In DCAN-Labs/MarginalModelCIFTI: MarginalModelCifti constructs and tests marginal models for surface and volume data.

knitr::opts_chunk$set(echo = TRUE)

To install MarginalModelCifti, one should do the following:

1) make a directory for the MarginalModelCifti package mkdir ~/MarginalModelCifti 2) enter the directory cd ~/MarginalModelCifti 3) clone the MarginalModelCifti repository git clone https://gitlab.com/Fair_lab/marginalmodelcifti.git ./ 4) return to your initial home directory cd .. 5) Type R 6) After a prompt appears, make sure devtools is installed by typing install.packages("devtools") 7) Load devtools library(devtools) 8) install the MarginalModelCifti package install("MarginalModelCifti/")

NOTE: You may also want to clone the SurfConnectivity package, in case you do not have access to it. a) make a directory for SurfConnectivity mkdir ~/SurfConnectivity b) go into SurfConnectivity folder cd ~/SurfConnectivity c) clone the SurfConnectivity repository here git clone https://gitlab.com/Fair_lab/surfconnectivity.git ./

NOTE: You may also want to clone the CommunityChisquared package, in case you do not have access to it. i) make a directory for CommunityChisquared mkdir ~/CommunityChiSquaredAnalysis ii) go into CommunityChisquared folder cd ~/CommunityChiSquaredAnalysis iii) clone the CommunityChiSquared repostory here git clone https://github.com/DCAN-Labs/CommunityChiSquaredAnalysis.git ./

Call the MarginalModelCifti library -- if this errors you will need to install it using devtools

library(MarginalModelCifti)

Set your project folder, which is where you plan to run the analysis, then go to the folder

projectsfolder="/mnt/rose/shared/projects/ABCD/avg_pconn_maker/"
setwd(projectsfolder)
getwd()

Now declare the needed variables to run a marginal model

Set the below variable to your external (i.e. non-imaging) dataset. The dataset should be a csv with headers representing the variables.

external_df="/mnt/rose/shared/projects/ABCD/avg_pconn_maker/cordova_analysis_margmod_pcs/gp1_10min_pconn.csv"

Set the below variable to a single column textfile, where each row contains a path to each participant's metric file.

The metric file should be ordered in the same order as the external_df file

concfile="/mnt/rose/shared/projects/ABCD/avg_pconn_maker/cordova_analysis_margmod_pcs/group1_10min.conc"

Set the below variable structtype to the type of brain structure (i.e. "surface", "volume", or "pconn") for the metric file.

This is needed for the cluster detection to work properly.

structtype="pconn"

If the structtype is set to "surface", set the below variable structfile to the corresponding surface file.

This is needed for surface-based cluster detection. The value can be set to NULL for volumes.

structfile=NULL

If the structtype is set to "surface" or "pconn", set the below variable matlab_path to the matlab2016b compiler.

matlab_path="/mnt/max/shared/code/external/utilities/Matlab2016bRuntime/v91"

If the structtype is set to "surface", set the below variable to the SurfConnectivity script

surf_command="/mnt/max/shared/projects/FAIR_users/Feczko/code_in_dev/SurfConnectivity/"

Specify the model you want to run in the below variable notation.

Use the function formula to make a formal notation within R.

The predicted variable will always be y representing the values in the metric file.

The predictor variables should use the column names within the external_df csv header.

notation = formula(y~pc2_new)

Set the below variable corstr to the correlation structure of the cases. Usually this should just be "independence".

corstr="independence"

Set the below variable family_dist to the appropriate distribution of your data, "gaussian" is the default

family_dist="gaussian"

Set the below variable dist_type to the distribution used for wild bootstrapping.

Acceptable values are "radenbacher", "webb4", "webb6", and "mammen".

dist_type="radenbacher"

set the below variable z_thresh to the z statistic threshold used for determining observed and permuted cluster sizes

z_thresh = 2.3

Set the below variable nboot to the number of wild bootstraps to perform.

The precision of the p value is equal to 1/nboot.

For example, if 1000 bootstraps are selected, the smallest p value can be 0.001.

WARNING, this part can be slow.

nboot=4

Set the below variable p_thresh to the p value threshold for assessing significant clusters.

Currently this has no functionality

p_thresh=0.05

Set the below variable sigtype to determine how to perform multiple comparison correction.

Acceptable values are "point" (FWE for voxels), "enrichment", and "cluster".

sigtype="enrichment"

Set the below variable id_subjects to the column header containing the subject id in the external_df file.

id_subjects="subjectkey"

Set the below variable output_directory to where you want to save your outputs

output_directory="/mnt/rose/shared/projects/ABCD/avg_pconn_maker/cordova_analysis_margmod_pcs/pc2_gp1_test"

Set the below variable ncores to how many CPUs to run permutation testing in parallel

ncores=4

Set the below variable zcor to a custom covariance matrix to denote participant similarity (e.g. a kinship or site matrix)

zcor=NULL

The below variable fastSwE enables the fast sandwich estimator.

Generally, it is recommended to set this to true to reduce overhead computations.

fastSwE=TRUE

The below variable handles sample size adjustments when dealing with small sample sizes.

Acceptable transforms are currently "HC2" and "HC3". "HC2" and "HC3".

Correction applied as the homogenous version, per Bryan Guillaume and Tom Nichols's work.

adjustment=NULL

Set the below variable wave to a csv file that denotes how subjects should be grouped and nested

wave = "/mnt/rose/shared/projects/ABCD/avg_pconn_maker/cordova_analysis_margmod_pcs/gp1_marg_nested.csv"

The below variable will normalize the external_df data per variable if set to true

norm_external_data=TRUE

The below variable will normalize the imaging data per datapoint if set to true

norm_internal_data=TRUE

The below variable will output marginal values as statistical maps if set to true

marginal_outputs = FALSE

The below variable represents a numeric matrix for drawing the map, only useful for marginal outputs

marginal_matrix = NULL

The below variable is a string representing the path to the enrichment repository and compiled code

enrichment_path = "/mnt/max/shared/projects/FAIR_users/Feczko/code_in_dev/CommunityChisquaredAnalysis/"

The below variable is a string that represents the path to a csv containing modules

modules = "/mnt/max/shared/projects/FAIR_users/Feczko/code_in_dev/CommunityChisquaredAnalysis/gordon_modules.csv"

The below variable is a string that represents the path to the workbench command

wb_command = "/usr/local/bin/wb_command"

With all the variables determined, you can now run the MarginalModel package using the ConstructMarginalModel command

all_maps  <- ConstructMarginalModel(external_df=external_df,concfile=concfile,structtype=structtype,structfile=structfile,matlab_path=matlab_path,surf_command=surf_command,wave=wave,notation=notation,zcor=zcor,corstr=corstr,family_dist=family_dist,dist_type=dist_type,z_thresh=z_thresh,nboot=nboot,p_thresh=p_thresh,sigtype=sigtype,id_subjects=id_subjects,output_directory=output_directory,ncores=ncores,fastSwE=fastSwE,adjustment=adjustment,norm_external_data=norm_external_data,norm_internal_data=norm_internal_data,marginal_outputs=marginal_outputs,marginal_matrix=marginal_matrix,enrichment_path=enrichment_path,modules=modules,wb_command=wb_command)

DCAN-Labs/MarginalModelCIFTI documentation built on Nov. 30, 2021, 3:40 p.m.