Tokarski, E. | Search bioRxiv

bioRxiv2025

BifurcatoR: A Framework for Revealing Clinically Actionable Signal in Variance Masquerading as Noise

BackgroundDisease heterogeneity is a persistent challenge in medicine, complicating both research and treatment. Standard analytical pipelines often assume patient populations are homogeneous, overlooking variance patterns that may signal biologically distinct subgroups. Variance heterogeneity (VH)--including skewness, outliers, and multimodal distributions--offers a powerful but underused lens for detecting latent etiological structures relevant to prognosis and therapeutic response. MethodsA major barrier to VH analysis is the fragmented landscape of available methods, many of which rely on normality assumptions that biological data frequently violate. In addition, existing tools often require programming expertise, and clear guidance on study design considerations--such as sample size and method selection--is lacking. To address these issues, we developed BifurcatoR, an open-source software platform that simplifies the detection, modeling, and interpretation of VH. BifurcatoR integrates simulation-based method evaluation, study design recommendations, and a user-friendly web interface to support VH analysis across a range of data distributions. We benchmarked VH methods through simulation and applied BifurcatoR to two clinical datasets: acute myeloid leukemia (AML) and obesity. ResultsSimulation studies revealed that VH method performance is highly context-specific, varying with distribution shape, mean-variance coupling, and underlying subgroup structure. In AML, BifurcatoR identified two molecularly distinct subgroups with different treatment responses, including an EVI1-high group with significantly poorer prognosis (p < 0.005) among KMT2A-rearranged cases. In a separate study, VH analysis uncovered immunophenotypic subgroups in obesity based on gene-level discordance across monozygotic twin pairs, highlighting latent variation in adipose immune cell composition. ConclusionsVH is not "noise", biological variation without clinical relevance. Instead, VH is a structured signal that can reveal latent and clinically meaningful subtypes. BifurcatoR offers a practical, accessible framework for incorporating VH into biomedical research, with implications for biomarker discovery, patient stratification, and precision medicine.

bioinformatics↗

bioRxiv2019

The Threat of Scream paradigm: A tool for studying sustained physiological and subjective anxiety

Progress in understanding the emergence of pathological anxiety depends on the availability of paradigms effective in inducing anxiety in a simple, consistent and sustained way. Much progress has been made using the Threat-of-Shock paradigm (TOS), which generates anxiety through the delivery of unpredictable electric shocks to participants. However, TOS may be problematic when testing vulnerable populations. Moreover, it is not clear whether anxiety can be sustained throughout experiments of long duration. Here, we bring support for an alternative approach called the Threat-of-Scream paradigm (TOSc), in which the tactile delivery of shocks is replaced by the auditory delivery of distress screams. We report on an one-hour long study (plus its replication) in which participants were exposed to blocks before which they were told that they could hear aversive screams at any time (threat blocks), vs. blocks before which they were told that no scream will be heard (safe blocks). Both the experiment and its replication showed higher subjective reports of anxiety, higher skin conductance level, and positive correlation between the two measures, in threat compared to safe blocks. Anxiety measures were sustained throughout the experiments, suggesting little emotional and physiological habituation. Our results suggest that the delivery of low intensity distress screams can be an efficient, stable and cheap methodology to assess the impact of sustained anxiety on a diversity of cognitive functions and populations. We therefore believe the TOSc will become an essential part of the psychological toolkit, particularly so for researchers interested in the emergence of pathological anxiety.

neuroscience↗