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PASTE

PASTE Overview

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 full paper here.

Additional examples and the code to reproduce the paper's analyses can be found here. Preprocessed datasets used in the paper can be found on zenodo.

Installation

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 try the following example in a notebook or the command line.

Quick Start

To use PASTE we require at least two slices of spatial-omics data (both expression and coordinates) that are in anndata format (i.e. read in by scanpy/squidpy). We have included a breast cancer dataset from [1] in the sample_data folder of this repo that we will use as an example below to show how to use PASTE.

import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
import scanpy as sc
import paste as pst

# Load Slices
data_dir = './sample_data/' # change this path to the data you wish to analyze

# Assume that the coordinates of slices are named slice_name + "_coor.csv"
def load_slices(data_dir, slice_names=["slice1", "slice2"]):
    slices = []  
    for slice_name in slice_names:
        slice_i = sc.read_csv(data_dir + slice_name + ".csv")
        slice_i_coor = np.genfromtxt(data_dir + slice_name + "_coor.csv", delimiter = ',')
        slice_i.obsm['spatial'] = slice_i_coor
        # Preprocess slices
        sc.pp.filter_genes(slice_i, min_counts = 15)
        sc.pp.filter_cells(slice_i, min_counts = 100)
        slices.append(slice_i)
    return slices

slices = load_slices(data_dir)
slice1, slice2 = slices

# Pairwise align the slices
pi12 = pst.pairwise_align(slice1, slice2)

# To visualize the alignment you can stack the slices 
# according to the alignment pi
slices, pis = [slice1, slice2], [pi12]
new_slices = pst.stack_slices_pairwise(slices, pis)

slice_colors = ['#e41a1c','#377eb8']
plt.figure(figsize=(7,7))
for i in range(len(new_slices)):
    pst.plot_slice(new_slices[i],slice_colors[i],s=400)
plt.legend(handles=[mpatches.Patch(color=slice_colors[0], label='1'),mpatches.Patch(color=slice_colors[1], label='2')])
plt.gca().invert_yaxis()
plt.axis('off')
plt.show()

# Center align slices
## We have to reload the slices as pairwise_alignment modifies the slices.
slices = load_slices(data_dir)
slice1, slice2 = slices

# Construct a center slice
## choose one of the slices as the coordinate reference for the center slice,
## i.e. the center slice will have the same number of spots as this slice and
## the same coordinates.
initial_slice = slice1.copy()    
slices = [slice1, slice2]
lmbda = len(slices)*[1/len(slices)] # set hyperparameter to be uniform

## Possible to pass in an initial pi (as keyword argument pis_init) 
## to improve performance, see Tutorial.ipynb notebook for more details.
center_slice, pis = pst.center_align(initial_slice, slices, lmbda) 

## The low dimensional representation of our center slice is held 
## in the matrices W and H, which can be used for downstream analyses
W = center_slice.uns['paste_W']
H = center_slice.uns['paste_H']

GPU implementation

PASTE now is compatible with gpu via Pytorch. All we need to do is add the following two parameters to our main functions:

pi12 = pst.pairwise_align(slice1, slice2, backend = ot.backend.TorchBackend(), use_gpu = True)

center_slice, pis = pst.center_align(initial_slice, slices, lmbda, backend = ot.backend.TorchBackend(), use_gpu = True)

For more details, see the GPU section of the Tutorial notebook.

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

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).

Sample Dataset

Added sample spatial transcriptomics dataset consisting of four breast cancer slice courtesy of:

[1] 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).

References

Ron Zeira, Max Land, Alexander Strzalkowski and Benjamin J. Raphael. "Alignment and integration of spatial transcriptomics data". Nature Methods (2022). https://doi.org/10.1038/s41592-022-01459-6