View source: R/databaseCreator.R
databaseCreator | R Documentation |
Processes multiple PET image files matching a specified filename pattern.
Each file is processed using neuroCleaner
, and the results are aggregated
into a unified data frame for functional data analysis. This function serves as a key step
in the neuroSCC
workflow, bridging raw image data and Simultaneous Confidence Corridors (SCC) computation.
databaseCreator(
pattern,
control = TRUE,
useSequentialNumbering = FALSE,
demo = NULL,
quiet = FALSE
)
pattern |
|
control |
|
useSequentialNumbering |
|
demo |
|
quiet |
|
The function performs the following steps
Identifies image files matching the given pattern.
Processes each file using neuroCleaner
, optionally merging demographic data.
Adds a subject identifier column (CN_number
or AD_number
).
Aggregates all results into a single data frame.
If no files are successfully processed, an empty data frame is returned with a warning.
This function is typically followed by matrixCreator
, which converts the output
into a matrix format for functional analysis.
A data.frame
combining processed voxel-level data from all matched files.
Each row represents a voxel (3D pixel). The column structure depends on input
For the control group: CN_number
, z
, x
, y
, pet
For the pathological group: AD_number
, z
, x
, y
, pet
If demographics are included: additional columns PPT
, Group
, Sex
, Age
neuroCleaner
for the underlying image processing function.
matrixCreator
for the next step in the workflow that converts
the database to a matrix format for SCC analysis.
# NOTE: To keep runtime below CRAN limits, this example processes only 1 subject.
# You can expand the pattern to include all subjects for real use.
# Example: Create a database from a single synthetic PET image (control group)
controlPattern <- "^syntheticControl1\\.nii\\.gz$"
databaseControls <- databaseCreator(pattern = controlPattern, control = TRUE, quiet = TRUE)
head(databaseControls)
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