Biology subjects

Policar, P. G.

Publications and source records attributed to Policar, P. G..

3 recordsLinked to original sources

bioRxiv2025

velotest: Statistical assessment of RNA velocity embeddings reveals quality differences for reliable trajectory visualizations

Contemporary single-cell RNA-sequencing studies typically use RNA velocity analyses to investigate cellular dynamics. Commonly, RNA velocities are assessed with two-dimensional projections. Although it is well established that nonlinear embeddings of gene expression data cannot comprehensively preserve the structure of the original high-dimensional data across scales, there is no tool that systematically assesses and quantifies the faithfulness of velocity embeddings. This situation potentially leads to misleading velocity visualizations and putative cell state transitions. To this end, we introduce velotest, a cell-level statistical test that quantifies the faithfulness of a velocity embedding with respect to its high-dimensional counterpart. Applying velotest across multiple datasets and embedding methods, we find strong differences in the embedding qualities between embedding methods, datasets, and regions in individual datasets. velotest thus provides an essential validation step and enables researchers to select a suitable visualization of high-dimensional velocities, ensuring that hypotheses drawn from velocity projections are grounded in accurately represented dynamics.

bioinformatics↗

bioRxiv2019

openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding

SummaryPoint-based visualisations of large, multi-dimensional data from molecular biology can reveal meaningful clusters. One of the most popular techniques to construct such visualisations is t-distributed stochastic neighbor embedding (t-SNE), for which a number of extensions have recently been proposed to address issues of scalability and the quality of the resulting visualisations. We introduce openTSNE, a modular Python library that implements the core t-SNE algorithm and its extensions. The library is orders of magnitude faster than existing popular implementations, including those from scikit-learn. Unique to openTSNE is also the mapping of new data to existing embeddings, which can surprisingly assist in solving batch effects.\n\nAvailabilityopenTSNE is available at https://github.com/pavlin-policar/openTSNE.\n\nContactpavlin.policar@fri.uni-lj.si, blaz.zupan@fri.uni-lj.si

bioinformatics↗

bioRxiv2019

Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification

Dimensionality reduction techniques, such as t-SNE, can construct informative visualizations of high-dimensional data. When working with multiple data sets, a straightforward application of these methods often fails; instead of revealing underlying classes, the resulting visualizations expose data set-specific clusters. To circumvent these batch effects, we propose an embedding procedure that takes a t-SNE visualization constructed on a reference data set and uses it as a scaffold for embedding new data. The new, secondary data is embedded one data-point at the time. This prevents any interactions between instances in the secondary data and implicitly mitigates batch effects. We demonstrate the utility of this approach with an analysis of six recently published single-cell gene expression data sets containing up to tens of thousands of cells and thousands of genes. In these data sets, the batch effects are particularly strong as the data comes from different institutions and was obtained using different experimental protocols. The visualizations constructed by our proposed approach are cleared of batch effects, and the cells from secondary data sets correctly co-cluster with cells from the primary data sharing the same cell type.

bioinformatics↗