Biology subjects

Charrout, M.

Publications and source records attributed to Charrout, M..

3 recordsLinked to original sources

bioRxiv2025

Mass cytometry data integration methods reveal rural-urban gradient of immune profiles across geography

The human immune system strongly varies across populations and is shaped by a wide range of host and environmental factors. As such, a rural compared to urban lifestyle has previously been associated with baseline differences in immune profiles, which can impact outcome of vaccination. The question whether there is a immune signature that associates with rural living irrespective of ancestry or geography has been addressed here along with which analytical methods are most suitable. Using three mass cytometry datasets, we studied shared and population-specific immune characteristics of healthy rural- or urban-living Indonesian, Senegalese and Tanzanian adults and urban-living Europeans. After harmonized preprocessing and quality control, 75.4 million cells were integrated using four different data-integration methods. Among these, CytoNorm with in silico references performed best, revealing shared differences in the differentiation state within the lymphocyte compartment that distinguishes rural from urban-living individuals. Differentiated CD4+ T cells expressing CTLA-4, PD-1 and ICOS characterized rural living, as did CD4+ T cells with high CD161 expression in combination with CRTH2 and GATA3. In the innate compartment, CD56- CD16 NK cells with low CD38 and CD11c expression were expanded in rural living subjects. Together, our results demonstrate that, using a harmonized analytical pipeline and machine learning-based integration, large-scale mass cytometry data can be combined across diverse cohorts to identify shared immunological hallmarks of rural living. These signatures may provide targets for modulating immune responses in future vaccine studies.

immunology↗

bioRxiv2023

Benchmarking Variational AutoEncoders on cancer transcriptomics data

Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream task of cluster agreement with cancer subtypes. We studied the effect of the latent space dimensionality, learning rate, optimizer and initialization on the quality of subsequent clustering of the TCGA samples. We found {beta}-TCVAE and DIP-VAE to have a good performance, on average, despite being more sensitive to hyperparameters selection. Based on these experiments, we derived recommendations for selecting the different hyperparameters settings. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we correlated the different representations with various data characteristics such as age, days to metastasis, immune infiltration, and mutation signatures. We found that for all models the latent factors, in general, do not uniquely correlate with one of the data characteristics even for models specifically designed for disentanglement.

bioinformatics↗

bioRxiv2020

Untangling biological factors influencing trajectory inference from single cell data

Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity towards the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unravelling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajetory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation. Significance StatementPseudotime inference is a bioinformatics tool used to characterize and understand the role and activity of genes involved in cell differentiation. To achieve this, the level of expression of thousands of genes are simultaneously used to order cells along a developmental axis. However, this may result in distorted trajectories as many genes are not necessary involved in cell differentiation, and might even provide the pseudotime inference tool with conflicting (confounding) information. Here we present a methodology for improving inference of the differentiation trajectories by restricting it to a small set of genes assumed to regulate cell differentiation.

bioinformatics↗