Biology subjects

Vilanova, A.

Publications and source records attributed to Vilanova, A..

3 recordsLinked to original sources

bioRxiv2025

PanVA: a visual analytics tool for pangenomic variant analysis

SummaryThe growing number of sequences and increasing proof that single references create reference bias have driven the development of pangenomes to represent the genomic diversity of species. To leverage this complex diversity information for biological insights, analysis and visualization support are needed to explore the variants in the context of metadata and phylogenies. We developed PanVA, an interactive visual analytics tool for exploring sequence variants in groups of homologous sequences in their biological context. PanVA is a web application that allows users to explore existing instances or create new ones to visualize their own data. Availability and ImplementationThe PanVA source code is available on GitHub at https://github.com/PanBrowse/PanVA under the GPLv3 License. Documentation and and public demo instances showcasing examples can be accessed at https://panbrowse.github.io/PanVA/.

bioinformatics↗

bioRxiv2024

Implicit Neural Representation of Multi-shell Constrained Spherical Deconvolution for Continuous Modeling of Diffusion MRI

Diffusion magnetic resonance imaging (dMRI) provides insight into the micro and macro-structure of the brain. Multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) models the underlying local fiber orientation distributions (FODs) using the dMRI signal. While generally producing high-quality FODs, MSMT-CSD is a voxel-wise method that can be impacted by noise and produce erroneous FODs. Local models also do not use the spatial correlation between neighboring voxels to increase parameter estimating power. Additionally, voxel-wise methods require interpolation at arbitrary locations outside of voxel centers. These interpolations can be computationally costly or inaccurate, depending on the method of choice. Expanding upon previous work, we apply the implicit neural representation (INR) methodology to the MSMT-CSD model. This results in an unsupervised machine learning framework that generates a continuous representation of a given dMRI dataset. The input of the INR consists of coordinates in the volume, which produce the spherical harmonics coefficients parameterizing an FOD at any desired location. A key characteristic of our model is its ability to leverage spatial correlations in the volume, which acts as a form of regularization. We evaluate the output FODs quantitatively and qualitatively in synthetic and real dMRI datasets and compare them to existing methods.

neuroscience↗

bioRxiv2017

Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analysed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry datasets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We applied HSNE to a study on gastrointestinal disorders and three other available mass cytometry datasets. We found that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional datasets.

bioinformatics↗