Biology subjects

Vanderaa, C.

Publications and source records attributed to Vanderaa, C..

7 recordsLinked to original sources

bioRxiv2026

gamdid: generalized additive models for differential distributions in single cell experiments

Single-cell proteomics (SCP) generates protein abundance measurements across hundreds to thousands of individual cells, offering unprecedented resolution to study cellular heterogeneity. However, existing differential abundance (DA) methods are limited to detecting shifts in mean expression, leaving biologically relevant differences in shape undetected. Indeed, the specific power of SCP is to identify differences between individual cells in a population, which are typically only found as shape differences rather than in mean expression. We here therefore present gamdid (generalized additive models for differential distributions), a novel statistical framework and R package for differential distribution (DD) analysis in SCP data. gamdid is based on generalized additive models (GAMs) to flexibly model heterogeneous distributions, perform inference and provide interpretable visualizations. Through semi-synthetic benchmarking on two SCP datasets, gamdid demonstrates conservative false discovery rate control and substantially outperforms competing methods for differences in shape, while achieving comparable performance for mean shifts. A spike-in case study further demonstrates the utility of gamdid and its interpretable visualization. Uniquely among DD methods, gamdid supports omnibus testing across more than two groups, with post-hoc pairwise comparisons via stagewise testing, and is specifically tailored for proteomics abundance data.

genomics↗

bioRxiv2025

omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation applied to bulk- and single cell proteomics data.

The unprecedented speed and sensitivity of mass spectrometry (MS) unlocked large-scale applications of proteomics and even enabled proteome profiling of single cells. However, this fast-evolving field is hindered by a lack of scalable dimensionality reduction tools that can compensate for substantial batch effects and missingness across MS runs. Therefore, we present omicsGMF, a fast, scalable, and interpretable matrix factorization method, tailored for bulk and single-cell proteomics data. Unlike current workflows that sequentially apply imputation, batch correction, and principal component analysis, omicsGMF integrates these steps into a unified framework, dramatically enhancing data processing and dimensionality reduction. Additionally, omicsGMF provides robust imputation of missing values, outperforming bespoke state-of-the-art imputation tools. We further demonstrate how this integrated approach increases statistical power to detect differentially abundant proteins in the downstream data analysis. Hence, omicsGMF is a highly scalable approach to dimensionality reduction in proteomics, that dramatically improves many important steps in proteomics data analysis.

bioinformatics↗

bioRxiv2024

msqrob2TMT: robust linear mixed models for inferring differential abundant proteins in labelled experiments with arbitrarily complex design

Labelling strategies in mass spectrometry (MS)-based proteomics enable increased sample throughput by acquiring multiplexed samples in a single run. However, contemporary designs often require the acquisition of multiple runs, leading to a complex correlation structure. Addressing this correlation is key for correct statistical inference and reliable biomarker discovery. Therefore, we present msqrob2TMT, a set of mixed model-based workflows tailored toward differential abundance analysis for labelled MS-based proteomics data. Thanks to its increased flexibility, msqrob2TMT can model both sample-specific and feature-specific (e.g. peptide or protein) covariates, which unlocks the inference to experiments with arbitrarily complex designs as well as to correct explicitly for feature-specific properties. We benchmark our novel workflows against the state-of-the-art tools MSstatsTMT and DeqMS in a spike-in study. We show that our workflows are modular, more flexible and have improved performance by adopting robust ridge regression. We also found that reference channel normalization and imputation can have a deleterious impact on the statistical outcome. Finally, we demonstrate the significance of msqrob2TMT on a real-life mice study, showcasing the importance of effectively accounting for the hierarchical correlation structure in the data.

bioinformatics↗

bioRxiv2023

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data

Analysing mass spectrometry (MS)-based single-cell proteomics (SCP) data is challenging. The data analysis must address numerous problems that are inherent to both MS-based proteomics technologies and single-cell experiments. This has led to the development of complex and divergent data processing workflows within the field. In this work, we present scplainer, a principled and standardised approach for extracting meaningful insights from SCP data. The approach relies on minimal data processing combined with linear modelling. The approach is a simple yet powerful approach for exploring and interpreting various types of SCP data. scplainer performs variance analysis, differential abundance analysis and component analysis while streamlining the visualization of the results. This thorough exploration enhances our capacity to gain a deeper understanding of the biological processes hidden in the data. Finally, we demonstrate that scplainer corrects for technical variability, and even enables the integration of data sets from different SCP experiments. The approach effectively generates high-quality data that are amenable to perform downstream analyses. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.

bioinformatics↗

bioRxiv2022

Identification and implication of tissue-enriched ligands in epithelial-endothelial crosstalk during pancreas development

Development of the pancreas is driven by an intrinsic program coordinated with signals from other cell types in the epithelial environment. These intercellular communications have been so far challenging to study because of the low concentration, localized production and diversity of the signals released. Here, we combined scRNAseq data with a computational interactomic approach to identify signals involved in the reciprocal interactions between the various cell types of the developing pancreas. This in silico approach yielded 40,607 potential ligand-target interactions between the different main pancreatic cell types. Among this vast network of interactions, we focused on three ligands potentially involved in communications between epithelial and endothelial cells. Bmp7 and Wnt7b, expressed by pancreatic epithelial cells and predicted to target endothelial cells, and Sema6d, involved in the reverse interaction. In situ hybridization confirmed the localized expression of Bmp7 in the pancreatic epithelial tip cells and of Wnt7b in the trunk cells. On the contrary, Sema6d was enriched in endothelial cells. Functional experiments on ex vivo cultured pancreatic explants indicated that tip cell-produced Bmp7 restrained development of endothelial cells. This work identified ligands with a restricted tissular and cellular distribution and highlighted the role of Bmp7 in the intercellular communications shaping vessel development during pancreas organogenesis.

developmental biology↗

bioRxiv2021

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductors Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data. ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author SummaryExperimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data.

bioinformatics↗

bioRxiv2021

Utilizing scp for the analysis and replication of single-cell proteomics data

IntroductionMass spectrometry-based proteomics is actively embracing quantitative, single-cell level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and built from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses. Areas coveredThis special report is the first step toward the formalization and standardization of SCP data analysis. scp, the software that accompanies this work, successfully replicates one of the landmark SCP studies and is applicable to other experiments and designs. We created a repository containing the replicated workflow with comprehensive documentation in order to favor further dissemination and improvements of SCP data analyses. Expert opinionReplicating SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlight the intimate dependence that exists between batch effects and data missingness and offer avenues for dealing with these exciting challenges. 1 Article highlightsO_LISingle-cell proteomics (SCP) is emerging thanks to several recent technological advances, but further progress is still lagging due to the lack of principled and systematic data analysis. C_LIO_LIThis work offers a standardized solution for the processing of SCP data demonstrated by the replication of a landmark SCP work. C_LIO_LITwo important challenges remain: batch effects and data missingness. Furthermore, these challenges are not independent and therefore need to be modeled simultaneously. C_LI

bioinformatics↗