scDenorm_reproducibility

Overview

This repository contains Jupyter notebooks to reproduce the analyses presented in the manuscript "scDenorm: a denormalisation tool for Integrating Single-cell Transcriptomics Data."

Tutorial for Running Notebooks

1. Download the Notebooks: Clone or download this repository from GitHub: scDenorm GitHub Repository or from Zenodo: scDenorm Data.
2. Download and install Docker and Jupyter: Follow the instructions for installation: Docker Get Started.
3. Download Data:
   • Download the data file from Zenodo: scDenorm Data.
   • Unzip the downloaded data and place the relevant files into the scDenorm_reproducibility/data folder.
4. Run Docker Image:
   • Ensure Docker is running.
   • Load the Docker image directly from the .tar.gz file:

     docker load < scdenorm_v0.tar.gz

     or

     tar -xzf scdenorm_v0.tar.gz
     docker load -i scdenorm_v0.tar

   • Run the Docker container with the following command (update the local path accordingly):

     docker run --platform linux/amd64 \
     -p 8888:8888 \
     -v /path/to/scDenorm_reproducibility/data:/app \
     scdenorm_v0 \
     jupyter lab --ip=0.0.0.0 --no-browser --allow-root

   • Note: Ensure to share the project folder with Docker. Go to Docker → Preferences → Resources → File Sharing and add the local project path.

Example running Fig5.ipynb

1. Open Fig5.ipynb.
2. Select Kernel > Change Kernel > Python [conda env: sc].
3. Data Import: Copy the data from Zenodo into scDenorm_reproducibility/data, including:
   • fig5_input.h5ad
   • PBMC_before_scDenorm.h5ad
   • PBMC_after_scDenorm.h5ad
   • PBMC_groundgo.csv
   • PBMC_beforego.csv
   • PBMC_aftergo.csv
4. Run the notebook cells in order.

Example running Fig5_R_goanalysis.ipynb

1. Open Fig5_R_goanalysis.ipynb.
2. Select Kernel > Change Kernel > R [conda env: sc].
3. Data Import: Copy the data from Zenodo into scDenorm_reproducibility/data, including:
   • PBMC_raw_count_b0_deg.csv
   • PBMC_raw_count_b1_deg.csv
   • PBMC_normlized_data_1e3_b1_deg.csv
4. Run the notebook cells in order.

Notebooks

The repository includes the following notebooks:

• Fig1.ipynb: Analysis for Figure 1
• Fig2.ipynb: Analysis for Figure 2
• Fig3.ipynb: Analysis for Figure 3
• Fig4.ipynb: Analysis for Figure 4
• Fig5.ipynb: Analysis for Figure 5
• Fig6.ipynb: Analysis for Figure 6
• Fig7.ipynb: Analysis for Figure 7

Environment Configurations on local computer

• Python:
  • Environment file: config/environment.yaml
  • To create a Conda environment using the specifications in the environment.yaml file:

    conda env create -f environment.yaml

• R:
  • Installed packages: config/installed_packages.csv
  • To install necessary R packages, run the following in the R terminal:

    pkg_list <- read.csv("installed_packages.csv", stringsAsFactors = FALSE)
    for (pkg in pkg_list$Package) {
      if (!requireNamespace(pkg, quietly = TRUE)) {
        message(" Installing the package: ", pkg)
        install.packages(pkg, dependencies = TRUE)
      }
    }

How to Use and Install scDenorm

• Documentation: https://changebio.github.io/scDenorm

• Install:

  • Using pip:

    pip install scDenorm

• Usage:

  scdenorm data/pbmc3k_norm.h5ad --fout data/pbmc3k_denorm.h5ad

Citation

Yin Huang, Anna Vathrakokili Pournara, Ying Ao, Ziliang Huang, Hui Zhang, Yongjian Zhang, Sheng Liu, Alvis Brazma, Irene Papatheodorou, Xinlu Yang, Ming Shi, Zhichao Miao “scDenorm: a denormalisation tool for integrating single-cell transcriptomics data”(Under review)

For any questions or issues, please open an issue in the repository.