Lück, N. | bioRxiv Science

bioRxiv2026

StabCell: Stability selection for clustering and marker detection in single-cell RNA sequencing

MotivationConventional pipelines for differential expression analysis in single-cell RNA sequencing (scRNA-seq) data first cluster individual cells and then test for differentially expressed genes between the resulting clusters. Using the same data for clustering and testing, however, poses a selective inference problem and can result in overconfidence in differences that may not reflect true biological variation. ResultsWe introduce StabCell, a stability selection framework which integrates clustering and detection of differentially expressed marker genes. By repeatedly performing clustering and differential expression analysis on complementary random subsamples, StabCell assesses clustering and marker stability, yielding a stable clustering with sets of stable marker genes. In simulations, we demonstrate that StabCell provides approximate empirical per-family error rate (PFER) control, selecting fewer false positive marker genes compared with conventional approaches, especially in cases with low signal-to-noise ratio and low sequencing depth. Applying the method to a cell differentiation dataset from induced pluripotent stem cells (IPSCs) to cardiomyocytes reveals that meaningful marker genes are consistently among the top-ranked genes. These results indicate that StabCell can improve the interpretability and robustness of scRNA-seq analyses. Availability and implementationAn implementation of StabCell in the statistical programming language R is available at https://github.com/LuckyLueck/StabCell. Code to reproduce the results is available at https://github.com/LuckyLueck/StabCell_paper.

bioinformatics↗

bioRxiv2022

SpaCeNet: Spatial Cellular Networks from omics data

Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells together with the cells spatial distribution provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. "SpaCeNet" is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence relations between captured variables within individual cells and by disentangling these from conditional independence relations between variables of different cells. A python implementation of SpaCeNet is publicly available at https://github.com/sschrod/SpaCeNet.

bioinformatics↗