A modern platform for single-cell sequencing data analysis
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.
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
With BioTuring Single Cell Browser, you can pair TCR repertoire sequencing data with single cell RNA-seq expression data, at the same time get more information on the epitope that can be recognized
With BioTuring Single-cell Browser, you can access and analyze single-cell sequencing datasets from latest high-impact publications (5,499,721 cells). This library of published data can be combined with in-house data for meta-analysis.
Finding differentially expressed genes
Identifying cell types with real-time prediction
Finding marker genes
Pairing clonotype data with expression data
Viewing composition of a cell population
Querying gene expression
UMAP for tracking retinal development
Eugene Bolotin - Senior Bioinformatics Scientist, Kite Pharma
Researcher (Pharmacology), Chugai Pharmaceutical
Chris Ahuja - University of Toronto.