Welcome to Clustergrammer’s Documentation!

The Clustergrammer project consists of web-based tools for visualizing and analyzing high-dimensional data as interactive and shareable hierarchically clustered heatmaps (see Introduction to Clustergrams). Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see Biology-Specific Features) to facilitate the exploration of gene-level biological data. The project is free, open-source (all code is available on GitHub), and being actively developed at the Human Immune Monitoring Center and the Ma’ayan Lab at the Icahn School of Medicine at Mount Sinai.

What’s New

Visium Spatial Transcriptomics Data from 10X Genomics

visium-clustergrammer2

We used Clustergrammer2, the plotting library bqplot, the Jupyter dashboard library voila, and the Jupyter notebook hosting service Binder to build an interactive data exploration dashboard for Visium data from the mouse brain from 10X Genomics (try dashboard: Visium-Clustergrammer2 Dashboard, code: https://github.com/ismms-himc/visium-clustergrammer2). This dashboard generates linked views of spatial tissue data and high-dimensional gene expression data - see GitHub repo https://github.com/ismms-himc/visium-clustergrammer2 for more information.

Single Cell Immune Profiling of Atherosclerotic Plaques

Our collaborators in the Giannarelli Lab used single-cell proteomics and transcriptomics to investigate the immune landscape of atheroscelerotic plaques (Fernandez et al.) and identify features of T cells and macrophages that were associated with clinical symptomatic disease state (see Single-cell Gene Expression and Proteomics from Human Atherosclerotic Plaques). We used Clustergrammer2 to analyze scRNA-seq and CITE-seq data as well as infer cell-cell communication pathways.

Clustergrammer2

MyBinder-scRNA-seq NBViewer-scRNA-seq

Clustergrammer2 is a WebGL (specifically regl) visualization tool that enables researchers to easily visualize and explore large high-dimensional single-cell datasets (e.g. scRNA-seq data) without the need for traditional dimensionality reduction methods (e.g. t-SNE or UMAP). Please see the tutorial video above and Case Studies and Tutorials for more information.

Case Studies and Examples

Clustergrammer was developed to visualize high-dimensional biological data (e.g. genome-wide expression data), but it can also generally be applied to any high-dimensional data. Please refer to the Case Studies and Tutorials and links below for more information:

JupyterCon 2018

The Clustergrammer-Widget was presented at JupyterCon 2018.

Contact

Please contact Nicolas Fernandez (nicolas.fernandez@mssm.edu) and Avi Ma’ayan (avi.maayan@mssm.edu) for support, comments, and suggestions. Users can also visit the discussion forum Clustergrammer2-Gitter.

Citing Clustergrammer

Please consider supporting Clustergrammer by citing our publication:

Fernandez, N. F. et al. Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data. Sci. Data 4:170151 doi: 10.1038/sdata.2017.151 (2017).

Funding

Clustergrammer is being developed by the Ma’ayan Lab and the Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai for the BD2K-LINCS DCIC and the KMC-IDG.

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