Welcome to Celligrate!

Celligrate is a project for cell type characterization and integration from single cell RNA-sequencing (scRNA-seq) data. The backbone of Celligrate consists of two carefully-designed and extensively-validated computational algorithms: NS-Forest and FR-Match. NS-Forest is a random forest machine learning algorithm for cell type marker gene identification. FR-Match is a topological graph theory-based statistical learning algorithm for cell type matching. Celligrate also introduces a notion of “cell type barcode” for insightful visualization of cell type expression data. The use of Celligrate extends the utility of the upstream scRNA-seq analysis pipelines to downstream use cases, and ultimately accelerates the growth of knowledge about cell types by pooling results from individual studies.

See Methods for more details.

Software Suite

Tutorials

NS-Forest

FR-Match

Demo data in the tutorials

For inquiries of demo data, please contact zhangy@jcvi.org.

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