scRNAseq Analysis of ICI-Treated Patients

This page contains analysis scripts for scRNAseq datasets of melanoma and NSCLC patients treated with immune checkpoint inhibitors (ICIs).

Notebooks Overview

1. Sade-Feldman et al. (Melanoma)

This notebook performs scRNAseq analysis on a melanoma dataset. Key steps include:

• Loading and preprocessing the data.
• Log2 transformation of TPM counts.
• Exploratory analyses and visualization.

2. Caushi et al. (NSCLC)

This notebook performs scRNAseq analysis on a NSCLC dataset. Key steps include:

• Quality control of scRNAseq data.
• Analysis and visualization.

Prerequisites

To reproduce the analyses, ensure you have the following dependencies installed:

• Python (>=3.8)
• Libraries: numpy, pandas, seaborn, matplotlib, statsmodels, scipy, scikit-learn, scanpy, anndata, scrublet

Install dependencies using:

pip install numpy pandas seaborn matplotlib statsmodels scipy scikit-learn scanpy anndata scrublet

Data Sources

• The Sade-Feldman et al. Melanoma dataset, including patient metadata, clustering and tSNE coordinates, can be downloaded from the Single-Cell Portal. Expression matrix is also available via GSE120575.
• The Caushi et al. NSCLC dataset is available via GSE176021.

Ensure the datasets are saved in the appropriate directories according to your convenience.

Repository Structure

CD38_Project/
|-- Sade_Feldman_et_al_Melanoma_analysis.ipynb
|-- Caushi_et_al_NSCLC_analysis.ipynb

Citation

If you use this repository, please consider citing our paper with the relevant original studies:

• Revach et al.
• Sade-Feldman et al.
• Caushi et al.

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For questions or issues, please contact [email protected].