liger

LIGER (Linked Inference of Genomic Experimental Relationships)

Downcodes小编 Oct., 2024

LIGER (installed as rliger) is a powerful package for integrating and analyzing multiple single-cell datasets. Developed by the Macosko lab and actively maintained and extended by the Welch lab, LIGER utilizes integrative non-negative matrix factorization to identify both shared and dataset-specific factors across different single-cell datasets.

Dive deeper into the methods and analyses in our Cell paper. Access the data used in our SN and BNST analyses via study "SCP466" on the Single Cell Portal.

LIGER Applications

LIGER proves particularly useful for comparing and contrasting experimental datasets in diverse research contexts, including:

1. Identifying cell types shared across multiple experiments: LIGER allows researchers to identify common cell types across multiple datasets, even when these datasets come from different experimental conditions or platforms.

2. Detecting differences in cell type composition: LIGER facilitates the comparison of cell type proportions across different conditions, enabling researchers to identify changes in cellular composition driven by factors such as drug treatment or developmental stage.

3. Analyzing cell type-specific gene expression: LIGER enables researchers to study gene expression patterns specific to different cell types across multiple datasets, providing insights into the molecular mechanisms underlying cellular function and differentiation.

4. Investigating cell-cell interactions: LIGER can be used to study interactions between different cell types across multiple datasets, providing a deeper understanding of cellular communication and signaling pathways.

LIGER Functionality

Once multiple datasets are integrated, LIGER offers a range of functionalities for further data exploration, analysis, and visualization. Users can:

1. Perform dimensionality reduction: LIGER provides tools for dimensionality reduction techniques like principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the integrated data and identify underlying cellular relationships.

2. Cluster cells: LIGER facilitates cell clustering based on their similarities in gene expression profiles, enabling researchers to identify distinct cell populations within the integrated data.

3. Perform differential expression analysis: LIGER allows users to identify genes differentially expressed between different cell types or experimental conditions, providing insights into the molecular mechanisms underlying cellular differences.

4. Visualize the integrated data: LIGER provides various visualization tools, such as heatmaps and scatter plots, to explore and present the integrated data in a clear and informative manner.

Interoperability

LIGER is designed to seamlessly integrate with popular single-cell analysis packages like Seurat, facilitating a smooth workflow and enhancing data analysis capabilities.

Citing LIGER

If you use LIGER in your research, please cite our paper accordingly:

Joshua D. Welch et al., Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity, Cell, VOLUME 177, ISSUE 7, P1873-1887.E17 (2019), https://doi.org/10.1016/j.cell.2019.05.006

Liu, J., Gao, C., Sodicoff, J. et al. Jointly defining cell types from multiple single-cell datasets using LIGER. Nat Protoc 15, 3632–3662 (2020), https://doi.org/10.1038/s41596-020-0391-8

Gao, C., Liu, J., Kriebel, A.R. et al. Iterative single-cell multi-omic integration using online learning. Nat Biotechnol 39, 1000–1007 (2021), https://doi.org/10.1038/s41587-021-00867-x

Kriebel, A.R., Welch, J.D. UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization. Nat Commun 13, 780 (2022), https://doi.org/10.1038/s41467-022-28431-4

Feedback and Support

Feel free to open an Issue on our repository if you have any questions, comments, or suggestions.

Usage

For detailed usage examples and guided walkthroughs of specific use cases, please refer to our articles below:

[Link to Article 1]

[Link to Article 2]

[Link to Article 3]

LIGER 2.0.0 and beyond: Since version 2.0.0, LIGER has undergone significant updates to enhance usability and interoperability with other packages. Learn about these exciting new features here: [Link to New Feature Introduction]

LIGER 1.0.1 Tutorials: If you need to access the tutorials for the previous version of rliger (v1.0.1), please visit our GitHub archive: [Link to GitHub Archive] Download the desired rendered HTML files and open them in your browser.

Sample Datasets

The rliger package includes various types of small toy datasets for basic demonstrations of its functions. After attaching the package in an R session, you can load them using:

`R

data("pbmc")

data("pbmcPlot")

data("bmmc")

`

Additionally, we provide a curated set of real-world datasets for more comprehensive demos, encompassing scRNAseq, scATACseq, spatial transcriptomics, and DNA methylation data. These datasets are thoroughly described in the articles that utilize them.

Don't hesitate to explore these datasets through the links provided above!

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