Biology subjects

Virshup, I.

Publications and source records attributed to Virshup, I..

6 recordsLinked to original sources

bioRxiv2025

anndataR improves interoperability between R and Python in single-cell transcriptomics

SummaryMany single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. AvailabilityanndataRs source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241. Contactrobrecht.cannoodt@gmail.com

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bioRxiv2023

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, handling uni- and multi-modal spatial omics datasets remains a challenge owing to large volumes of data, heterogeneous data types and the lack of unified spatially-aware data structures. Here, we introduce SpatialData, a framework that establishes a unified and extensible multi-platform file-format, lazy representation of larger-than-memory data, transformations, and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated via multiple vignettes, including integrative analysis on a multi-modal Xenium and Visium breast cancer study.

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bioRxiv2023

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of featureobservation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple listlike schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

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bioRxiv2021

anndata: Annotated data

anndata is a Python package for handling annotated data matrices in memory and on disk (github.com/theislab/anndata), positioned between pandas and xarray. anndata offers a broad range of computationally efficient features including, among others, sparse data support, lazy operations, and a PyTorch interface. Statement of needGenerating insight from high-dimensional data matrices typically works through training models that annotate observations and variables via low-dimensional representations. In exploratory data analysis, this involves iterative training and analysis using original and learned annotations and task-associated representations. anndata offers a canonical data structure for book-keeping these, which is neither addressed by pandas (McKinney, 2010), nor xarray (Hoyer & Hamman, 2017), nor commonly-used modeling packages like scikit-learn (Pedregosa et al., 2011).

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bioRxiv2021

Squidpy: a scalable framework for spatial single cell analysis

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides both infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.

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bioRxiv2020

constclust: Consistent Clusters for scRNA-seq

1Unsupervised clustering to identify distinct cell types is a crucial step in the analysis of scRNA-seq data. Current clustering methods are dependent on a number of parameters whose effect on the resulting solutions accuracy and reproducibility are poorly understood. The adjustment of clustering parameters is therefore ad-hoc, with most users deviating minimally from default settings. constclust is a novel meta-clustering method based on the idea that if the data contains distinct populations which a clustering method can identify, meaningful clusters should be robust to small changes in the parameters used to derive them. By reconciling solutions from a clustering method over multiple parameters, we can identify locally robust clusters of cells and their corresponding regions of parameter space. Rather than assigning cells to a single partition of the data set, this approach allows for discovery of discrete groups of cells which can correspond to the multiple levels of cellular identity. Additionally constclust requires significantly fewer computational resources than current consensus clustering methods for scRNA-seq data. We demonstrate the utility, accuracy, and performance of constclust as part of the analysis workflow. constclust is available at https://github.com/ivirshup/constclust1.

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