Skip to content

jdwor/mmdt

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
R
 
 
man
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

mmdt

mmdt is an R package for conducting the multi-modal density test, based on the paper “A local group differences test for subject-level multivariate density neuroimaging outcomes” by Dworkin et al. published in Biostatistics. This package creates data structures necessary for applying the method to imaging data, then allows the user to perform the analyses, summarize the results, and create figures for visualization.

Installation

To get the latest development version from GitHub:

devtools::install_github('jdwor/mmdt')

Build Status

Functions

Below is a list of the functions and a description of options available to utilize through the mmdt package. An example of how to run the analysis from beginning to end is given below, in the “Vignette” section.

get.mmdt.obj

This function creates an ‘mmdt object’ from vectors of nifti filenames, subject IDs, and subject group memberships. This information is compiled into a data structure that can be entered into the ‘mmdt’ function to perform the analysis.

get.mmdt.obj(masks, modal1, modal2, modal3=NULL,
             modal4=NULL, modal5=NULL, modal6=NULL,
             ids, groups, parallel=TRUE, cores=2, pb=TRUE)

Arguments

  • masks A vector of class that gives .nii or .nii.gz filenames for subjects’ masks. Masks demarcate which voxels should be included in the analysis, and are coded by TRUE/FALSE or 1/0.
  • modal# Vectors of class that give .nii or .nii.gz filenames for a given imaging modality across subjects. At least two modalities (modal1 and modal2) must be entered. Up to 6 can be included.
  • ids A vector of subject ids. Must be the same length as the filenames in the ‘modal#’ vectors.
  • groups A vector of group membership. Must be two categories, and should be the same length as ‘ids’.
  • parallel A logical value that indicates whether the user’s computer should run the code in parallel.
  • cores If parallel = TRUE, cores is an integer value that indicates how many cores the function should be run on.
  • pb A logical value that indicates whether or not a progress bar will be shown during analysis.

mmdt

This function runs the multi-modal density test (mmdt) using an mmdt object obtained from ‘get.mmdt.obj’.

mmdt(mmdt.obj, mins=NULL, maxs=NULL,
     gridsize=NULL, H=NULL, mc.adjust="BH",
     nperm=500, parallel=TRUE, cores=2, pb=TRUE)

Arguments

  • mmdt.obj An mmdt object obtained using the ‘get.mmdt.obj’ function.
  • mins A vector giving the lower intensity bounds for each modality. If NULL, lower bounds will be set to the minimum observed value for each modality.
  • maxs A vector giving the upper intensity bounds for each modality. If NULL, upper bounds will be set to the maximum observed value for each modality.
  • gridsize A vector giving the number of points along each dimension at which the densities should be evaluated and tested. If NULL, this value defaults to 151x151 for two modalities, 51x51x51 for three, and 21x21x21x21 for four. Must be specified manually when analyzing 4-6 modalities.
  • H The bandwidth matrix used for kernel density estimation. If NULL, a plug-in bandwidth estimator is used.
  • mc.adjust A character vector giving the multiple comparison adjustments to use. Default is “BH”, which controls FDR using the Benjamini-Hochberg procedure. The additional options are: “BY”, which controls FDR using the Benjamini-Yekutieli procedure, “maxt”, which controls FWER using max-t correction, and “tfce”, which controls FWER using threshold-free cluster enhancement. Both of the latter options use permutation to determine significance.
  • nperm If mc.adjust contains either ‘maxt’ or ’tfce, this is an integer value that gives the number of permutations desired to estimate the null distribution.
  • parallel A logical value that indicates whether the user’s computer should run the code in parallel.
  • cores If parallel = TRUE, cores is an integer value that indicates how many cores the function should be run on.
  • pb A logical value that indicates whether or not a progress bar will be shown during analysis.

summarize.mmdt

This function outputs a summary of the mmdt output, printing whether or not there were significant differences after adjustment for multiple comparisons, and giving the approximate locations of differences within the density space.

summarize.mmdt(mmdt.results)

Arguments

  • mmdt.results An object resulting from the ‘mmdt’ command.

fig.mmdt

This function creates visualizations of the mmdt results (either a t-statistic map or a significance map).

fig.mmdt(mmdt.results, type="significance", 
         mc.adjust="BH", coords=c(NA,NA))

Arguments

  • mmdt.results An object resulting from the ‘mmdt’ command.
  • type Type of image to be produced. Can be “t-statistic” or “significance”. Default is “significance”.
  • mc.adjust If type=“significance”, this states which adjustment method to use for visualization.
  • coords If more than two modalities were used to create ‘mmdt.results’ object, this gives a vector of length d [e.g., c(NA, NA, 3.25) for d=3] giving the coordinates at which the plane should be visualized. Entries should be “NA” for the two modalities to be plotted along the x and y axes, and other entries should give the value along the each other dimensions at which the results should be visualized.

mmdt.to.brain

This function maps mmdt results back onto subjects’ brain image domains for visualization and exploration purposes.

mmdt.to.brain(mmdt.results, type="t-statistic", mc.adjust="BH",
              mask, modal1, modal2, modal3=NULL, modal4=NULL,
              modal5=NULL, modal6=NULL)

Arguments

  • mmdt.results An object resulting from the ‘mmdt’ command.
  • type Type of image to be produced. Can be “t-statistic” or “significance”. Default is “significance”.
  • mc.adjust If type=“significance”, this states which adjustment method to use to determine significance.
  • mask A string that gives a .nii or .nii.gz filename for the given subject’s mask. Masks will demarcate which voxels will be included, should be coded by TRUE/FALSE or 1/0, and should be the same as the masks used to conduct the mmdt analyses.
  • modal# Strings that give a .nii or .nii.gz filename for a subject’s given imaging modality. At least two modalities (modal1 and modal2) must be entered. Up to 6 can be included. The same modalities used in the mmdt analyses should be entered here, in the same order.

Vignette

The following gives a simple example of how the functions provided in this package should be used to conduct and summarize the multi-modal density test.

First, an data object should be created using the mmdt.obj function. In this example, we have four subjects, with IDs from 1 to 4. The first two subjects are in group 1, and the last two subjects are in group 2. We can create the mmdt.obj object as follows:

masks = c("mask01.nii", "mask02.nii", "mask03.nii", "mask04.nii")
t1s = c("t101.nii", "t102.nii", "t103.nii", "t104.nii")
flairs = c("flair01.nii", "flair02.nii", "flair03.nii", "flair04.nii")
ids = c(1, 2, 3, 4)
groups = c(1, 1, 2, 2)

mmdt.obj = get.mmdt.obj(masks = masks, modal1 = t1s, modal2 = flairs,
                        ids = ids, groups = groups)

Once we have the mmdt.obj object, we can carry out the analysis using the mmdt function. Here, we will use the defaults for several values, and will therefore not enter them in the function call. We will use the defaults for 1) mins and maxs, which will perform the test over the full space of voxel intensity values, 2) gridsize, which uses the a 151x151 grid for two modalities, and 3) the KDE bandwidth H, which uses a plug-in estimator for each subject.

For the values that we will enter manually, we will correct for multiple comparisons using both Benjamini-Hochberg (for FDR control), and max-t correction with 500 permutations (for FWER control). We will also run the function in parallel, on four cores, with a progress bar.

mmdt.results = mmdt(mmdt.obj, mc.adjust = c("BH", "maxt"), nperm = 500, 
                    parallel = TRUE, cores = 4, pb = TRUE)

Once we have the ‘mmdt.results’ object in hand, we can summarize the results using the summarize.mmdt function. Here, we simply call the function with the ‘mmdt.results’ object, and it prints our results to the console.

summarize.mmdt(mmdt.results)

We can then create vizualization of these results using the fig.mmdt function. We will first visualize the t-statistic map, and then the significance map with Benjamini-Hochberg correction. Since we only used two modalities (T1 and FLAIR), we do not need to enter anything for the ‘coords’ parameter.

tfig = fig.mmdt(results, type = "t-statistic")
tfig

sfig = fig.mmdt(results, type = "significance", mc.adjust = "BH")
sfig

Finally, if we want to manually check where the relevant voxel intensity profiles tend to be located in the brain, we can use the mmdt.to.brain function to map the t-statistics or significance values back onto the subjects’ brain masks used to run the analysis. We would do this one at a time for each subject, and the example below shows how we would do this for Subject 1. First, we will create a nifti image in which voxels are assigned the t-statistic of their location in the density space, and then we will create a nifti image in which voxels are labeled if they are located at a region of the density space in which there was a significant group difference.

tstat.mask.s1 = mmdt.to.brain(mmdt.results, type = "t-statistic",
                              mask = "mask01.nii", modal1 = "t101.nii",
                              modal2 = "flair01.nii")
writeNifti(tstat.mask.s1, file="mmdt.tstat.01.nii.gz")

sig.mask.s1 = mmdt.to.brain(mmdt.results, type = "significance",
                            mask = "mask01.nii", modal1 = "t101.nii",
                            modal2 = "flair01.nii")
writeNifti(sig.mask.s1, file="mmdt.sig.01.nii.gz")

There you have it! Feel free to reach out to jordan.dworkin [at] nyspi [dot] columbia [dot] edu if you have any questions about the package.

About

An R package to implement the Multi-Modal Density Test (MMDT; Dworkin et al. 2020)

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages