arnaud-ma/BPRMeth.jl

Some julia code related to the BPRMeth package and paper.

On some benchmarks, it is hundreds of times faster than the original R package. Multithreading is not supported though because
the speed boost is partly based on memory management.

Warning

Only used for study purposes. Expect nothing more. Should be considered as a proof of concept. For example it is not documented, and the API is not stable at all.

Installation

julia>] add https://github.com/arnaud-ma/BPRMeth.jl

Dataset example

The package comes with a small example dataset available in the data/ folder, which is a subset of the ENCODE K562 cell line. It contains the zipped methylation data in a bedRrbs format and the annotation in a gtf format.

Example

Loading data

using BPRMeth
using StableRngs; rng = StableRng(42) # used for reproducibility, optional
using Plots

met_file = "data/K562/seq_rep1.bedRrbs.gz"
anno_file = "data/K562/anno_rep1V3.gtf.gz"

regions = construct_regions(; met_file, anno_file, min_sites = 15) #! take some seconds

To have the same dataset as the R package:

using FileIO, BPRMeth
regions = load("regionsR.jld2", "regionsR")

Model and visualization

nb_basis = 5
model = bpr_model(nb_basis)
# or model = KGLM(nb_basis; kind=Binomial, link=ProbitLink, kernel = GaussianKernel, ridge_factor = 0.5, μ = LinRange(-1, 1, M + 2)[2:(end - 1)], lengthscale = sqrt(2) / M, intercept = true)

plot(region)
X = LinRange(-1, 1, 1000)
p = plot(X, BPRMeth.predict(model, X))

# fit one region (the one you want)
region = only([r for r in regions if r.region.gene_id == "ENSG00000182087"])
result = fit_mle(model, regions[1])

plot(region)
X = LinRange(-1, 1, 1000)
plot!(p, X, BPRMeth.predict(model, X))

# fit all regions at once
results = fit_mle(model, regions)

Clustering

Clustering results supports the Clustering.jl API.

nb_clusters = 3
cluster_result = fit_mle(nb_clusters, regions, model; rng = copy(rng), verbose = true)


# some visualization
p = plot()
X = LinRange(-1, 1, 1000)
ys = [BPRMeth.predict(model, X, result) for result in cluster_result.models]
p = plot(X, Ys, linewidth = 3)