A Classification of Cellular Processes Through Aggregation of Single-Cell Gene Expression Trajectories

To develop standards for describing “cell trajectories” found in single-cell RNA-seq datasets, methods to align trajectories, and tools to search known and novel cell differentiation transitions.

The Stuart lab focused on establishing benchmark datasets to aid in new data analyses to support the Human Cell Atlas and creating annotated and queryable clustered information from these datasets. They published two papers, developed new protocols, generated a benchmark dataset, and built 14 computational tools—including scoreCT, a tool to automate cell-type annotation on single-cell RNA-sequencing data, thus addressing one of the major bottlenecks in RNA-seq analysis.

Papers:

• UCSC Cell Browser: Visualize Your Single-Cell Data
• Cross-Species Alignment of Single Cell States with Biological Process Activity

Software:

DPT-docker, Cluster-DB, Leesl, scoreCT, Cluster Solution Format, Cytracepy, Slicer Docker, SCIMITAR, Slingshot Docker, Monocle Docker, UCSC Cell Type, Traj Formats, UCSC Cell Atlas API, UCSC Cell Atlas, Wishbone Docker