matrixtype: type of input matrix to compute (default: :pca)
    • :pca: matrix of Principal Components
    • :freq: matrix of scaled allele frequencies

🔵 hclust​

hclust(data::PopData; linkage::Symbol = :single, branchorder::Symbol = :r, distance::PreMetric = euclidean, matrixtype::Symbol = :pca)

An expansion of Clustering.hclust (from Clustering.jl) to perform hierarchical clustering on a PopData object. This is a convenience method which converts the PopData object to either an allele frequency or PCA matrix, converts that into a distance matrix, and performs hierarchical clustering on that distance matrix. Returns an Hclust object, which contains many metrics but does not include cluster assignments. Use cutree(::PopData, ::Hclust; krange...) to compute the sample assignments for a range of k clusters.

Keyword Arguments

  • linkage: defines how the distances between the data points are aggregated into the distances between the clusters (default: :single)
    • :single: use the minimum distance between any of the cluster members
    • :average: use the mean distance between any of the cluster members
    • :complete: use the maximum distance between any of the members
    • :ward: the distance is the increase of the average squared distance of a point to its cluster centroid after merging the two clusters
    • :ward_presquared: same as :ward, but assumes that the distances in the distance matrix are already squared.
  • branchorder: algorithm to order leaves and branches (default: :r)
    • :r: ordering based on the node heights and the original elements order (compatible with R's hclust)
    • :optimal: branches are ordered to reduce the distance between neighboring leaves from separate branches using the "fast optimal leaf ordering" algorithm
  • distance: type of distance matrix to calculate on matrixtype (default: euclidean)
    • see Distances.jl for a list of options (e.g. sqeuclidean, etc.)
  • matrixtype: type of input matrix (default: :pca)
    • :pca: matrix of Principal Components
    • :freq: matrix of allele frequencies

🔵 kmeans​

kmeans(data::PopData; k::Int64, iterations::Int64 = 100, matrixtype::Symbol = :pca)

Perform Kmeans clustering (using Kmeans++) on a PopData object. Returns a KmeansResult object. Use the keyword argument iterations (default: 100) to set the maximum number of iterations allowed to achieve convergence. Interally, kmeans clustering is performed on either the principal components of the scaled allele frequencies, or just the scaled allele frequencies themselves. In both cases, missing values are replaced by the global mean allele frequency.

Keyword Arguments

  • k: the number of desired clusters, given as an Integer
  • iterations::Int64: the maximum number of iterations to attempt to reach convergence (default: 100)
  • matrixtype: type of input matrix to compute (default: :pca)
    • :pca: matrix of Principal Components
    • :freq: matrix of scaled allele frequencies

Example

julia> cats = @nancycats ;

julia> km = kmeans(cats, k = 2)

🔵 kmedoids​

kmedoids(data::PopData; k::Int64, iterations::Int64 = 100, distance::PreMetric = euclidean, matrixtype::Symbol = :pca)

Perform Kmedoids clustering on a PopData object. Returns a KmedoidsResult object. Use the keyword argument iterations (default: 100) to set the maximum number of iterations allowed to achieve convergence. Interally, kmeans clustering is performed on either the principal components of the scaled allele frequencies, or just the scaled allele frequencies themselves. In both cases, missing values are replaced by the global mean allele frequency.

Keyword Arguments

  • k: the number of desired clusters, given as an Integer
  • iterations::Int64: the maximum number of iterations to attempt to reach convergence (default: 100)
  • distance: type of distance matrix to calculate on matrixtype (default: euclidean)
    • see Distances.jl for a list of options (e.g. sqeuclidean, etc.)
  • matrixtype: type of input matrix to compute (default: :pca)
    • :pca: matrix of Principal Components
    • :freq: matrix of scaled allele frequencies

🔵 show​

Base.show(io::IO, data::KMeansResults)