FastCCC Deployment

Introduction

In this section, we introduce how to install FastCCC. For details on using FastCCC, please check the Basic Usage section for an overview of the core functions and how to run CCC analysis on a single-cell dataset. For examples on how to build the human CCC reference panel and perform CCC analysis on user-collected query datasets, refer to the Human CCC Reference section.

Additionally, FastCCC serves as a comprehensive CCC analysis toolkit with various tools that are widely applicable in single-cell RNA-seq studies. Usage of these tool functions can be found in the Toolkits section.

Installation

FastCCC is implemented as a Python (>= 3.11) package. If you wish to use our development version, which allows downloading reference datasets from the CellxGene using the FastCCC environment, please note that the current Python version must be lower than 3.13, as CellxGene-Census does not support higher versions. FastCCC depends on a few other Python packages that include numpy, pandas, scipy, scanpy, loguru, openpyxl, and gseapy. Please refer to the package pyproject.toml file for details. For your convenience, you can choose one of the following methods to set up and use the environment.

Method 1: Installing via conda

recommend

You can install the environment using conda by following the steps:

conda create -n FastCCC python=3.11
conda activate FastCCC

Get FastCCC from github:

git clone https://github.com/Svvord/FastCCC.git

Go to the folder FastCCC and install:

cd ./FastCCC
pip install -e .

Tips

We recommend this installation method for general users, as it has been tested across various servers to ensure a smooth setup and execution.

Method 2: Installing via pip

Coming soon

Coming soon

We are currently organizing the code and packaging functionalities to enhance user convenience. Once the code is finalized, we will upload it to PyPI to support installation via pip install. At this stage, please use the code available on GitHub and install it using Conda or Poetry.

pip install fastccc # coming soon.

Method 3: Installing developing version via Poetry

For developing, we are using the Poetry package manager. To install Poetry, follow the instructions here.

git clone https://github.com/Svvord/FastCCC.git
cd ./FastCCC
poetry install

Tips

We recommend creating a dedicated FastCCC virtual environment with Python version 3.11 and installing dependencies using Poetry.