Apr 18, 2024
Version 2
CODA: shorthand for calling functions | HuBMAP | JHU-TMC V.2
- Kyu Sang Han1,
- Pei-Hsun Wu1,
- Joel Sunshine2,
- Ashley Kiemen2,
- Sashank Reddy2,
- Denis Wirtz1,2
- 1Johns Hopkins University;
- 2Johns Hopkins Medicine
Protocol Citation: Kyu Sang Han, Pei-Hsun Wu, Joel Sunshine, Ashley Kiemen, Sashank Reddy, Denis Wirtz 2024. CODA: shorthand for calling functions | HuBMAP | JHU-TMC. protocols.io https://dx.doi.org/10.17504/protocols.io.5qpvoknkbl4o/v2Version created by Kyu Sang Han
Manuscript citation:
A.M. Braxton, A.L. Kiemen, M.P. Grahn, A. Forjaz, J. Parksong, J.M. Babu, J. Lai, L. Zheng, N. Niknafs, L. Jiang, H. Cheng, Q. Song, R. Reichel, S. Graham, A.I. Damanakis, C.G. Fischer, S. Mou, C. Metz, J. Granger, X.-D. Liu, N. Bachmann, Y. Zhu, Y.Z. Liu, C. Almagro-Pérez, A.C. Jiang, J. Yoo, B. Kim, S. Du, E. Foster, J.Y. Hsu, P.A. Rivera, L.C. Chu, D. Liu, E.K. Fishman, A. Yuille, N.J. Roberts, E.D. Thompson, R.B. Scharpf, T.C. Cornish, Y. Jiao, R. Karchin, R.H. Hruban, P.-H. Wu, D. Wirtz, and L.D. Wood, “3D genomic mapping reveals multifocality of human pancreatic precancers”, Nature (2024)
A.L. Kiemen, A. Forjaz, R. Sousa, K. Sang Han, R.H. Hruban, L.D. Wood, P.H. Wu, and D. Wirtz, “High-resolution 3D printing of pancreatic ductal microanatomy enabled by serial histology”, Advanced Materials Technologies 9, 2301837 (2024)
T. Yoshizawa, J. W. Lee, S.-M. Hong, D.J. Jung, M. Noe, W. Sbijewski, A. Kiemen, P.H, Wu, D. Wirtz, R.H. Hruban, L.D. Wood, and K. Oshima. “Three-dimensional analysis of ductular reactions and their correlation with liver regeneration and fibrosis”, Virchows Archiv (2023).
A.L. Kiemen, A.I. Damanakis, A.M. Braxton, J. He, D. Laheru, E.K. Fishman, P. Chames, C. Almagro Perez, P.-H. Wu, D. Wirtz, L.D. Wood, and R. Hruban, “Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer”, Med 4, 75-91 (2023)
A. Kiemen, Y. Choi, A. Braxton, C. Almagro Perez, S. Graham, M. Grahm, N., N. Roberts, L. Wood, P. Wu, R. Hruban, and D. Wirtz, “Intraparenchymal metastases as a cause for local recurrence of pancreatic cancer”, Histopathology 82: 504-506 (2022)
A.L. Kiemen, A.M. Braxton, M.P. Grahn, K.S. Han, J.M. Babu, R. Reichel, A.C. Jiang, B. Kim, J. Hsu, F. Amoa, S. Reddy, S.-M. Hong, T.C. Cornish, E.D. Thompson, P. Huang, L.D. Wood, R.H. Hruban, D. Wirtz and P.H. Wu, “CODA: quantitative 3D reconstruction of large tissues at cellular resolution”, Nature Methods 19: 1490-1499 (2022)
K.S.Han, I. Sander, J. Kumer, E. Resnick, C. Booth, B. Starich, J. Walston, A.L. Kiemen, S. Reddy, C. Joshu, J. Sunshine, D. Wirtz, P.-H. Wu "The microanatomy of human skin in aging." bioRxiv (2024): 2024-04.
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: April 18, 2024
Last Modified: April 18, 2024
Protocol Integer ID: 98447
Keywords: CODA, machinelearning, deeplearning
Funders Acknowledgement:
Institute of Arthritis and Musculoskeletal and Skin Diseases
Grant ID: U54AR081774
National Cancer Institute
Grant ID: U54CA143868
Abstract
To downsample ndpi or svs images to 10x, 5x, and 1x tifs, use this function:
create_downsampled_tif_images
or try Openslide in python
To calculate registration on the low resolution (1x) images
1. calculate the tissue area and background pixels using this function:
calculate_tissue_ws
2. calculate the registration transforms:
calculate_image_registration
To build a 3D tissue volume using sematic segmentation:
1. generate manual annotations in Aperio imagescope
2. apply the deep learning function to train a model and segment the high resolution (5x or 10x) images:
train_image_segmentation
To apply the registration to segmented images:
apply_image_registration
To build a 3D tissue matrix from registered, classified images:
build_tissue_volume
To build a 3D cell volume containing nuclear coordinates:
1. Build a mosaic image containing regions of many whole-slide images for cell detection optimization:
make_cell_detection_mosaic
2. Manually annotate the mosaic image to get the ‘ground-truth’ number of cell nuclei:
manual_cell_count
3. Determine cell detection parameters using the manual annotations on the mosaic image:
get_nuclear_detection_parameters
4. Deconvolve the high-resolution (5x or 10x) H&E images before applying the cell detection algorithm:
deconvolve_histological_images
5. Detect cells on the hematoxylin channel of the high-resolution images:
cell_detection
6. Apply the registration to the cell coordinates:
register_cell_coordinates
7. Build a 3D cell coordinate matrix corresponding to the 3D tissue matrix:
build_cell_volume
shorthand in the abstract
shorthand in the abstract
Use the above shorthand to facilitate your workflow by using it as a "cheat sheet"