Unfold single parameter cluster permutation testing

This is an adaptation of the ClusterDepth.jl tutorial.

We simulate a 1x2 design and perform cluster permutation testing via the ClusterDepth.jl package

Simulate data

Let's create data from 20 subjects

using Random
using CairoMakie
using UnfoldSim
using Unfold
using UnfoldMakie
using Statistics
using ClusterDepth
using DataFrames
using Distributions

n_subjects = 20
data,events = UnfoldSim.predef_eeg(n_subjects;return_epoched=true)
times = range(0,step=1/100,length=size(data,1))

Fitting regression Models

Fit an UnfoldModel to each subject:

formula = @formula(0 ~ 1 + condition)
models = map((d, ev) -> (fit(UnfoldModel, formula, DataFrame(ev), collect(d), times), ev.subject[1]),
    eachslice(data; dims=3),
    groupby(events, :subject))

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now we can inspect the data easily, and extract the face-effect

function add_subject!(df, s)
    df[!, :subject] .= s
    return df
end
allEffects = map((x) -> (effects(Dict(:condition => ["car", "face"]), x[1]), x[2]) |> (x) -> add_subject!(x[1], x[2]), models) |> e -> reduce(vcat, e)

plot_erp(allEffects; mapping=(color=:condition, group=:subject))

extract the face-coefficient from the linear model

allCoefs = map(m -> (coeftable(m[1]), m[2]) |> (x) -> add_subject!(x[1], x[2]), models) |> e -> reduce(vcat, e)
plot_erp(allCoefs; mapping=(group=:subject, col=:coefname))

let's unstack the tidy-coef table into a matrix and put it to clusterdepth for clusterpermutation testing

faceCoefs = allCoefs |> x -> subset(x, :coefname => x -> x .== "condition: face")
erpMatrix = unstack(faceCoefs, :subject, :time, :estimate) |> x -> Matrix(x[:, 2:end])' |> collect
summary(erpMatrix)

"45×20 Matrix{Union{Missing, Float64}}"

Clusterdepth

pvals = clusterdepth(erpMatrix; τ=quantile(TDist(n_subjects - 1), 0.95), nperm=5000);

well - that was fast, less than a second for a cluster permutation test. not bad at all!

Plotting

Some plotting, and we add the identified cluster

first calculate the ERP

faceERP = groupby(faceCoefs, [:time, :coefname]) |>
          x -> combine(x, :estimate => mean => :estimate,
    :estimate => std => :stderror);

put the significance into a dataframe-form

significant_regions_df = ClusterDepth.cluster(pvals .<= 0.05) |> x -> DataFrame(:from => times[x[1]], :to => times[x[1] .+ x[2]] , :coefname => "condition: face")

and plot it!

plot_erp(faceERP; stderror=true, significance=significant_regions_df)

Looks good to me! We identified the cluster :-)