Single-cell Browser

A modern platform for single-cell sequencing data analysis

1.3 million cells in 3Dinteractive

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


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:



• 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


• 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)


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

Get started with

BioTuring Single Cell Browser