Back

UNCURL-App: Interactive Database-Driven Analysis of Single Cell RNA Sequencing Data

Zhang, Y.; Mao, S.; Mukherjee, S.; Kannan, S.; Seelig, G.

2020-04-16 bioinformatics
10.1101/2020.04.15.043737 bioRxiv
Show abstract

MotivationAnalysis of single cell RNA sequencing (scRNA-seq) datasets is a complex and time-consuming process, requiring both biological knowledge and technical skill. With the rapid growth in scRNA-seq datasets, there is a need to simplify and systematize this process. ResultsWe introduce UNCURL-App, an online GUI-based interactive tool which integrates the scRNA-seq analysis pipeline with prior knowledge. UNCURL-App introduces two key innovations: First, cell type databases are integrated directly with the rest of the analysis process, allowing the user to identify potential cell types or pathways directly from the data. Second, tools for interactive re-analysis allow the user to create, merge, or delete clusters. In addition, UNCURL-App integrates multiple stages of the data analysis pipeline into a single interface, including dimensionality reduction, clustering, differential expression, and cell type identification. AvailabilityThe web tool is available at https://uncurl.cs.washington.edu/. Source code is available at https://github.com/yjzhang/uncurl_app Contactgseelig@uw.edu

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.