This vignette guides you through the initial data preparation steps necessary to use the neuroSCC package for analyzing neuroimaging data with Simultaneous Confidence Corridors (SCC).
We’ll begin by loading a sample PET neuroimaging file included with the package and inspecting its structure.
niftiFile <- system.file("extdata", "syntheticControl1.nii.gz", package = "neuroSCC")
# Load and clean data using neuroCleaner
petData <- neuroCleaner(niftiFile)
# Inspect the data
head(petData)
#> z x y pet
#> 1 1 1 1 0
#> 2 1 1 2 0
#> 3 1 1 3 0
#> 4 1 1 4 0
#> 5 1 1 5 0
#> 6 1 1 6 0
str(petData)
#> 'data.frame': 902629 obs. of 4 variables:
#> $ z : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ x : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ y : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ pet: int 0 0 0 0 0 0 0 0 0 0 ...Now, let’s demonstrate how to create a structured database using multiple PET files available in the package.
# Create database for control subjects
controlPattern <- "^syntheticControl.*\\.nii.gz$"
databaseControls <- databaseCreator(pattern = controlPattern, control = TRUE, quiet = FALSE)
#> Processing CN_number 1 - File 1 of 2
#> Processing CN_number 2 - File 2 of 2
# Inspect the created database
head(databaseControls)
#> CN_number z x y pet
#> 1 1 1 1 1 0
#> 2 1 1 1 2 0
#> 3 1 1 1 3 0
#> 4 1 1 1 4 0
#> 5 1 1 1 5 0
#> 6 1 1 1 6 0
table(databaseControls$CN_number)
#>
#> 1 2
#> 902629 902629We’ll transform the PET database into a matrix format suitable for SCC analysis.
# Create matrix for Z-slice 35
matrixControls <- matrixCreator(databaseControls, paramZ = 35, quiet = FALSE)
#> Processing Subject 1
#> Processing Subject 2
# Inspect matrix structure
dim(matrixControls)
#> [1] 2 9919
str(matrixControls)
#> num [1:2, 1:9919] 0 0 0 0 0 0 0 0 0 0 ...Normalization adjusts for global intensity differences between subjects.
# Perform mean normalization
normalizedMatrix <- meanNormalization(matrixControls, returnDetails = FALSE)
#>
#> Mean before normalization: 4.345851
#>
#> Normalization completed.Contours from the neuroimage are used to set boundaries for SCC computations.
# Extract contours from sample data
contours <- neuroContour(niftiFile, paramZ = 35, plotResult = TRUE)
# Check contours structure
length(contours)
#> [1] 1
str(contours[[1]])
#> 'data.frame': 345 obs. of 2 variables:
#> $ x: num 9 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 ...
#> $ y: num 43.1 44 45 46 47 48 49 50 51 52 ...The triangulation step requires the external
Triangulation package, currently not on CRAN. Ensure you
have this package installed. If not, install it using:
Conditional example for triangulation:
if (!requireNamespace("Triangulation", quietly = TRUE)) {
cat("Triangulation package is not installed.\nInstall it using: remotes::install_github('FIRST-Data-Lab/Triangulation')\n")
} else {
# Perform triangulation with the first contour
mesh <- Triangulation::TriMesh(n = 15, contours[[1]])
# Inspect mesh
print(mesh[["V"]][1:10, ])
print(mesh[["Tr"]][1:10, ])
}
#> Warning in tri.mesh(X, Y): success: collinearities reduced through jitter
#> Warning in tri.mesh(X, Y): dataset started with 3 colinear points, jitter
#> added!
#> X Y
#> [1,] 13.15333 38.42000
#> [2,] 13.15333 43.47333
#> [3,] 13.15333 48.52667
#> [4,] 13.15333 53.58000
#> [5,] 13.15333 58.63333
#> [6,] 13.15333 63.68667
#> [7,] 18.20667 28.31333
#> [8,] 18.20667 33.36667
#> [9,] 18.20667 38.42000
#> [10,] 18.20667 43.47333
#> node1 node2 node3
#> [1,] 1 542 541
#> [2,] 1 541 540
#> [3,] 1 540 538
#> [4,] 1 538 537
#> [5,] 1 537 536
#> [6,] 1 536 535
#> [7,] 1 535 533
#> [8,] 1 533 532
#> [9,] 1 532 531
#> [10,] 1 531 8You’re now ready to perform Simultaneous Confidence Corridor analyses:
one_group_scc
vignette)two_group_comparison
vignette)one_vs_group
vignette)Feel free to explore these vignettes to continue your analysis journey with neuroSCC.