Biology subjects

Neuvial, P.

Publications and source records attributed to Neuvial, P..

5 recordsLinked to original sources

bioRxiv2026

cit_gsa: gene set analysis for scRNA-seq with covariate adjustment

Single-cell RNA-seq enables gene expression analysis at single-cell resolution. Differential Expression Analysis (DEA) performed at the gene set level for better power and interpretation, identifies gene sets associated with a factor of interest. Existing single-cell DEA methods rely on two-step approaches, a gene-level analysis followed by an enrichment step, are often limited to two-group comparisons and do not support covariate adjustment. We introduce cit_gsa, a novel one-step self-contained gene set DEA method for Single-cell RNA-seq that handles more than two conditions and allows covariate adjustment. A benchmark of cit_gsa against state-of-the-art methods shows that our method has better performance in complex scenarios, and real-data analysis confirms that covariate adjustment improves biological interpretation.

genomics↗

bioRxiv2022

Statistical tests to detect differences between codon-specific Ramachandran plots

In their recent work, Rosenberg et al. [1] studied the dependence between the identity of synonymous codons and the distribution of the backbone dihedral angles of the translated amino acids. In the past, it has been shown that the use of synonymous codons is highly relevant in multiple biological processes including, among others, mRNA splicing, translational rates and protein folding [2, 3]. While the correlation between synonymous codons and secondary structure in translated proteins has been widely studied [4-6], Rosenberg et al. evaluated the effect of codon identity on a finer scale, analyzing whether the distribution of ({phi}, {psi}) dihedral angles within secondary structure elements is significantly altered when synonymous codons are used. Their conclusion, showing significant differences, particularly for amino acid residues involved in {beta}-strands, would represent a new paradigm for the role played by synonymous codons in defining protein structure. However, the statistical methodology used in that study was formally incorrect, casting doubt on the obtained results. Besides, it is based on density estimates that might be imprecise for small sample sizes, yielding misleading comparisons. These methodological errors are described in the following section. Then, using an appropriate methodology, we reanalyzed the data presented in [1]. Our results show that the influence of the codon on the distribution of the dihedral angles is statistically non-significant for all types of secondary structures, contradicting the conclusion by Rosenberg et al.. These results were corroborated by repeating the analysis on structures extracted from the AlphaFold Database [7, 8] for the same set of proteins, and shown to be robust with respect to the definition secondary structural classes and also when considering the nature of the neighbor residues. Overall, our observations demonstrate that the influence of the synonymous codons on the backbone dihedral angles can not be inferred with current data.

bioinformatics↗

bioRxiv2022

WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins

The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, being one ensemble characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.

bioinformatics↗

bioRxiv2022

Powerful and interpretable control of false discoveries in differential expression studies

MotivationThe standard approach for statistical inference in differential expression (DE) analyses is to control the False Discovery Rate (FDR). However, controlling the FDR does not in fact imply that the proportion of false discoveries is upper bounded. Moreover, no statistical guarantee can be given on subsets of genes selected by FDR thresholding. These known limitations are overcome by post hoc inference, which provides guarantees of the number of proportion of false discoveries among arbitrary gene selections. However, post hoc inference methods are not yet widely used for DE studies. ResultsIn this paper, we demonstrate the relevance and illustrate the performance of adaptive interpolation-based post hoc methods for DE studies. First, we formalize the use of permutation-based methods to obtain sharp confidence bounds that are adaptive to the dependence between genes. Then, we introduce a generic linear time algorithm for computing post hoc bounds, making these bounds applicable to large-scale DE studies. The use of the resulting Adaptive Simes bound is illustrated on a RNA sequencing study. Comprehensive numerical experiments based on real microarray and RNA sequencing data demonstrate the statistical performance of the method. AvailabilityA cross-platform open source implementation within the R package sanssouci is available at https://pneuvial.github.io/sanssouci/.

bioinformatics↗

bioRxiv2019

Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

The development of single cell transcriptomic technologies yields large datasets comprising multimodal informations such as transcriptomes and immunophenotypes. Currently however, there is no software to easily and simultaneously analyze both types of data. Here, we introduce Single-Cell Virtual Cytometer, an open-source software for flow cytometry-like visualization and exploration of multi-omics single cell datasets. Using an original CITE-seq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of transcriptomes and phenotypes of functional maturation in peripheral T lymphocytes from healthy donors. So this free and open-source algorithm constitutes a unique resource for biologists seeking for a user-friendly analytic tool for multimodal single cell datasets.

bioinformatics↗