SSAM (Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation)

This repository contains the ongoing development of SSAM, including bugfixes, maintained by the Computational Omics Laboratory at Pusan National University (https://pnucolab.com).

If you are looking for the published version in 2021, please refer to https://github.com/HiDiHlabs/ssam.

Prerequisites

Currently SSAM was only tested with Python 3 in Linux environment. For details, please follow the instructions here: https://ssam.readthedocs.io/en/release/tldr.html#installation

Install

https://ssam.readthedocs.io/en/release/tldr.html#installation

Documentation

https://ssam.readthedocs.io/

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/.

Contents

• Quick start / tldr page
  • Installation
  • Example Data
  • Create SSAM dataset and vector field
  • Creating the de novo cell map
  • Creating the tissue domain map
• Tutorial: Spatial gene expression analysis with SSAM
  • Installation
    • A step-by-step guide
    • (Optional) Using sctransform for normalization
    • SSAM’s source code
  • Data Preparation
    • Download VISp data
    • Load data into python
    • Transform Data
    • Create the SSAMDataset and SSAMAnalysis objects
  • Creating the vector field
    • Prepare data for KDE
    • Run KDE
    • Local maxima search and normalization
    • Visualization
    • Normalization
  • SSAM de novo analysis
    • Clustering of expression vectors
    • Cluster annotation and diagnostics
    • Visualisng the clusters
    • Cell type map
  • SSAM guided analysis
    • Single cell RNA sequencing data
    • Cell-type gene expression signatures
    • Generate a guided cell-type map
  • Clustering Local L-1 Maxima
    • Removing outliers
  • Diagnostic plots
    • Example 1: a large cluster that can be easily annotated
    • Example 2: a large cluster that cannot be easily annotated
    • Example 3: a small cluster that is good
    • Example 4: a small cluster that is questionable
  • Cluster annotation
  • Visualisation of 2D gene expression embeddings (t-SNE and UMAP)
    • Running t-SNE
    • Running UMAP
    • Plotting embeddings
  • Identifying tissue domains
    • Perform circular window sampling
    • Clustering domain signatures
    • Visualizing identified domains
    • Post-processing the identified domains
    • The final plot
  • Cell-type composition analysis in tissue domains
    • Performing the cell-type composition analysis
    • Plotting the composition of each domain
    • Plotting the composition of the entire tissue
  • Experimental features
  • Cell-type classification using Adversarial Autoencoders
    • Mapping cell types using an adversarial autoencoder
  • Segmenting the SSAM cell type map
• Module contents

Indices and tables

• Index