# 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

## Usage

We have now made a documentation website for rliger 2.0.0. Please check it out for detailed introduction.

We have made a number of vignettes for typical types of analysis that can be performed with LIGER.

- Integrating Multiple Single-Cell RNA-seq Datasets
- Jointly Defining Cell Types from scRNA-seq and scATAC-seq
- Iterative Single-Cell Multi-Omic Integration Using Online iNMF
- Integrating unshared features with UINMF
- Integrating spatial transcriptomic and transcriptomic datasets using UINMF
- scATAC and scRNA Integration using unshared features (UINMF)
- Cross-species Analysis with UINMF
- Jointly Defining Cell Types from Single-Cell RNA-seq and DNA Methylation

Meanwhile, since version 2.0.0, LIGER is massively updated for usability and interoperability with other packages. Below are links to the introduction of new features.

- Introduction to new liger object and other related classes
- Running Liger directly on Seurat objects

## Loading the package

    library(rliger)