scDenorm: a denormalisation tool for Integrating Single-cell Transcriptomics Data

Datasets and Jupyter notebooks to reproduce the analyses presented in the manuscript, scDenorm: a denormalisation tool for Integrating Single-cell Transcriptomics Data. Tutorial for Running Notebooks Download the Notebooks: Clone or download this repository from GitHub: scDenorm GitHub Repository or from Zenodo: scDenorm Data. Download and install Docker and Jupyter: Follow the instructions for installation: Docker Get Started. 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. 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 Open Fig5.ipynb. Select Kernel > Change Kernel > Python [conda env: sc]. 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 Run the notebook cells in order. Example running Fig5_R_goanalysis.ipynb Open Fig5_R_goanalysis.ipynb. Select Kernel > Change Kernel > R [conda env: sc]. 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 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