Version 0.1.0

.Rbuildignore

@@ -1,2 +1,6 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
+^\.github$

.Rproj.user/6A9A55E1/pcs/files-pane.pper

@@ -5,5 +5,5 @@
             "ascending": true
         }
     ],
-    "path": "~/Desktop/WHIMs/R"
+    "path": "~/Desktop/WHIMs"
 }

.Rproj.user/6A9A55E1/pcs/source-pane.pper

@@ -1,3 +1,3 @@
 {
-    "activeTab": 1
+    "activeTab": 3
 }

.Rproj.user/6A9A55E1/pcs/windowlayoutstate.pper

@@ -1,14 +1,14 @@
 {
     "left": {
-        "splitterpos": 125,
+        "splitterpos": 176,
         "topwindowstate": "NORMAL",
-        "panelheight": 663,
-        "windowheight": 701
+        "panelheight": 682,
+        "windowheight": 720
     },
     "right": {
-        "splitterpos": 272,
+        "splitterpos": 204,
         "topwindowstate": "NORMAL",
-        "panelheight": 663,
-        "windowheight": 701
+        "panelheight": 682,
+        "windowheight": 720
     }
 }

.Rproj.user/6A9A55E1/pcs/workbench-pane.pper

@@ -1,5 +1,5 @@
 {
-    "TabSet1": 0,
+    "TabSet1": 4,
     "TabSet2": 0,
     "TabZoom": {}
 }

.github/.gitignore

@@ -0,0 +1 @@
+*.html

.github/workflows/pkgdown.yaml

@@ -0,0 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown.yaml
+
+permissions: read-all
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/[email protected]
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -2,3 +2,5 @@
 .Rhistory
 .RData
 .Ruserdata
+inst/doc
+docs

DESCRIPTION

@@ -1,19 +1,37 @@
 Package: whims
 Type: Package
-Title: What the Package Does (Title Case)
+Title: NeuroPartitioner: An R Package for ROI-Based Transformation of Neuroimages into High-Dimensional Data Frames
 Version: 0.1.0
 Authors@R: c(
-    person("Jinyao", "Tian", , "[email protected]", role = "aut")
+    person("Joshua", "Milstein", email = "[email protected]", role = c("aut")),
+    person("Jinyao", "Tian", email = "[email protected]", role = c("aut", "cre"))
   )
-Maintainer: Jinyao Tian <[email protected]>
-Description: More about what it does (maybe more than one line)
-    Use four spaces when indenting paragraphs within the Description.
+Maintainer: Jinyao Tian <[email protected]>, Joshua Milstein <[email protected]>
+Description: NeuroPartitioner is an advanced R package designed to convert NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames, focusing on region of interest (ROI) based processing. This package incorporates a key algorithm called ‘partition’, which offers a fast, flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while being interpretable, as each maps uniquely to one variable in the reduced dataset. The ‘partition’ algorithm, detailed in Millstein et al. (2020), allows for customization in variable selection, measurement of information loss, and data reduction methods. NeuroPartitioner is indispensable for researchers requiring efficient, accurate preparation of neuroimaging data for detailed statistical analysis and machine learning applications, enhancing the interpretability and utility of neuroimaging studies.
 License: MIT + file LICENSE
-Encoding: UTF-8
-LazyData: true
-
 Imports:
-    Partition (>= version_number)
-
+    Rcpp,
+    devtools,
+    partition,
+    dplyr,
+    ggplot2,
+    neurobase,
+    purrr,
+    tibble,
+    reshape2,
+    parallel,
+    roxygen2,
+    DT
+SystemRequirements: FSL (FMRIB Software Library, available at https://fsl.fmrib.ox.ac.uk/fsl/docs/#/install/index)
 LinkingTo: 
     Rcpp
+Encoding: UTF-8
+Language: en-US
+LazyData: true
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.3.2
+Suggests: 
+    knitr,
+    rmarkdown
+VignetteBuilder: knitr
+URL: https://jtian123.github.io/WHIMs/

NAMESPACE

@@ -1 +1,22 @@
-exportPattern("^[[:alpha:]]+")
+# Generated by roxygen2: do not edit by hand
+
+export(eve_Fl)
+export(eve_T1)
+export(run_partition_pipeline)
+export(suppar)
+import(R6)
+importFrom(EveTemplate,getEvePath)
+importFrom(Rcpp,sourceCpp)
+importFrom(dplyr,"%>%")
+importFrom(extrantsr,bias_correct)
+importFrom(extrantsr,fslbet_robust)
+importFrom(fslr,fast)
+importFrom(fslr,fsl_biascorrect)
+importFrom(fslr,fslbet)
+importFrom(fslr,fslreorient2std)
+importFrom(fslr,fslstats)
+importFrom(neurobase,readnii)
+importFrom(neurobase,writenii)
+importFrom(oro.nifti,img_data)
+importFrom(oro.nifti,voxres)
+useDynLib(whims, .registration = TRUE)

R/PartitionPipeline.R

@@ -0,0 +1,125 @@
+#Purpose: Make an object for this pipeline, to streamline them
+#' @import R6
+PartitionPipeline <- R6Class("PartitionPipeline",
+                           public = list(
+                             tind = NULL,
+                             nfl = NULL,
+                             main_dir = NULL,
+                             tissue_type = NULL,
+                             outp_volume = TRUE,
+                             ICC_thresh_vec = NULL,
+                             suppar_thresh_vec = seq(0.7, 1, 0.01),
+                             B = 2000,
+                             roi = NULL,
+                             num_cores = NULL,
+                             # Constructor
+                             initialize = function(tind = NULL, nfl = NULL, main_dir = NULL, tissue_type = NULL,
+                                                   outp_volume = TRUE, ICC_thresh_vec = NULL, suppar_thresh_vec = seq(0.7, 1, 0.01), B = 2000, roi = NULL, num_cores = NULL) {
+                               self$tind <- tind
+                               self$nfl <- nfl
+                               self$main_dir <- main_dir
+                               self$tissue_type <- tissue_type
+                               self$outp_volume <- outp_volume
+                               self$ICC_thresh_vec <- ICC_thresh_vec
+                               self$suppar_thresh_vec <- suppar_thresh_vec
+                               self$B <- B
+                               self$roi <- roi
+                               self$num_cores = num_cores
+                             },
+
+                             # Step1: Process images and extract intensities and tissues
+                             iproc = function() {
+                               cat("Starting image processing for ROI:", self$tind, "\n")
+                               result = iproc(tind = self$tind, nfl = self$nfl, main_dir = self$main_dir, outp_volume = self$outp_volume)
+                               self$roi = result
+                             },
+
+                             # Step2: Apply super partition analysis
+                             supparfun = function() {
+                               cat("Starting applying Super Partition to ROI:", self$roi, "\n")
+                               supparfun(
+                                  tind = self$tind,
+                                  roi = self$roi,
+                                  thresh_vec = self$suppar_thresh_vec,
+                                  B = self$B,
+                                  main_dir = self$main_dir)
+                             },
+
+                             # Step3: Map super partitions to ROI
+                             map_suppar_roi = function() {
+                               map_suppar_roi(roi = self$roi, main_dir = self$main_dir)
+                             },
+
+                             # Step4: Partition intensities based on super partitions using lapply
+                             parfun = function() {
+                               dep_list_path = file.path(self$main_dir, "dep_list", paste0(self$roi,'.rds'))
+                               listes <- readRDS(dep_list_path)
+                               cat("Partitioning intensities for all sublists based on super partitions.\n")
+                               cl <- makeCluster(self$num_cores)
+                               parLapply(cl, listes, function(sub_list) {
+                                 parfun(liste = sub_list,tind = self$tind, thresh_vec = self$ICC_thresh_vec, main_dir = self$main_dir)
+                               })
+                               # Stop the cluster after use
+                               on.exit(stopCluster(cl))
+                             },
+
+                             # Step5: Segment tissue types
+                             tissue_segment = function() {
+                               dep_list_path = file.path(self$main_dir, "dep_list", paste0(self$roi,'.rds'))
+                               listes <- readRDS(dep_list_path)
+                               cat("tissue segmentation for all sublists based on super partitions.\n")
+                               lapply(listes, function(sub_list) {
+                                 tissue_segment(liste = sub_list,thresh_vec = self$ICC_thresh_vec,tind = self$tind, tissue_type =self$tissue_type,
+                                                brain_volume_path = NULL, main_dir = self$main_dir)
+                               })
+                             },
+
+                             # Step6: Combine by tissue type for each threshold and roi
+                             Cmb_tissue_type = function(){
+                               lapply(as.list(self$ICC_thresh_vec), function(x){
+                                 Cmb_tissue_type(thresh = x, roi = self$roi, tissue_type = self$tissue_type, main_dir = self$main_dir)
+                               })
+                             },
+
+                             # Step7: Process independent variables from Super-Partition
+                             process_indep_variables = function(){
+                               indep_list_path = file.path(self$main_dir, "indep_list", paste0(self$roi,'.rds'))
+                               indep_list <- readRDS(indep_list_path)
+                               process_indep_variables(indep_list = indep_list, tissue_type = self$tissue_type, tind = self$tind,
+                                                       roi = self$roi, main_dir = self$main_dir)
+                             },
+
+                             # Step8: Combine independent variables with reduced variables by tissue type by roi
+                             Cmb_indep_with_dep = function(){
+                               lapply(as.list(self$ICC_thresh_vec), function(x){
+                                 Cmb_indep_with_dep(thresh = x, roi = self$roi, tissue_type = self$tissue_type, main_dir = self$main_dir)
+                               })
+                             }
+                           )
+)
+
+#test
+# pipeline <- PartitionPipeline$new(tind = 5, nfl = list.files('/Users/jinyaotian/Downloads/pipeline_test/eve_t1', full.names = TRUE),
+#                                   main_dir = "/Users/jinyaotian/Downloads/pipeline_test",
+#                                   tissue_type = 2,
+#                                   ICC_thresh_vec = c(0.8, 0.9),
+#                                   roi = "inferior_frontal_gyrus_left",
+#                                   num_cores = 5)
+#
+# # Step 1
+# pipeline$iproc()
+# # Step 2
+# pipeline$supparfun()
+# # Step 3
+# pipeline$map_suppar_roi()
+# # Step 4
+# pipeline$parfun()
+# # Step 5
+# pipeline$tissue_segment()
+# # Step 6
+# pipeline$Cmb_tissue_type()
+# # Step 7
+# pipeline$process_indep_variables()
+# # Step 8
+# pipeline$Cmb_indep_with_dep()
+