Overview
Good figures make results clear. We build static and interactive visualisations tailored to your data and journal.
Salient features
- Volcano, heatmap, track and browser views
- Consistent, accessible styling
- Editable vector output
Analyses we offer
Genome data visualization
Creates genome browser views and track figures of coverage, variants and annotation.
Methods: igv, igv.js, pyGenomeTracks.
Transcriptome data visualization
Produces volcano plots, heatmaps, PCA and UMAP figures for expression data.
Methods: EnhancedVolcano, ComplexHeatmap, scanpy.
Proteome data visualization
Visualises proteomics quantification, QC and differential results.
Methods: alphapeptstats, MSstats.
Epigenome data visualization
Builds profile plots, heatmaps and tracks around peaks and genomic features.
Methods: deepTools, pyGenomeTracks, eg-react.
Graphing and visualisation
Creates custom static and interactive figures to publication standards.
Methods: matplotlib, ggplot2, plotly.py.
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.