Cellenics

Designed with wet-lab biologists in mind, Cellenics is an open-source platform aimed at empowering research biologists to analyze their own single-cell RNA sequencing (scRNA-seq) datasets. This user-facing application for project management, data processing, quality control, and visualization was developed as part of a collaborative effort between Harvard Medical School’s (HMS’) Department of Biomedical Informatics (DBMI), Single Cell Core, and the TH Chan Harvard School of Public Health Bioinformatics Core, with input from HMS’ Biopolymers Facility and Center for Computational Biomedicine (CCB). Cellenics modules enable in-depth data exploration through differential expression and pathway analyses as well as the generation of fully customizable plots for publication.

• Automated cluster annotation with ScType
  • Found in the Cell sets and Metadata tile of Data Exploration
• Seurat object upload for exploration of previously analyzed datasets
  • Run QC, clustering, and annotation in R or python, then use Cellenics for point-and-click exploratory analyses and plotting.
• BD Rhapsody data upload
• Trajectory Analysis with Monocle3
  • Plot cells along a trajectory that defines the biological process’s activity or progression.
• SCTransform normalization for the Seurat integration method
  • A popular method to remove the influence of technical effects from single-cell RNA-seq data.
• Subset and re-cluster cell-sets from an existing dataset
  • Improves resolution in order to identify distinct subpopulations within an existing cluster.
• Streamline connection of SCC and BPF users with Cellenics (coming soon)
  • Users of the HMS SCC and BPF cores will have the option to indicate they want to use Cellenics for their downstream data analysis.