scITD

• Introduction
• Installation
• Walkthrough
• Citation

Introduction

Single-Cell Interpretable Tensor Decomposition (scITD) is computational method capable of extracting multicellular gene expression programs that vary across donors or samples. The approach is premised on the idea that higher-level biological processes often involve the coordinated actions and interactions of multiple cell types. Given single-cell expression data from multiple heterogenous samples, scITD aims to detect these joint patterns of dysregulation impacting multiple cell types. This method has a wide range of potential applications, including the study of inter-individual variation at the population-level, patient sub-grouping/stratification, and the analysis of sample-level batch effects. The multicellular information provided by our method allows one to gain a deeper understanding of the ways that cells might be interacting or responding to certain stimuli. To enable such insights, we also provide an integrated suite of downstream data processing tools to transform the scITD output into succinct, yet informative summaries of the data.

Installation

The package has several dependencies from Bioconductor. To install these:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("ComplexHeatmap", "edgeR", "sva", "Biobase"))

Then, to install scITD from CRAN:

install.packages('scITD')

To install the latest version of scITD from GitHub:

devtools::install_github("kharchenkolab/scITD")

Walkthrough

Follow the walkthrough to learn how to use scITD. The tutorial introduces the standard processing pipeline and applies it to a dataset of PBMC’s from 45 healthy donors.

We also created a tutorial for running ligand-receptor analysis. This uses the same dataset as the main walkthrough.

Citation

If you find scITD useful for your publication, please cite:

Jonathan Mitchel, M. Grace Gordon, Richard K. Perez, Evan Biederstedt, Raymund Bueno, Chun Jimmie Ye, Peter V. Kharchenko (2022). Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals. bioRxiv 2022.