Omar, M. N. | bioRxiv Science

bioRxiv2026

sctrial: Participant-Level Differential Analysis for Longitudinal Single-Cell Experiments

Longitudinal single-cell RNA sequencing studies in clinical trials and translational cohorts offer a powerful view of treatment response, disease progression, and cellular dynamics, but their hierarchical structure poses a major inferential challenge: thousands of cells are measured within the same participants across repeated time points, whereas the participant, not the cell, is the true unit of biological replication. Conventional cell-level workflows can therefore yield inflated significance and misleading confidence in reported associations. Here, we present sctrial, an open source analytical framework for repeated-measures single-cell studies that uses design-specific participant-level estimands, including difference-in-differences for two-group longitudinal comparisons, and small-cluster-aware uncertainty quantification. In simulation benchmarks using a hierarchical gamma-Poisson generative model, sctrial maintained well-calibrated error rates in mixed-signal gene panels where established multi-subject methods showed inflated false positive rates among unaffected genes. We applied sctrial to five independent datasets spanning melanoma immunotherapy, COVID-19 severity, BNT162b2 vaccination, AML chemotherapy, and CAR-T therapy. Across these studies, sctrial identified immune programs whose direction and magnitude differed across therapeutic and disease contexts, while benchmarking analyses showed that many associations highlighted by conventional cell-level workflows were attenuated or no longer supported when inference was performed at the participant level. These analyses illustrate how participant-aware inference can reduce pseudoreplication-driven signal inflation and provide a more rigorous basis for interpreting longitudinal single-cell data. sctrial enables reproducible participant-level analysis of longitudinal single-cell experiments and facilitates more reliable biological interpretation in translational and clinical studies. The software is implemented in Python and compatible the AnnData/scverse ecosystem.

bioinformatics↗

bioRxiv2019

Large scale differential gene expression analysis identifies genes associated with Bipolar Disorder

Background and purposeBipolar disorder (BD) is a common psychiatric disorder with high morbidity and mortality. Several polymorphisms have been found to be implicated in the pathogenesis of BD, however, these loci have small effect sizes that fail to explain the high heritability of the disease. Here, we provide more insights into the genetic basis of BD by identifying the differentially expressed genes (DEGs) and their associated pathways and biological processes in post-mortem brain tissues of patients with BD.\n\nMethodsEight datasets were eligible for the differential expression analysis. We used six datasets for the discovery of the gene signature and used the other two for independent validation. We performed the multi-cohort analysis by a random-effect model using R and MetaIntegrator package.\n\nResultsThe initial analysis resulted in the identification of 126 DEGs (30 up-regulated and 94 down-regulated). We refined this initial signature by a forward search process and resulted in the identification of 22 DEGs (6 up-regulated and 16 down-regulated). We validated the final gene signature in the independent datasets and resulted in an Area Under the ROC Curve (AUC) of 0.756 and 0.76, respectively. We performed gene set enrichment analysis (GSEA) which identified several biological processes and pathways related to BD including Ca transport, inflammation and DNA damage response.\n\nConclusionOur findings support the previous findings that link BD pathogenesis to abnormalities in glial inflammation and calcium transport and also identify several other biological processes not previously reported to be associated with the development of the disease. Such findings will improve our understanding of the genetic basis underlying BD and may have future clinical implications.

bioinformatics↗