BMEN 4480 Final Project (Fall 2020)

Overview

We applied NicheNet to infer intercellular Ligand-Receptor (L-R) interactions from scRNA-seq data. In particular, we investigated the mutual interactions between cancer stem cells (CSCs) and tumor associated macrophages (TAMs) retrieved from the following dataset via NicheNet. We also modified the L-R interaction weights by combining NicheNet's prior network scores and our updated scores from the dataset-specific expression values.

Prerequisites

Python: Numpy, Pandas, Scanpy
R: nichenetr

Installation of NicheNet from their github page:

Installation typically takes a few minutes, depending on the number of dependencies that has already been installed on your pc. You can install nichenetr (and required dependencies) from github with:

install.packages("devtools")
devtools::install_github("saeyslab/nichenetr")

nichenetr was tested on both Windows and Linux (most recently tested R version: R 4.0.0)

Files & Directories

Scripts

• preprocessing.ipynb Preprocessing, data cleaning, clustering, identification of expressed genes & marker genes for each cluster
• weight_calculatioo.ipypb Update the edge scores of the L-R prior network from NicheNet with expression values (see the concept figure)
• rl_utils.R Utility & visualization functions for ligand receptor interaction inference
• rl_predictions.Rmd L-R inference with NicheNet's prior network
• rl_predictions_weighted.Rmd L-R inference with our updated network
• visualize.Rmd Visualization of top L-R interactions
• Brain.R Differential analysis & gene ontology, pathway analysis

Directories

• dataset: Original & preprocessed count matrices
• supplementary_materials: supplementary table for final report
• plots: Output plots with NicheNet's prior network
• plots_weighted: Output plots with our updated network
• top_rl_pairs: Top inferreed L-R pairs with NicheNet's prior network
• top_rl_pairs_weighted: Top inferred L-R pairs with our updated network