Kari Lavikka

I made a bold move: I bumped the GenomeSpy version from v0.90.0 to … v1.0.0! 🎉 I also wrote a blog post about it, reflecting a bit on the development journey: genomespy.app/blog/posts/2...

GenomeSpy is an open-source toolkit for building interactive genome visualizations using a declarative grammar. A bit like ggplot2, but primarily for genome-aligned data with smooth interactivity. It also includes an application for exploring multi-sample genomic datasets, such as cancer cohorts.

I made an R package that creates oncoplots, lollipop plots, rainfall plots, and GISTIC plots with basic interactivity such as tooltips and zooming. It's almost a drop-in replacement for the corresponding maftools' plotting functions.

This was also a good opportunity to demonstrate how GenomeSpy (genomespy.app) can be embedded in an R package. The package generates visualization specifications and lets GenomeSpy do the rendering. And finally, I like my plots prettier.

I made an R package that creates oncoplots, lollipop plots, rainfall plots, and GISTIC plots with basic interactivity such as tooltips and zooming. It's almost a drop-in replacement for the corresponding maftools' plotting functions.

When doing copy-number analysis, have you ever struggled to interpret ASCAT sunrise plots and similar purity/p​​loidy grids? Have you wanted to compare how different solutions affect the allele-specific copy-number fit and better understand the fitting procedure?

The visualization also projects the inferred integer copy numbers back into LogR and BAF space, making it easier to see how well a selected solution explains the observed data. The visualization is here: genomespy.app/docs/example...

When doing copy-number analysis, have you ever struggled to interpret ASCAT sunrise plots and similar purity/p​​loidy grids? Have you wanted to compare how different solutions affect the allele-specific copy-number fit and better understand the fitting procedure?

I recently added a parameterizable arrow mark to GenomeSpy, my open-source toolkit for interactive genomic data visualization, and implemented a couple of fancy examples to showcase it. (1/3)

A reference sequence translated into amino acids in all three reading frames on both strands, and a simple read-alignment viewer for BAM files. They also use some new data transformations I added to the visualization grammar. (2/3)

It’s been a bit quiet on the GenomeSpy front, but I finally had time to implement interval selections and other fancy features. Vega-Lite–style conditional encodings are compiled into shader code, with selection tests running on the GPU—making interactions snappy. (1/2)

Jellyfish visualization tool and the Jellyfisher R package have now been published in OUP Bioinformatics! 🎉 doi.org/10.1093/bioi... #Bioinformatics #DataViz (1/7)

Jellyfish and Jellyfisher are available on GitHub: github.com/HautaniemiLa..., github.com/HautaniemiLa... (docs: hautaniemilab.github.io/jellyfisher/) #OpenSource #RStats #Bioinformatics (6/7)

Jellyfish visualization tool and the Jellyfisher R package have now been published in OUP Bioinformatics! 🎉 doi.org/10.1093/bioi... #Bioinformatics #DataViz (1/7)

Jellyfish automates the drawing process and generates visually pleasing plots with ease. Based on the data used in the tumor evolution paper, we've now made auto-generated, interactive Jellyfish plots available at hautaniemilab.github.io/jellyfish/ (4/7)

Jellyfish visualization tool and the Jellyfisher R package have now been published in OUP Bioinformatics! 🎉 doi.org/10.1093/bioi... #Bioinformatics #DataViz (1/7)

Metastases in solid tumors consist of heterogeneous subclonal mixtures that evolve across space and time. Jellyfish integrates tumor phylogeny and subclonal compositions of spatiotemporal samples into a unified plot, making subclonal dynamics easier to interpret. #CancerResearch (2/7)