Single Cell Analysis for Everyone

Empowering researchers and clinicians to gain insights from single cell experiments with interactive data visualization and easy to use but powerful machine learning methods to classify and model single cell data.


Download scOrange
Blog mark
10 May
Combine clustering and time component to pinpoint the time of trophectoderm (TE) and inner cell mass (ICM) differentiation in human preimplantation embryotic cells Read more
06 May
Determine karyotype (XY, XX, X0) of the samples from single cell data and observe the time of activation of Y chromosome in a preimplantation embryo. Read more
23 April
Identify subsets of exhausted CD8 + T cells during chronic viral infections and corresponding groups in T cells isolated from tumours. Read more

Explore the Diversity of Cells Within Your Sample

Load data from any platform and filter outlier cells. Normalize expression values across samples and platforms. Identify and explore sub-populations with a sample and across multiple samples.

Discover New Marker Genes that Distinguish Cell Types

Identify signature genes for each subpopulation using multiple methods. Use gene ontology enrichment to explore the biological meaning and identify cell types.

Predict New Cell Types Based on Marker Genes

Build classifiers to identify the cell type of each subpopulation. Use classifier on new data samples to predict cell types and focus on interesting cell type populations.