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Mass Univariate Regression Analysis (MUR)

The MUR package implements R functions for mass univariate analysis of three-dimensional phenotypes.

Installation

Currently this package is only available on GitHub and requires R (>= 3.3.2). For installation, the easiest way is using devtools. If you don’t have devtools installed, please start by typing:

install.packages("devtools")
library(devtools)

Then, to install the package just use install_githubfunction.

install_github("Marjola89/MUR")

Install Dependencies

The following packages are not available on CRAN so they may need to be installed seperately.

  • multtest 
    if (!require("BiocManager", quietly = TRUE))
 install.packages("BiocManager")

BiocManager::install("multtest")
  • mutools3D 
    install.packages("devtools")
library(devtools)
install_github("UK-Digital-Heart-Project/mutools3D", build_vignettes = TRUE)

Code

Produce Matrices

First prepare matrices X and Y in .rds files e.g S2S_kidney_left.rds for the model that will be used in Mass Univariate Analysis code.

model <- loadingdata(fileClinicalDataPath, folderImagingDataPath, indVar, nPoints, organ)

Input parameters:

  • fileClinicalDataPath: Set the path of the file containing the clinical data of the subjects for all the models e.g. "/home/yourname/clinicaldata-path/data/ClinicalData.csv".
  • folderImagingDataPath: Set the path of the folders storing the image data e.g. "/home/yourname/folder-path/".
  • phenotype: Define the imaging phenotype e.g. 1 for S2S, 2 for Curvature.
  • nPoints: Set the number of points (vertices) in the mesh, this will change for each organ e.g. for kidney left: 4380.
  • organ: Set organ segmentation e.g. "liver", "spleen", "kidney_left" etc.

Output parameters:

  • model: a list including the design matrix X, the imaging matrix Y, the matrix with x-coordinates X_coordinate, the matrix with y-coordinates Y_coordinateand the matrix with z-coordinates Z_coordinate.

Notes:

  • It is essential to saved the output as:
phenoType <- c(1, 2)
# 1 = S2S, 2 = Curvature

phenotypeNames <- c("S2S","Curvature")

for(iM in 1:length(phenotype)){
  model <- loadingdata(fileClinicalDataPath, folderImagingDataPath, phenotype[iM], nPoints, organ)

  X <- model[[1]]
  Y <- model[[2]]
  Xc <- model[[3]]
  Yc <- model[[4]]
  Zc <- model[[5]]

  saveRDS(X, paste(data_dir, phenotypeNames[phenotype[iM]], "_", organ, "_clinicalData.rds", sep = ""))
  saveRDS(Y, paste(data_dir, phenotypeNames[phenotype[iM]], "_", organ, ".rds", sep = ""))
  saveRDS(Xc, paste(data_dir, phenotypeNames[phenotype[iM]], "_", organ, "_Xcoordinate.rds", sep = ""))
  saveRDS(Yc, paste(data_dir, phenotypeNames[phenotype[iM]], "_", organ, "_Ycoordinate.rds", sep = ""))
  saveRDS(Zc, paste(data_dir, phenotypeNames[phenotype[iM]], "_", organ, "_Zcoordinate.rds", sep = ""))

}
  • data_dir: is the path of the data directory were the matrices X and Y for the model will be saved.

Code for computing the NN list and the voronoi area

This is needed for the Mass Univariate Analysis. Usage:

NN_Area <- create_nnlist_area(mesh_Coordinates, dpl)

Input parameters:

  • mesh_Coordinates: The matrix from the 3D model mesh of the template.
  • dpl : A list describing the structure of the dummy points to be added to the data being triangulated. e.g. list(ndx = 1.5, ndy = 1.5). If the argument is NULL then no dummy point is added to the data. ndx: The x-dimension of a rectangular grid; if either ndx or ndy is null, no grid is constructed. ndy: The y-dimension of the aforementioned rectangular grid.

Notes:

  • Alternative way to create NN matrices and area is using computeNNmatrix and computeVertAreas from the R package mutools3D.

Mass univariate regression analysis Code

Computes the associations between the 3D mesh-derived phenotype (e.g. signed distances) and other clinical variables using a linear regression framework.

Run the Mass Univariate Analysis with TFCE using the system's cores.

mur_analysis <- mass_univariate_analysis(inputClinical, Y, A, NNmatrix, mesh_Coordinates, phenotype, organ, scale_range, nPermutations, extract_range, output_dir)

This code is dependent on three functions (~/function/murq.R, ~/function/permFL_fast.R and ~/function/TFCE.R) which are called within the code.

Input parameters:

  • X is the design matrix. Number of rows = number of subjects in the study, number of columns = number of vertices in the atlas. Numerical variable must be normalized to 0-mean and unit-standard deviation. Categorical variables must be coded using dummy coding. The first column should contain the intercept (all 1s).
  • Y is the imaging matrix. Number of rows = N. Number of columns = V.
  • A a V-dimensional vector containing the area associated with a vertex, usually its Voronoi area.
  • NNmatrix Nx2 matrix containing the mesh edges.
  • mesh_Coordinates: The matrix from the 3D model mesh of the template.
  • phenotype: Define the imaging phenotype e.g. "S2S", "Curvature".
  • organ: Set organ segmentation e.g. "liver", "spleen", "kidney_left" etc.
  • nPermutations: Number of permutation testing e.g. 1000.
  • extract_range: Range of the independent variables to extract the beta coefficients and p-values after MUR analysis e.g. c(2, 5:8).
  • output_dir: Set the path of the output directory were the output with the coefficients and p-values for each covariate are saved as a .txt file.

Notes:

  • For more information on how to run the mass_univariate_analysis code see the mutools3D documentation.

Sub-functions for computing the mass univariate regression analysis

  • murq.R for the mass univariate regression analysis across the mesh vertices.

    result <- murq(X, Y, extract)

  • permFL_fast.R for the permutation the data and applying TFCE.

    TFCEresults <- permFL_fast(X, Y, extract, A, NNmatrix, nPermutations, E = 0.5, H = 2)

    Input parameters:

    • X is the design matrix. Number of rows = number of subjects in the study, number of columns = number of vertices in the atlas. Numerical variable must be normalized to 0-mean and unit-standard deviation. Categorical variables must be coded using dummy coding. The first column should contain the intercept (all 1s).
    • Y is the imaging matrix. Number of rows = N. Number of columns = V.
    • extract is an array expressing which covariates in X you want to extract.
    • A a V-dimensional vector containing the area associated with a vertex, usually its Voronoi area.
    • NNmatrix Nx2 matrix containing the mesh edges.
    • nPermutations number of permutations in the permutation test, default is 1000.
    • E is the TFCE parameter, by default fixed to 0.5.
    • H is the TFCE parameter, by default fixed to 2.

    Notes:

    • TFCE.R function is used from the R package mutools3D.
    • murq.R and permFL_fast.R functions are similar to the functions mur.R and permFL.R of the mutools3D package however, they are enhanced to allow process of big datasets.