cspot 1.0.9

CELL SPOTTER (CSPOT): A scalable framework for automated processing of highly multiplexed tissue images

🐊 Getting Started with CSPOT

Kindly note that CSPOT is not a plug-and-play solution. It's a framework that requires significant upfront investment of time from potential users for training and validating deep learning models, which can then be utilized in a plug-and-play manner for processing large volumes of similar multiplexed imaging data.

There are two ways to set it up based on how you would like to run the program

• Using an interactive environment like Jupyter Notebooks
• Using Command Line Interface

Before we set up CSPOT, we highly recommend using a environment manager like Conda. Using an environment manager like Conda allows you to create and manage isolated environments with specific package versions and dependencies.

Download and Install the right conda based on the opertating system that you are using

Create a new conda environment

# use the terminal (mac/linux) and anaconda promt (windows) to run the following command
conda create --name cspot -y python=3.9
conda activate cspot

Install cspot within the conda environment.

pip install cspot

Interactive Mode

Using IDE or Jupyter notebooks

pip install notebook

# open the notebook and import CSPOT
import cspot as cs
# Go to the tutorial section to follow along

Command Line Interface

wget https://github.com/nirmalLab/cspot/archive/main.zip
unzip main.zip 
cd cspot-main/cspot 
# Go to the tutorial section to follow along

Docker Container

docker pull nirmallab/cspot:cspot
# Go to the tutorial section to follow along