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BioTuring
Single-cell Browser
A modern platform for single-cell sequencing data analysis
View
1.3 million cells in 3Dinteractive
displays
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.
Get a complete
analysis landscape
Quantification
Quantify transcripts at unparalleled speed using Hera-T. No commands are required.
Batch effect removal
Retain true biological variances with various batch effect removal methods:
• CCA
• MNN
• Harmony
Dimension reduction and clustering
• 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
Annotation and prediction
• 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
Comparison
• 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
Pairing V(D)J data
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
Instantly explore published single-cell datasets alongside in-house data
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.
A unique microglia type associated with restricting development of Alzheimer’s disease (Keren-Shaul et al., 2017)
Finding differentially expressed genes
The single-cell transcriptional landscape of mammalian organogenesis (Cao et al., 2019)
3D UMAP
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris (Tabula Muris Consortium, 2017)
Identifying cell types with real-time prediction
Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity (van Galen et al., 2019)
Finding marker genes
Single-cell map of diverse immune phenotypes in the breast tumor microenvironment (Azizi et al., 2018)
Pairing clonotype data with expression data
Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma (Li et al., 2019)
Viewing composition of a cell population
The bone marrow microenvironment at single-cell resolution (Tikhonova et al., 2019)
Querying gene expression
Clark, Brian S., et al. "Single-Cell RNA-Seq Analysis of Retinal Development Identifies NFI Factors as Regulating Mitotic Exit and Late-Born Cell Specification (Clark et al., 2019)
UMAP for tracking retinal development
Find out custom-built
analytics solutions
for you
For immunologists
- Predict cell types with a built-in knowledge base highly optimized for immune cells
- Study T cell expansion by pairing clonotype data from TCR repertoire sequencing with expression data
- Explore single-cell immune datasets from high-impact studies
For neurologists
- Track neural development with 2D and 3D UMAP
- Discover novel neural cell types and subtypes with marker gene detection and sub-clustering
- Explore and combine published neural cell atlases (like Dropviz) with in-house data for meta-analysis
For bioinformaticians
- Quantify transcripts with high speed and accuracy
- Export MTX files to flexibly use for your own pipeline
