Metadata-Version: 2.1
Name: paste-bio
Version: 1.2.0
Summary: A computational method to align and integrate spatial transcriptomics experiments.
Home-page: https://github.com/raphael-group/paste
Author: Max Land
Author-email: max.ruikang.land@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/raphael-group/paste/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# PASTE

![PASTE Overview](https://github.com/raphael-group/paste/blob/main/paste_overview.png)

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:
1. `pairwise_align`: align spots across pairwise slices.
2. `center_align`: integrate multiple slices into one center slice.

You can read our preprint [here](https://www.biorxiv.org/content/10.1101/2021.03.16.435604v1). 

PASTE is actively being worked on with future updates coming. 

### Recent News

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.

### Dependencies

To run PASTE, you will need the following Python packages:
1. POT: Python Optimal Transport (https://PythonOT.github.io/)
2. Scanpy (https://scanpy.readthedocs.io/en/stable/)
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` 

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](sample_data/) of this repo 
that we will use as an example below to show how to use PASTE.

```python
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']
```

### 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).


