Data Integration with LIGER

Introduction

LIGER was initially introduced in Welch et al. 2019 as a method for integrating single-cell RNA-seq data across multiple technologies, species, and conditions. The method relies on integrative nonnegative matrix factorization (iNMF) to identify shared and dataset-specific factors.

LIGER can be used to compare and contrast experimental datasets in a variety of contexts, for instance:

  • Across experimental batches
  • Across individuals
  • Across sex
  • Across tissues
  • Across species (e.g., mouse and human)
  • Across modalities (e.g., scRNAseq and spatial transcriptomics data, scMethylation, or scATAC-seq)

Once multiple datasets are integrated, the package provides functionality for further data exploration, analysis, and visualization. Users can:

  • Identify clusters
  • Find significant shared (and dataset-specific) gene markers
  • Compare clusters with previously identified cell types
  • Visualize clusters and gene expression using t-SNE and UMAP