Meet BioTuring Browser and its new CITE-seq dashboard, a complete package for interactively exploring single-cell gene expression data in parallel with surface protein information
Instantly access and reanalyze latest single-cell RNA-seq and CITE-seq datasets from publications, all uniformly annotated, and ready for visualization.
Look for populations that own similar transcriptional profiles to your selected cells in the entire public database, at the same time study what genes are similarly expressed and enrichment processes.
Detect studies with positive expression of one or multiple genes, at the same time quickly compare gene expression among different clusters, cell types, disease conditions,... in any datasets.
Not just creating a gateway to published works, BioTuring Single-cell Browser is an end-to-end solution for YOUR own single-cell RNA sequencing data.
Retain true biological variances with various batch effect removal methods:
• Perform dimension reduction with t-SNE and UMAP
• Cluster the cells by k-means and graph-based clustering
• Explore novel cell sub-types on an interactive sub-clustering dashboard
• Predict cell types in real time upon selecting any groups of cells. The knowledge base for prediction can be customized to your own definition.
• Explore marker genes and run enrichment analysis
• Track the compositional changes in different treatments, or which treatment enriches different cell types.
• Find differentially expressed genes in any two groups of cells such as two cell types, sub-types, conditions, or any two stages of the disease
Perform parallel analysis combining single-cell RNA-seq data, TCR-seq data and cell surface information
BioTuring Single-cell Browser is optimized to visualize up to 1.3 million single cells at a time on a standard laptop with interactive t-SNE and UMAP.
Eugene Bolotin - Senior Bioinformatics Scientist, Kite Pharma
Researcher (Pharmacology), Chugai Pharmaceutical
Chris Ahuja - University of Toronto.
Niv Sabath - Senior Scientist, Compugen