Metadata-Version: 2.1
Name: spared
Version: 2.2.1
Summary: SpaRED and Spackle library
Home-page: https://github.com/BCV-Uniandes/SpaRED/tree/main
Author: Daniela Vega
Author-email: d.vegaa@uniandes.edu.co
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/BCV-Uniandes/SpaRED/issues
Project-URL: Source, https://github.com/BCV-Uniandes/SpaRED/
Description: # Library_Spared_Spackle
        
        This repository contains all the necessary files to create a PyPI library to the SPARED and SpaCKLE contributions
        
        This is the  README file which will contain the long description of the PiPy library. Most libraries have a README file. Mean while this file will only contain this information and will be soon updated. 
        
        ## Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion
        
        [Gabriel Mejía](https://scholar.google.com/citations?hl=es&user=yh69hnYAAAAJ)<sup>1,2</sup>\*, [Daniela Ruiz](https://scholar.google.com/citations?hl=es&user=Zm-tYR0AAAAJ)<sup>1,2</sup>\*, Paula Cárdenas<sup>1,2</sup>, Leonardo Manrique<sup>1,2</sup>, Daniela Vega<sup>1,2</sup>, [Pablo Arbelaez](https://scholar.google.com/citations?hl=es&user=k0nZO90AAAAJ)<sup>1,2</sup>
        
        <br/>
        <font size="1"><sup>*</sup>Equal contribution.</font><br/>
        <font size="1"><sup>1 </sup> Center  for  Research  and  Formation  in  Artificial  Intelligence (<a href="https://cinfonia.uniandes.edu.co">CinfonIA</a>), Bogotá, Colombia.</font><br/>
        <font size="1"><sup>2 </sup> Universidad  de  los  Andes,  Bogotá, Colombia.</font><br/>
        
        - Preprint available at arXiv
        - Visit the project on our [website](https://bcv-uniandes.github.io/spared_webpage/)
        - Download [SpaRED datasets](https://drive.google.com/drive/folders/15W_rZlt5PhUlslM-u5_jw9etjkGRXb-N?usp=sharing)
        
        ### Abstract
        
        Spatial Transcriptomics is a novel technology that aligns histology images with spatially resolved gene expression profiles. Although groundbreaking, it struggles with gene capture yielding high corruption in acquired data. Given potential applications, recent efforts have focused on predicting transcriptomic profiles solely from histology images. However, differences in databases, preprocessing techniques, and training hyperparameters impact a fair comparison between methods. To address these challenges, we present a systematically curated and processed database collected from 26 public sources, representing an 8.6-fold increase compared to previous works. Additionally, we propose a state-of-the-art transformer-based completion technique for inferring gene expression, which significantly boosts the performance of transcriptomic profile predictions across all datasets. Altogether, our contributions constitute the most comprehensive benchmark of gene expression prediction from histology images to date and a stepping stone for future research.
        
        ## System Dependencies
        
        Before installing the Python package, ensure the following system dependencies are installed:
        
        ```shell
        conda create -n spared
        conda activate spared
        conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
        conda install lightning -c conda-forge
        pip install torch_geometric
        conda install -c conda-forge squidpy
        pip install wandb
        pip install wget
        pip install combat
        pip install opencv-python
        pip install positional-encodings[pytorch]
        pip install openpyxl
        pip install pyzipper
        pip install plotly
        pip install sh
        pip install sphinx
        pip install -U sphinx-copybutton
        pip install -U sphinx_rtd_theme
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
