Summary High-throughput transcriptomics has made gene signatures central to interpreting gene expression data, with applications in diagnosis, prognosis, and prediction. Quantifying signature activity and assessing its robustness remain challenging because scoring methods primarily rely on various assumptions, and no single approach is universally optimal. Here, we present pysigscore, a Python framework for gene set scoring in bulk and single-cell RNA-seq data. pysigscore integrates 18 built-in scoring methods with a fully customisable scorer, allowing users to define and benchmark new scoring functions. It also provides reliability analyses, including p-value estimation and leave-one-out experiments, to assess the significance of scores and gene-level contributions. We validated pysigscore on the CCLE, TCGA, and PBMC datasets, recovering the expected enrichment in liver, hypoxia, inflammatory, and cell-cycle signatures. Availability and Implementation Source code is available at https://github.com/bioinformatics-hub/pysigscore. Contact: tommaso.giacomello@phd.unibocconi.it, francesca.buffa@unibocconi.it Supplementary information Supplementary data are available at Bioinformatics online.
MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer–based statistics to detect biological variation without requiring alignment, is presented.
L. Mboning, Maciej Dlugosz, Marek Kokot et al.· bioRxiv· 0 citations
Abstract Motivation Inferring gene networks from single-cell RNA sequencing data is challenging due to high sparsity, dimensionality, and technical noise. Current pipelines lack the multi-dataset integration and comprehensive post-processing analysis. Results scGraphVerse is an R package that integrates multiple algori...
Francesco Cecere, D. De Canditiis, Annamaria Carissimo et al.· Bioinformatics Advances· 0 citations
Bulk and single-cell RNA sequencing (scRNA-seq) have become essential for investigating disease mechanisms and identifying diagnostic biomarkers. However, the growing volume of transcriptomic data remains difficult to reuse efficiently for many researchers. Downstream analysis often requires multiple statistical, visua...
Summary Single-sample transcriptomic analysis can provide gene-level views of how individual tumors deviate from matched reference states, complementing cohort-level differential expression. Here, we present HopRatio, a rank-based framework that quantifies, for each gene in each sample, the fraction of stable reference...
Yue Zhao, Bo Gao, Rui-Zhong Chen· iScience· 0 citations
This study presents a modular analytics pipeline that combines publicly available datasets with established open-source tools; standardizes quality control, differential expression analysis, and pathway analysis; and leverages competitive machine learning to unify disparate RNA-seq datasets for robust biomarker identif...
Cheryl L. Sesler, Lukasz S. Wylezinski, Guzel I. Shaginurova et al.· Journal of Molecular Diagnos...· 0 citations
This work introduces a two-stage statistical framework for interpretable patient-level disease classification from single-cell data, and recovered biologically coherent, cell-type specific gene signatures consistent with known disease mechanisms, demonstrating improved interpretability without sacrificing predictive ac...
Zhi-Wei Xiao, William Torous, Jeffrey B. Cheng et al.· bioRxiv· 0 citations
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