Biomedical subjects

Axel Martinelli

Publications and source records attributed to Axel Martinelli.

3 recordsLinked to original sources

PubMed2025

PLAID: ultrafast single-sample gene set enrichment scoring.

SUMMARY: In recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling the identification of unique molecular profiles in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. To address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. PLAID uniquely integrates the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. AVAILABILITY AND IMPLEMENTATION: PLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid.

Algorithms↗

PubMed2025

NPM: latent batch effects correction of omics data by nearest-pair matching.

MOTIVATION: Batch effects (BEs) are a predominant source of noise in omics data and often mask real biological signals. BEs remain common in existing datasets. Current methods for BE correction mostly rely on specific assumptions or complex models, and may not detect and adjust BEs adequately, impacting downstream analysis and discovery power. To address these challenges we developed NPM, a nearest-neighbor matching-based method that adjusts BEs and may outperform other methods in a wide range of datasets. RESULTS: We assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPM demonstrates the ability in correcting for BEs, while preserving biological differences. It may outperform other methods based on multiple metrics. Altogether, NPM proves to be a valuable BE correction approach to maximize discovery in biomedical research, with applicability in clinical research where latent BEs are often dominant. AVAILABILITY AND IMPLEMENTATION: NPM is freely available on GitHub (https://github.com/bigomics/NPM) and on Omics Playground (https://bigomics.ch/omics-playground). Computer codes for analyses are available at (https://github.com/bigomics/NPM). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, and GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.

Humans↗

PubMed2005

Linkage group selection: rapid gene discovery in malaria parasites.

The identification of parasite genes controlling phenotypes such as drug resistance, virulence, immunogenicity, and transmission is vital to malaria research. Classical genetic methods have achieved these goals only rarely and with difficulty. We describe here a novel genetic method, Linkage Group Selection (LGS), which achieves rapid de novo location of genes encoding selectable phenotypes of malaria parasites. A phenotype-specific selection pressure is applied to the uncloned progeny of a genetic cross between two malaria parasites that differ in the relevant phenotype. Selected and unselected progeny are analyzed using genome-wide quantitative genetic markers. Markers of the "sensitive" parent, which are reduced after selection, are sequenced and located in genomic databases. They are expected to be closely linked to gene(s) determining the phenotype under selection. We have validated LGS with the rodent malaria parasite Plasmodium chabaudi chabaudi using a phenotype, pyrimethamine resistance, whose controlling gene, that encoding dihydrofolate reductase (dhfr), is known. We show that molecular markers closely linked to dhfr, and only those linked to this gene, were reduced or removed by pyrimethamine treatment in accordance with the expectations of LGS.

Animals↗