Biology subjects

Ouardini, K.

Publications and source records attributed to Ouardini, K..

4 recordsLinked to original sources

bioRxiv2026

Comprehensive benchmarking of batch integration methods for spatial transcriptomics using a large-scale cancer atlas

Spatial transcriptomics (ST) enables spatially-resolved gene expression measurement, providing insights into tissue architecture and disease biology. However, batch effects from sequencing protocols, sample processing, and other technical factors can confound biological signals. Although batch correction has been extensively studied in single-cell transcriptomics, spatial integration methods lack rigorous benchmarking on large real-world datasets. This study benchmarks 11 representation-learning methods across three categories--linear, graph-based and probabilistic methods using Owkins MOSAIC Window dataset, a large-scale spatial transcriptomics atlas of human cancers. Methods are evaluated across three criteria: batch correction, biological conservation, and spatial conservation. We also propose a new integration metric to assess robustness of representations to domain shifts and generalizability to unseen samples. Probabilistic methods (scVIVA, scVI) outperform linear and graph-based approaches in batch correction and biological conservation. On the other hand Graph-based methods excelled at spatial conservation but underperformed in batch integration. Out-of-distribution evaluation reveals that sophisticated methods show reduced peformance on unseen samples while linear methods maintain robust generalization, highlighting trade-offs between integration quality and generalizability that should guide method selection for real-world applications.

cancer biology↗

bioRxiv2025

Joint probabilistic modeling of pseudobulk and single-cell transcriptomics enables accurate estimation of cell type composition

Bulk RNA sequencing provides an averaged gene expression profile of the numerous cells in a tissue sample, obscuring critical information about cellular heterogeneity. Computational deconvolution methods can estimate cell type proportions in bulk samples, but current approaches can lack precision in key scenarios due to simplistic statistical assumptions, limited modeling of cell-type heterogeneity and poor handling of rare populations. We present MixupVI, a deep generative model that learns representations of single-cell transcriptomic data and introduces a mixup-based regularization to enable reference-free deconvolution of bulk samples. Our method creates a latent representation with an additive property, where the representation of a pseudobulk sample corresponds to the weighted sum of its constituent cell types. We demonstrate how MixupVI enables accurate estimation of cell type proportions through benchmarking on pseudobulks simulated from a large immune single-cell atlas. To support reproducibility and foster progress in the field, we also release PyDeconv, a Python library that implements multiple state-of-the-art deconvolution algorithms and provides a comprehensive benchmark on simulated pseudobulk datasets.

bioinformatics↗

bioRxiv2024

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets, and assess their predictive power for survival and gene essentiality predictions. We demonstrate that non DL-based baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pre-training are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

bioinformatics↗

bioRxiv2021

Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data

AO_SCPLOWBSTRACTC_SCPLOWNovel experimental assays now simultaneously measure lineage relationships and transcriptomic states from single cells, thanks to CRISPR/Cas9-based genome engineering. These multimodal measurements allow researchers not only to build comprehensive phylogenetic models relating all cells but also infer transcriptomic determinants of consequential subclonal behavior. The gene expression data, however, is limited to cells that are currently present ("leaves" of the phylogeny). As a consequence, researchers cannot form hypotheses about unobserved, or "ancestral", states that gave rise to the observed population. To address this, we introduce TreeVAE: a probabilistic framework for estimating ancestral transcriptional states. TreeVAE uses a variational autoencoder (VAE) to model the observed transcriptomic data while accounting for the phylogenetic relationships between cells. Using simulations, we demonstrate that TreeVAE outperforms benchmarks in reconstructing ancestral states on several metrics. TreeVAE also provides a measure of uncertainty, which we demonstrate to correlate well with its prediction accuracy. This estimate therefore potentially provides a data-driven way to estimate how far back in the ancestor chain predictions could be made. Finally, using real data from lung cancer metastasis, we show that accounting for phylogenetic relationship between cells improves goodness of fit. Together, TreeVAE provides a principled framework for reconstructing unobserved cellular states from single cell lineage tracing data.

bioinformatics↗