PASTE is a computational method that leverages both gene expression similarity and spatial distances between spots to align and integrate spatial transcriptomics data. In particular, there are two methods:
pairwise_align: align spots across pairwise slices.center_align: integrate multiple slices into one center slice.
You can read our preprint here.
PASTE is actively being worked on with future updates coming.
As of version 1.1.0, PASTE now runs on AnnData making it very easy to integrate with Scanpy for better downstream analysis. Hooray!
This also means that the old version that uses the STLayer object is now deprecated.
To run PASTE, you will need the following Python packages:
- POT: Python Optimal Transport (https://PythonOT.github.io/)
- Scanpy (https://scanpy.readthedocs.io/en/stable/)
- Numpy
- Pandas
- scipy.spatial
- sklearn.preprocessing
The easiest way is to install PASTE on pypi: https://pypi.org/project/paste-bio/.
pip install paste-bio
Or you can install PASTE on bioconda: https://anaconda.org/bioconda/paste-bio.
conda install -c bioconda paste-bio
Check out Tutorial.ipynb for an example of how to use PASTE.
Alternatively, you can clone the respository and run from command line (see below).
We provide the option of running PASTE from the command line.
First, clone the repository:
git clone https://github.com/raphael-group/paste.git
Next, when providing files, you will need to provide two separate files: the gene expression data followed by spatial data (both as .csv) for the code to initialize one slice object.
Sample execution (based on this repo): python paste-cmd-line.py -m center -f ./sample_data/slice1.csv ./sample_data/slice1_coor.csv ./sample_data/slice2.csv ./sample_data/slice2_coor.csv ./sample_data/slice3.csv ./sample_data/slice3_coor.csv
Note: pairwise will return pairwise alignment between each consecutive pair of slices (e.g. [slice1,slice2], [slice2,slice3]).
| Flag | Name | Description | Default Value |
|---|---|---|---|
| -m | mode | Select either pairwise or center |
(str) pairwise |
| -f | files | Path to data files (.csv) | None |
| -d | direc | Directory to store output files | Current Directory |
| -a | alpha | Alpha parameter for PASTE | (float) 0.1 |
| -c | cost | Expression dissimilarity cost (kl or Euclidean) |
(str) kl |
| -p | n_components | n_components for NMF step in center_align |
(int) 15 |
| -l | lmbda | Lambda parameter in center_align |
(floats) probability vector of length n |
| -i | intial_slice | Specify which file is also the intial slice in center_align |
(int) 1 |
| -t | threshold | Convergence threshold for center_align |
(float) 0.001 |
| -x | coordinates | Output new coordinates (toggle to turn on) | False |
| -w | weights | Weights files of spots in each slice (.csv) | None |
| -s | start | Initial alignments for OT. If not given uses uniform (.csv structure similar to alignment output) | None |
pairwise_align outputs a (.csv) file containing mapping of spots between each consecutive pair of slices. The rows correspond to spots of the first slice, and cols the second.
center_align outputs two files containing the low dimensional representation (NMF decomposition) of the center slice gene expression, and files containing a mapping of spots between the center slice (rows) to each input slice (cols).
Added sample spatial transcriptomics dataset consisting of four breast cancer slice courtesy of:
Ståhl, Patrik & Salmén, Fredrik & Vickovic, Sanja & Lundmark, Anna & Fernandez Navarro, Jose & Magnusson, Jens & Giacomello, Stefania & Asp, Michaela & Westholm, Jakub & Huss, Mikael & Mollbrink, Annelie & Linnarsson, Sten & Codeluppi, Simone & Borg, Åke & Pontén, Fredrik & Costea, Paul & Sahlén, Pelin Akan & Mulder, Jan & Bergmann, Olaf & Frisén, Jonas. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 353. 78-82. 10.1126/science.aaf2403.
Note: Original data is (.tsv), but we converted it to (.csv).