SCITE-RNA

This repository contains the code and data for the paper Phylogenetic Tree Inference from Single-Cell RNA Sequencing. The code and datasets provided here enable users to replicate the experiments and figures presented in the paper, as well as to run SCITE-RNA on new data.

Table of Contents

• Overview
• Repository Structure
• Installation
  • Requirements
  • Cloning the Repository
  • Data Preparation
• Running the Model
  • Set Model Parameters
  • Simulated Data
  • Multiple Myeloma Data
  • Run on New Data
• Generating Figures

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Overview

We implement a new method for reconstructing phylogenetic trees from single-cell RNA sequencing data. SCITE-RNA selects single-nucleotide variants (SNVs), and reconstructs a phylogenetic tree of the sequenced cells. We maximize the likelihood of the inferred tree by alternating between the cell lineage and mutation tree spaces until convergence is achieved in both. This repository provides:

1. Scripts to execute SCITE-RNA. The model is split into C++ src_cpp and Python files src_python. Especially for large numbers of cells and SNVs it is recommended to use the C++ code, as it is significantly faster. The inferred trees should be comparable between the C++ and Python implementations.
2. Summaries of the data used in the paper is available in the data/ directory, which contains all necessary files to reproduce the figures.
3. Visualization scripts to generate plots as presented in the paper.

Repository Structure

SCITE-RNA
├── data/ # Input data files and results
      ├── input_data # Alternative and reference read counts among other files.
      ├── results # Inferred trees of the multiple myeloma dataset and figures
      └── simulated_data # Summary statistics of simulated data and respective inferred trees
├── generate_results_cpp/ # C++ scripts for tree inference
├── generate_results_python_r/ # Python and R scripts for simulating data, inferring trees and visualization
├── src_cpp/ # C++ source files for SCITE-RNA
├── src_python/ # Python source files for SCITE-RNA
├── configs/ # Model parameters (Python)
├── CMakeLists.txt # Primary configuration file for CMake
└── README.md # Project overview and setup instructions

Installation

Requirements

Python Libraries:

• numpy
• pandas
• matplotlib
• seaborn
• scipy
• numba
• math
• jupyter
• pyyaml
• graphviz

C++ Requirements

• CMake (>= 3.27)

Cloning the Repository

To set up the SCITE-RNA project locally:

git clone https://github.com/cbg-ethz/SCITE-RNA.git
cd SCITE-RNA

Data Preparation

To reproduce the figures quickly you can use the files provided in data. As the size and the number of files was quite large, we produced summary statistics using

generate_results_python_r/generate_summary_statistics.ipynb

Running the Model

Set Model Parameters

Adjust model parameters in configs/config.yaml for Python or adjust them in src_cpp/mutation_filter.h for C++.

Simulated Data

To generate new simulated data execute:

generate_results_python_r/comparison_data_generation.py

It offers the option to set the number of cells and SNVs and the number of clones simulated. The same file can also be used for tree inference. Alternatively, run the C++ version:

generate_results_cpp/comparison_num_clones.cpp 

for tree inference (not data generation).

To compare different optimization strategies of tree space switching run:

generate_results_cpp/comparison_tree_spaces_switching.cpp

Otherwise, the model will by default alternate between cell lineage and mutation tree optimization, starting from a random cell lineage tree.

All simulated results will be saved in data/simulated_data/.

Multiple Myeloma Data

To run SCITE-RNA on the Multiple Myeloma dataset:

1. Run mutation filtering:

   generate_results_python_r/MM.py
   

2. Perform tree inference in C++ for faster computation:

   generate_results_cpp/MM.cpp
   

Results will be saved in data/results/mm34/.

Run on New Data

To use SCITE-RNA on new data:

1. Prepare reference and alternative allele count files in .txt format. Use the format provided in data/input_data/new_data as a reference, where columns represent cells and rows represent SNVs.

2. Set the number of bootstrap samples (optional) and run SCITE-RNA tree inference with the following script:

   generate_results_cpp/run_sciterna.cpp
   

3. The results are saved in data/results/new_data/.

Generating Figures

To reproduce the plots presented in the paper, follow the instructions below:

• The plots are generated by default using the summary statistics generated with

    generate_results_python_r/comparison_data_generation.py
  

• Figure 3: Comparison of tree optimization strategies

  generate_results_python_r/comparison_tree_spaces_switching.py
  

• Figure 4: Comparison to SClineager and DENDRO + variable number of clones

• Figure A.2: Runtime comparison

  Optionally rerun DENDRO and SClineager on the simulated data first.

    generate_results_python_r/comparison_clones_sclineager_dendro_sciterna.R
  

  To generate the figures run

    generate_results_python_r/comparison_num_clones.ipynb
  

• Figure 5: Representative tree multiple myeloma

• Figure A.1: Gene expression analysis

    generate_results_python_r/bootstrap_results_mm.ipynb 
  

All figures will be saved in data/results/figures/.