SMART: SMART (Semi-manual alignment to reference template): a...

In jdknguyen/SMART: SMART (Semi-manual alignment to reference template): a pipeline for whole brain mapping projects

SMART (Semi-manual alignment to reference template): a pipeline for whole brain mapping projects.

Description

This package interfaces with the wholebrain package by Daniel Furth to process whole brain imaging datasets. The pipeline is split into five different parts, with the purpose of each function per part explained below. Note that all functions that are exclusively meant to be used with whole brain datasets will be marked with a (W) next to the function. Unmarked functions can be used on single slice datasets across the brain.

Details

For convention, if a return value is given by a function, the recommended variable name is indicated in italics in the return section of each function's help page. If this return value is a required argument for another function in the pipeline, the the recommended variable name will also be the same name as the argument.

Part 1. Setup pipeline

1. setup_pl() User friendly way to setup parameters for whole or partial brain pipeline analysis.

2. im_sort() A function to sort image paths for imaging datasets.

3. get_savepaths() Generate savepaths and save directories.

Part 2. Alignment (whole brain dataset)

1. choice() (W) User friendly choice game to align internal reference atlas plates.

2. brainmorph() (W) Generate a brain morph plot.

3. interpolate() (W) Interpolate all AP and z numbers for atlas plates in a whole brain project

Part 3. Registration

1. registration2() Console user interface built on top of registration() function from the wholebrain package.

2. regi_loop() Automatically loops through the image registration processs for the imaging dataset.

Part 4. Segmentation and forward warping

1. filter_loop() Loops through reference slices and allows user to check/change filter settings for segmentation or registration throughout the brain.

2. seg_loop() Loop through and segment images in the segmentation channel.

3. clean_duplicates() (W) Duplicate cell count clean up of segmentation output

4. cell_counter() Determines total number of cells segmented, retained, and removed by duplicate cleanup.

5. forward_warp() Perform forward warp loop back onto atlas space using segmentation and registration data.

Part 5. Dataset manipulation and plotting

1. isolate_dataset() Isolates a user-specified subset of the forward warped dataset.

2. get_rois() Get subset of the forward warped dataframe of just regions of interest.

3. get_sunburst() Generate a sunburst plot using a forward warped dataset.

4. get_tree() Create a dataframe of hierarchical region cell count data.

5. glassbrain2() Generate 3D plot of cells with option of plotting or removing glassbrain in the background.

6. get_table() Generates a dataframe showing region acronyms, their full name, hierachical paths, total cell counts, left and right counts, and cell count percentages.

Part 6. Aggregating data from multiple analyses

1. concatenate() Combines datasets from multiple brains.

2. cell_count_compilation() Compiles cell counts from multiple brains.

3. get_groups() Compiles group data from individual brains.

4. voxelize() Generate voxel-based heatmaps from multiple brains.

5. voxel_stats() Run statistical tests on voxel-based heatmaps.

jdknguyen/SMART documentation built on May 30, 2022, 10:51 p.m.