Overview
Single-cell data reveals cell types and states that bulk sequencing averages away. We handle quality filtering, integration, annotation and downstream biology.
Salient features
- Doublet, ambient RNA and batch handling
- Cell-type annotation and trajectory analysis
- Spatial and multi-modal extensions
Analyses we offer
scRNA-seq analysis
Takes single-cell expression data through QC, clustering, annotation and differential analysis.
Methods: scanpy, seurat, scvi-tools.
scDNA-seq analysis
Calls copy number and clonal structure from single-cell DNA sequencing.
Methods: SCOPE, SCICoNE, copykat.
Spatial transcriptomics
Maps gene expression within tissue, including deconvolution and spatial statistics.
Methods: squidpy, cell2location, spacexr.
Single-cell ATAC-seq
Analyses chromatin accessibility of individual cells and links it to cell identity.
Methods: signac, ArchR.
RNA velocity
Estimates the direction and speed of cell-state change from spliced and unspliced reads.
Methods: scvelo.
Trajectory inference
Orders cells along differentiation or response paths.
Methods: monocle3, dyno.
Cell-cell communication
Infers ligand-receptor signalling between cell types.
Methods: CellChat, liana, CellphoneDB.
What you receive
- A written report with methods, parameters and interpretation
- Processed data tables and publication-quality figures
- Scripts and settings so every result can be reproduced
Not sure which analysis fits your data? Contact us and we will advise.