paste-bio 1.0.1

A computational method to align and integrate spatial transcriptomics experiments.

PASTE

PASTE is a computational method that leverages both gene expression similarity and spatial distances between spots align and integrate spatial transcriptomics data. In particular, there are two methods:

1. pairwise_align: align spots across pairwise ST layers.
2. center_align: integrate multiple ST layers into one center layer.

You can read our preprint here.

PASTE is actively being worked on with future updates coming.

Dependencies

To run PASTE, you will need the following Python packages:

1. POT: Python Optimal Transport (https://PythonOT.github.io/)
2. NetworkX (https://networkx.org/)
3. Numpy
4. Pandas
5. scipy.spatial
6. sklearn.preprocessing

Installation

The easiest way is to install PASTE on pypi: https://pypi.org/project/paste-bio/.

pip install paste-bio

Check out Tutorial.ipynb for an example of how to use PASTE.

Or you can clone the respository and run from command line (see below).

Command Line

We provide the option of running PASTE from the command line.

First, clone the repository:

git clone https://github.com/raphael-group/paste.git

Sample execution: python paste-cmd-line.py -m pairwise -f file1.csv file2.csv file3.csv

Note: pairwise will return pairwise alignment between each consecutive pair of files (e.g. [file1,file2], [file2,file3]).

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
-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_layer	Specify which file is also the intial layer in center_align	(int) 1
-t	threshold	Convergence threshold for center_align	(float) 0.001

Input files are .csv files of the form:

       	'gene_a'  'gene_b'
'2x5'	   0         9      
'2x7'	   2         6      

Where the columns indexes are gene names (str), row indexes are spatial coordinates (str), and entries are gene counts (int). In particular, row indexes are of the form AxB where A and B are floats.

pairwise_align outputs a (.csv) file containing mapping of spots between each consecutive pair of layers. The rows correspond to spots of the first layer, and cols the second.

center_align outputs two files containing the low dimensional representation (NMF decomposition) of the center layer gene expression, and files containing a mapping of spots between the center layer (rows) to each input layer (cols).

Sample Dataset

Added sample spatial transcriptomics dataset consisting of four breast cancer layers 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).