If you work with single-cell data, you know how difficult it is to wrangle large volumes of data and distill it down to meaningful insights. A well-done analysis requires scientists to interrogate their data at any stage of the analysis and a robust infrastructure to manage hundreds of file outputs. Unfortunately, many scientists struggle to work with their single-cell data due to an absence of data management, inability to iterate and ineffective collaboration solutions. And their analyses are slowing to a halt.
To solve these challenges, we’re excited to announce support for Single-Cell Integrated Analysis in the BioBox Platform - a way for scientists to easily manage, visualize and analyze terabytes worth of single-cell data all in one interface.
Working with large amounts of data is no easy feat. With the added complexity of manually comparing and contrasting groups across varying single-cell experiments, organizing this data can become tedious. In the BioBox Platform you can:
One of the core highlights of a single-cell analysis is being able to subset the data based on cell populations and groups. Across this subset, users can now quickly analyze and discern cell identity, explore expression levels of your genes of interest, visualize data as violin & UMAP plots, and explore pathway enrichment.
And since cell type identification can become tricky with the added variable of contrasting groups, we enable you to explore genes that are conserved across groups for each cluster, too.
If you are a current BioBox user, the Single-Cell Integrated Analysis is readily available in your organization. Read this document to get a step-by-step guide on how to run your analysis. If you’re new around here, sign up for a free 30 day trial to get started with Single-Cell Integrated Analysis today.