Amino Acids

using CairoMakie, GCPDecompositions, LinearAlgebra

Load data

using CacheVariables, MAT, ZipFile

data = @cache "amino-acids-cache/data.bson" let
    # Download file
    url = "https://ucphchemometrics.com/wp-content/uploads/2023/05/claus.zip"
    zipname = download(url, tempname(@__DIR__))

    # Extract MAT file
    zipfile = ZipFile.Reader(zipname)
    matname = tempname(@__DIR__)
    write(matname, only(zipfile.files))
    close(zipfile)

    # Extract data
    data = matread(matname)

    # Clean up and output data
    rm(zipname)
    rm(matname)
    data
end

Dict{String, Any} with 6 entries:
  "evalme" => "delta=input(' How many nm above exc. would you like to cut off: ');id=on…
  "EmAx"   => [250.0 251.0 … 449.0 450.0]
  "X"      => [0.858 0.32 … 11.931 12.159; 1.312 1.742 … 2.376 1.716; … ; 1.243 -0.071 …
  "ExAx"   => [240.0 241.0 … 299.0 300.0]
  "readme" => Any["1.Column Trp in M", "2.Column Tyr in M", "3.Column Phe in M"]
  "y"      => [2.67e-6 0.0 0.0; 0.0 1.33e-5 0.0; … ; 1.58e-6 5.44e-6 0.000355; 8.79e-7 …

X = data["X"]

5×201×61 Array{Float64, 3}:
[:, :, 1] =
 0.858   0.32   0.863   0.301  -0.16   …  14.291  15.286  14.53   11.931  12.159
 1.312   1.742  0.987  -0.166   0.928      2.33    2.23    1.975   2.376   1.716
 1.248  -0.319  0.658   0.414  -0.518      2.989   2.938   2.739   2.782   3.264
 1.243  -0.071  1.017  -0.253  -1.151     10.961   9.597   8.76    7.894   7.189
 3.963   1.425  1.764   1.769   1.107      6.05    6.34    5.011   5.558   4.724

[:, :, 2] =
  1.868  1.01    1.904   1.198   0.559  …  14.359  14.087  13.352  13.123  11.265
  1.659  1.703  -0.317   0.882   0.91       1.934   2.378   2.221   1.675   1.443
 -1.364  1.393   0.814  -0.744  -0.322      3.194   2.536   2.81    2.381   2.026
 -0.246  2.12    0.247   0.442   0.157      9.526   9.73    8.261   8.198   7.19
  2.635  3.581   2.284   1.63    1.298      5.654   5.92    5.058   5.224   4.642

[:, :, 3] =
 3.017   2.368  2.014   0.323  -0.24   …  14.335  14.674  14.09   13.342  13.222
 3.014   2.774  2.727   1.206   0.325      2.288   1.786   2.39    1.825   1.912
 0.373  -0.129  0.432   0.103  -0.92       3.154   3.12    3.332   2.696   3.175
 1.303   0.397  1.933  -1.355  -0.101      9.673   9.766   9.351   8.018   8.948
 4.02    3.929  4.573   0.297   1.691      6.076   5.386   6.276   5.264   5.301

;;; … 

[:, :, 59] =
  0.067   0.035   0.102   0.011   0.074  0.111  …  7.214  7.337  6.813  7.07   6.23
 -0.05   -0.025   0.035  -0.093  -0.066  0.037     1.993  1.825  1.794  1.856  1.525
 -0.032  -0.008  -0.001  -0.086   0.004  0.078     0.899  1.093  0.974  0.943  0.918
  0.077   0.106   0.1     0.018   0.005  0.103     4.614  4.481  4.553  4.214  4.206
  0.071   0.096   0.101   0.013  -0.074  0.035     2.899  2.876  2.52   2.911  2.201

[:, :, 60] =
 0.104  0.111   0.071  0.236  0.107  0.117  …  6.268  6.174  6.293  5.325  5.7
 0.0    0.051  -0.033  0.1    0.008  0.052     1.967  1.784  1.788  1.535  1.537
 0.11   0.052  -0.01   0.025  0.102  0.051     1.077  1.168  1.103  0.826  0.823
 0.035  0.051   0.07   0.037  0.101  0.121     3.767  3.935  4.128  3.616  3.337
 0.103  0.118   0.068  0.103  0.104  0.051     2.428  2.578  2.333  2.64   2.194

[:, :, 61] =
 0.111  0.092  -0.037  0.05   0.037   0.058  …  5.26   4.682  4.518  4.678  4.52
 0.034  0.033  -0.088  0.017  0.001  -0.008     1.767  1.847  1.494  1.439  1.522
 0.091  0.032  -0.03   0.108  0.075  -0.007     1.029  1.047  0.805  0.7    0.775
 0.001  0.093  -0.033  0.052  0.032  -0.007     3.476  3.523  3.151  3.204  2.728
 0.035  0.101   0.022  0.12   0.102   0.06      1.869  2.142  1.901  1.782  1.993

em_wave = dropdims(data["EmAx"]; dims=1)

201-element Vector{Float64}:
 250.0
 251.0
 252.0
 253.0
 254.0
 255.0
 256.0
   ⋮
 445.0
 446.0
 447.0
 448.0
 449.0
 450.0

ex_wave = dropdims(data["ExAx"]; dims=1)

61-element Vector{Float64}:
 240.0
 241.0
 242.0
 243.0
 244.0
 245.0
 246.0
   ⋮
 295.0
 296.0
 297.0
 298.0
 299.0
 300.0

with_theme() do
    fig = Figure(; size=(800,500))

    # Loop through samples
    for i in 1:size(X,1)
        ax = Axis3(fig[fldmod1(i, 3)...]; title="Sample $i",
            xlabel="Emission\nWavelength", xticks=250:100:450,
            ylabel="Excitation\nWavelength", yticks=240:30:300,
            zlabel=""
        )
        surface!(ax, em_wave, ex_wave, X[i,:,:])
    end

    rowgap!(fig.layout, 40)
    colgap!(fig.layout, 50)
    resize_to_layout!(fig)
    fig
end

Run CP Decomposition

M = gcp(X, 3)

5×201×61 CPD{Float64, 3, Vector{Float64}, Matrix{Float64}} with 3 components
λ weights:
3-element Vector{Float64}: …
U[1] factor matrix:
5×3 Matrix{Float64}: …
U[2] factor matrix:
201×3 Matrix{Float64}: …
U[3] factor matrix:
61×3 Matrix{Float64}: …

with_theme() do
    fig = Figure()

    # Plot factors (normalized by max)
    for row in 1:ncomponents(M)
        barplot(fig[row,1], 1:size(X,1), normalize(M.U[1][:,row], Inf))
        lines(fig[row,2], em_wave, normalize(M.U[2][:,row], Inf))
        lines(fig[row,3], ex_wave, normalize(M.U[3][:,row], Inf))
    end

    # Link and hide x axes
    linkxaxes!(contents(fig[:,1])...)
    linkxaxes!(contents(fig[:,2])...)
    linkxaxes!(contents(fig[:,3])...)
    hidexdecorations!.(contents(fig[1:2,:]); ticks=false, grid=false)

    # Link and hide y axes
    linkyaxes!(contents(fig.layout)...)
    hideydecorations!.(contents(fig.layout); ticks=false, grid=false)

    # Add labels
    Label(fig[0,1], "Samples"; tellwidth=false, fontsize=20)
    Label(fig[0,2], "Emission"; tellwidth=false, fontsize=20)
    Label(fig[0,3], "Excitation"; tellwidth=false, fontsize=20)

    fig
end