SSAM (Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation)¶
Author: Jeongbin Park (jeongbin.park@charite.de)1,2 and Wonyl Choi (wonyl@bu.edu)3
1Digital Health Center, Berlin Institute of Health (BIH) and Charité – Universitätsmedizin, Berlin, Germany; 2Faculty of Biosciences, Heidelberg University, Heidelberg, Germany; 3Department of Computer Science, Boston University, Boston, the United States of America
(Not referring this :laughing:: https://en.wikipedia.org/wiki/Ssam)
This project was done under supervision of Dr. Naveed Ishaque (naveed.ishaque@charite.de) and Prof. Roland Eils (roland.eils@charite.de), and in collaboration with the SpaceTx consortium and the Human Cell Atlas project.
Please also check our example Jupyter notebooks here: https://github.com/eilslabs/ssam_example
Prerequisites¶
Currently SSAM was only tested with Python 3 in Linux environment. In addition to this package, SSAM requires a local R installation with pre-installed packages feather
and sctransform
. For details, please follow the instructions here: https://ssam.readthedocs.io/en/release/userguide/01-tldr.html#installation
Documentation¶
Citations¶
Jeongbin Park, Wonyl Choi, Sebastian Tiesmeyer, Brian Long, Lars E. Borm, Emma Garren, Thuc Nghi Nguyen, Bosiljka Tasic, Simone Codeluppi, Tobias Graf, Matthias Schlesner, Oliver Stegle, Roland Eils & Naveed Ishaque. “Cell segmentation-free inference of cell types from in situ transcriptomics data.” Nature Communications 12, 3545 (2021).
License¶
Copyright (C) 2018 Jeongbin Park and Wonyl Choi
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.
- Spatial gene expression analysis with SSAM
- quick start / tldr page
- Installation
- Data Preparation
- Creating the vector field
- The shape of the kernel
- Kernel bandwidth
- Input masks
- SSAM guided analysis
- Thresholding the guided cell-type map
- SSAM de novo analysis
- Filtering local maxima
- Filtering “stray” local maxima using k-nearest neighbour density
- Clustering Local L-1 Maxima
- Diagnostic plots
- Cluster annotation
- Thresholding the de-novo cell-type map
- Visualisation of 2D gene expression embeddings (t-SNE and UMAP)
- Identifying tissue domains
- Cell-type composition analysis in tissue domains
- Experimental features
- Cell-type classification using Adversarial Autoencoders
- Segmenting the SSAM cell type map
- Module contents