Biology subjects

Clement, L.

Publications and source records attributed to Clement, L..

At least 19 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↗

bioRxiv2026

Online extinction and novelty-triggered recovery of life-long visual memories in navigating ants

Insects exhibit flexible memory processes, yet current models often confine these modulations to discrete, reinforced events. During navigation, animals can learn to associate locations with resources or aversive events. However, such events occur sporadically within an otherwise continuous behavioral process involving latent learning. This raises a fundamental question for insect cognition: can insects continuously update visual memories during navigation? We show that navigating ants (Myrmecia midas) dynamically revalue visual memories en-route, even in the absence of any motivational change or external reinforcement. Using a trackball system that prevented visual progress, we found that nest-view attraction declined over time, reflecting a gradual, online extinction of these otherwise life-long attractive visual memories. These memory updates were view-specific, independent of path integrator state, and persisted after 24-hours, indicating the involvement of long-term memory processes. However, brief exposure to a novel visual scene was sufficient to partially restore nest-views attraction, showing that extinction is labile, and the original memories are preserved. Unlike classic paradigms, visual memory extinction operates here as a continuous, time-dependent process without reward-omission or prediction error; whereas memory reinstatement is triggered by discrete visual changes. We propose that these dynamics are mediated by opponent-processes in the insect mushroom bodies, where temporal estimates of view exposure drive extinction and novel view detection gates memory recovery.

ecology↗

bioRxiv2026

Decline of Common Toad populations in Flanders is not linked to surrounding landscape

AimWe aim to quantify population trends of Common Toad in Flanders and assess to which extent population trends depend on general landscape variables (land use identity, structure and change). LocationFlanders, Belgium MethodsUsing standardized time series obtained from citizen science and spanning over four decades, we used an end-begin contrast to get a trend value for 234 populations. Next, we developed a testing strategy to find associations between these trend values and the surrounding landscape ResultsAcross the study region, 40% of the populations have declined significantly, while only 10% show an increase. Declines were not associated with landscape characteristics in our study area, which is one of the most fragmented regions in Europe. Main conclusionsWe discuss how the research region, spatial and temporal resolution, as well as the generalist nature of the species, may explain these findings. Our study suggests limited effects of general landscape characteristics on the decline of Common Toad populations, indicating that other major drivers are likely responsible.

ecology↗

bioRxiv2025

IsoformSwitchAnalyzeR v2: Analysis of Functional Isoform Changes in Long-read and Single-cell Sequencing Data

Alternative splicing enables a single gene to produce a variety of mRNA transcripts, significantly enhancing protein diversity in higher eukaryotes. Isoform switching refers to the differential usage of a genes transcripts and occurs pervasively across physiological and pathological conditions. IsoformSwitchAnalyzeR was developed to identify these isoform switches and analyze their functional consequences. Advances in RNA-seq technology, including long-read and single-cell sequencing, along with state-of-the-art computational tools, enable unprecedented accuracy in isoform switch identification and its functional consequences, necessitating an update to IsoformSwitchAnalyzeR. Here we present IsoformSwitchAnalyzeR 2.0, with substantial improvements in the robustness of isoform switch detection, the incorporation of new functional annotation types, and interoperability with other bioinformatics tools. We showcase how IsoformSwitchAnalyzeRs standard workflow is now well-suited for analysis of both long-read RNA-seq and single-cell data through two case studies. Specifically, we analyze long-read data from patients with Alzheimers Disease and single-cell data from patients with glioblastoma. In both case studies, we find important isoform switches with disease-relevant functional consequences, showcasing the power of IsoformSwitchAnalyzeR v2. Taken together, these findings highlight the versatility and robustness of IsoformSwitchAnalyzeR in handling advanced sequencing technologies, thereby broadening its applicability across diverse research contexts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/693027v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f0ecd8org.highwire.dtl.DTLVardef@4d7285org.highwire.dtl.DTLVardef@999abeorg.highwire.dtl.DTLVardef@7e3e9d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

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

Strategies for addressing pseudoreplication in multi-patient scRNA-seq data

The rapidly evolving field of single-cell transcriptomics has provided a powerful means for understanding cellular heterogeneity. Large-scale studies with multiple biological samples hold promise for discovering differentially expressed biomarkers with a higher level of confidence through a better characterization of the target population. However, the hierarchical nature of these experiments introduces a significant challenge for downstream statistical analysis. Indeed, despite the availability of numerous differential expression methods, only a select few can accurately address the within-patient correlation of single-cell expression profiles. Furthermore, due to the high computational costs associated with some of these methods, their practical use is limited. In this manuscript, we undertake a comprehensive assessment of different strategies to address the hierarchical correlation structure in multi-sample scRNA-seq data. We employ synthetic data generated from a simulator that retains the original correlation structure of multi-patient data while making minimal assumptions, providing a robust platform for benchmarking method performance. Our analyses indicate that neglecting within-patient correlation jeopardizes type I error control. We show that, in line with some previous reports and in contrast with others, Poisson Generalized Estimation Equations provide a useful and flexible framework for addressing these issues. We also show that pseudobulk approaches outperform single-cell level methods across the board. In this work, we resolve the conflicting results regarding the utility of GEEs and their performance relative to pseudobulk approaches. As such, we provide valuable guidelines for researchers navigating the complex landscape of gene expression modeling, and offer insights on choosing the most appropriate methods based on the specific structure and design of their datasets.

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

Differential detection workflows for multi-sample single-cell RNA-seq data

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

bioinformatics↗

bioRxiv2023

msqrob2PTM: differential abundance and differential usage analysis of MS-based proteomics data at the post-translational modification and peptidoform level

In the era of open-modification search engines, more post-translational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis and ageing. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by the inherently low abundance of many PTMs and the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM, and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both Differential Peptidoform (PTM) Abundance (DPA) and Differential Peptidoform (PTM) Usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform (PTM) intensities, while for DPU, we correct for parent protein abundance by an intermediate normalisation step which calculates the log2-ratio of the peptidoform (PTM) intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

bioinformatics↗

bioRxiv2023

Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses.

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and limitations. Indeed, tools for differential transcript usage or rare disease diagnosis through splicing and expression outliers, either lack performance, discard information, or do not scale to large datasets. We show that replacing normalization offsets unlocks bulk RNA-seq tools for differential usage and aberrant splicing, providing a single framework for various short- and long-read applications. We then introduce saseR, a tool for prioritizing expression and usage outliers that is much faster than state-of-the-art methods, and significantly outperforms these for aberrant splicing detection.

bioinformatics↗

bioRxiv2022

Quality control for the target decoy approach for peptide identification

Reliable peptide identification is key in mass spectrometry (MS) based proteomics. To this end, the target-decoy approach (TDA) has become the cornerstone for extracting a set of reliable peptide-to-spectrum matches (PSMs) that will be used in downstream analysis. Indeed, TDA is now the default method to estimate the false discovery rate (FDR) for a given set of PSMs, and users typically view it as a universal solution for assessing the FDR in the peptide identification step. However, the TDA also relies on a minimal set of assumptions, which are typically never verified in practice. We argue that a violation of these assumptions can lead to poor FDR control, which can be detrimental to any downstream data analysis. We here therefore first clearly spell out these TDA assumptions, and introduce TargetDecoy, a Bioconductor package with all the necessary functionality to control the TDA quality and its underlying assumptions for a given set of PSMs. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/516857v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@fed04borg.highwire.dtl.DTLVardef@11d115dorg.highwire.dtl.DTLVardef@15f1a45org.highwire.dtl.DTLVardef@b5ceab_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

An interactive mass spectrometry atlas of histone posttranslational modifications in T-cell acute leukemia

The holistic nature of omics studies makes them ideally suited to generate hypotheses on health and disease. Sequencing-based genomics and mass spectrometry (MS)-based proteomics are linked through epigenetic regulation mechanisms. However, epigenomics is currently mainly focused on DNA methylation status using sequencing technologies, while studying histone posttranslational modifications (hPTMs) using MS is lagging, partly because reuse of raw data is impractical. Yet, targeting hPTMs using epidrugs is an established promising research avenue in cancer treatment. Therefore, we here present the most comprehensive MS-based preprocessed hPTM atlas to date, including 21 T-cell acute lymphoblastic leukemia (T-ALL) cell lines. We present the data in an intuitive and browsable single licensed Progenesis QIP project and provide all essential quality metrics, allowing users to assess the quality of the data, edit individual peptides, try novel annotation algorithms and export both peptide and protein data for downstream analyses, exemplified by the PeptidoformViz tool. This data resource sets the stage for generalizing MS-based histone analysis and provides the first reusable histone dataset for epidrug development.

cancer biology↗

bioRxiv2021

satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive scRNA-seq data. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs and scaling to scRNA-seq applications.

bioinformatics↗

bioRxiv2020

A lateralised design for the interaction of visual memories and heading representations in navigating ants

The navigational skills of ants, bees and wasps represent one of the most baffling examples of the powers of minuscule brains. Insects store long-term memories of the visual scenes they experience 1, and they use compass cues to build a robust representation of directions 2,3. We know reasonably well how long-term memories are formed, in a brain area called the Mushroom Bodies (MB) 4-8, as well as how heading representations are formed in another brain area called the Central Complex (CX) 9-12. However, how such memories and heading representations interact to produce powerful navigational behaviours remains unclear 7,13,14. Here we combine behavioural experiments with computational modelling that is strictly based on connectomic data to provide a new perspective on how navigation might be orchestrated in these insects. Our results reveal a lateralised design, where signals about whether to turn left or right are segregated in the left and right hemispheres, respectively. Furthermore, we show that guidance is a two-stage process: the recognition of visual memories - presumably in the MBs - does not directly drive the motor command, but instead updates a "desired heading" - presumably in the CX - which in turn is used to control guidance using celestial compass information. Overall, this circuit enables ants to recognise views independently of their body orientation, and combines terrestrial and celestial cues in a way that produces exceptionally robust navigation.

animal behavior and cognition↗

bioRxiv2020

Mating type specific transcriptomic response to sex inducing pheromone in the pennate diatom Seminavis robusta

Sexual reproduction is a fundamental phase in the life cycle of most diatoms. Despite its role as a source of genetic variation, it is rarely reported in nature and its molecular foundations remain largely unknown. Here, we integrate independent transcriptomic datasets, in order to prioritize genes responding to sex inducing pheromones (SIPs) in the pennate diatom Seminavis robusta. We observe marked gene expression changes associated with SIP treatment in both mating types, including an inhibition of S-phase progression, chloroplast division, mitosis and cell wall formation. Meanwhile, meiotic genes are upregulated in response to SIP, including a sexually induced diatom specific cyclin (dsCyc). Our data further suggest an important role for reactive oxygen species, energy metabolism and cGMP signaling during the early stages of sexual reproduction. In addition, we identify several genes with a mating type specific response to SIP, and link their expression pattern with physiological responses such as the production of the attraction pheromone diproline and mate-searching behaviour in MT+. Combined, our results provide a model for early sexual reproduction in pennate diatoms and significantly expand the suite of target genes to detect sexual reproduction events in natural diatom populations.

molecular biology↗

bioRxiv2020

Pitfalls in re-analysis of observational omics studies: a post-mortem of the human pathology atlas.

Uhlen et al. (Reports, 18 august 2017) published an open-access resource with cancer-specific marker genes that are prognostic for patient survival in seventeen different types of cancer. However, their data analysis workflow is prone to the accumulation of false positives. A more reliable workflow with flexible Cox proportional hazards models employed on the same data highlights three distinct problems with such large-scale, publicly available omics datasets from observational studies today: (i) re-analysis results can not necessarily be taken forward by others, highlighting a need to cross-check important analyses with high impact outcomes; (ii) current methods are not necessarily optimal for the re-analysis of such data, indicating an urgent need to develop more suitable methods; and (iii) the limited availability of potential confounders in public metadata renders it very difficult (if not impossible) to adequately prioritize clinically relevant genes, which should prompt an in-depth discussion on how such information could be made more readily available while respecting privacy and ethics concerns.

bioinformatics↗

bioRxiv2019

How do backward walking ants (Cataglyphis velox) cope with navigational uncertainty?

Current opinion in insect navigation assumes that animals need to align with the goal direction to recognise familiar views and approach it. Yet, ants sometimes drag heavy food items backward to the nest and it is still unclear to what extent they rely on visual memories while doing so. In this study displacement experiments and alterations of the visual scenery reveal that ants do indeed recognise and use the learnt visual scenery to guide their path while walking backward. In addition, the results show that backward homing ants estimate their directional certainty by combining visual familiarity with other cues such as their path integrator and the time spent backward. A simple model that combines path integration with repulsive and attractive visual memories captures the results.

animal behavior and cognition↗

bioRxiv2019

The Academic Career Readiness Assessment: Clarifying training expectations for future life sciences faculty

We describe here the development and validation of the Academic Career Readiness Assessment (ACRA) rubric, an instrument that was designed to provide more equity in mentoring, transparency in hiring, and accountability in training of aspiring faculty in the life sciences. We report here the results of interviews with faculty at 20 U.S. institutions which resulted in the identification of 14 qualifications and levels of achievement required for obtaining a faculty position at three groups of institutions: research-intensive (R), teaching-only (T), and research and teaching-focused (RT). T institutions hire candidates on teaching experience and pedagogical practices, and on their ability to serve diverse student populations. RT institutions hire faculty on both research and teaching-related qualifications, as well as on the ability to support students in the laboratory. R institutions hire candidates mainly on their research achievements and potential, which may limit the diversification of the life science academic pathway.

scientific communication and education↗