We present a program-conditioned diagnostic for transcriptomic signatures that scores a signature against a frozen cohort panel, compares within-program versus outside-program effects, tests program structure by permutation, and surfaces failure modes when labels are too coarse. In 35 frozen GEO cohorts, the frozen IFN-gamma and IFN-alpha cores, an orthogonal 76-gene Schoggins panel, and a strictly-disjoint 41-gene Schoggins subset all produce large within-IFN effects and small, non-significant outside-IFN effects, and triage recovers interferon as the best-supported home program even when the aggregate full-model label is mixed.
Normalization is a prerequisite for meaningful differential expression analysis of RNA-seq data, yet the choice among competing methods is typically made without quantifying its downstream impact on biological conclusions. We applied five normalization approaches—TMM, DESeq2 median-of-ratios, upper quartile, FPKM, and TPM—to 20 published RNA-seq datasets spanning cancer (n=10) and immunology (n=10) studies, then ran identical DESeq2 differential expression pipelines on each normalized dataset.
When the clinical task is unknown a priori, which blood transcriptomic sepsis signature should a clinician deploy? Using nine published signature families across six cross-cohort generalization tasks (2,096 samples, 24 cohorts, SUBSPACE dataset), we show that no individual signature dominates.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Failure Mode**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Failure Mode**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we term the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we term the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
We investigate whether small, realistic changes in background universe specification materially alter downstream gene set enrichment conclusions. Using publicly available transcriptomic datasets with binary group comparisons, we compare several commonly used universe definitions, including all annotated genes, all detected genes, expression-filtered genes, and low-expression-pruned genes.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
Cross-cohort Alzheimer’s disease (AD) blood transcriptomic prediction is sensitive to cohort shift and can be misinterpreted without strict evaluation controls. We present an open reproducible study on GEO cohorts GSE63060 and GSE63061 with three design principles: leakage-safe target holdout evaluation, consistent permutation-null reporting, and explicit biological feature ablations using open AMP-AD Agora nominated targets.
Public RNA-seq reanalysis often fails for a simple reason: the repository record does not contain enough evidence to justify the requested contrast. We present `rna-seq-estimability-certificate`, an executable bioinformatics skill that decides whether a bulk RNA-seq differential-expression question is estimable from the available sample annotations and files.
Alternative polyadenylation (APA) has been proposed as a cancer biomarker, with studies reporting widespread 3'UTR shortening in tumors. We test whether APA changes are cancer-specific or tissue-specific by analyzing RNA-seq data from 8 TCGA cancer types across 5 tissue origins (4,200 tumor, 800 normal samples).
Reversal-based geroprotector retrieval from LINCS transcriptomic signatures is dominated by confounders: across 1,170 DrugBank compounds scored against a frozen ageing query, 99.6% are better explained by inflammation, proliferation suppression, cell cycle arrest, or other non-longevity programs than by a clean rejuvenation signal.
Gene-set overlap against longevity databases is widely used to interpret transcriptomic signatures, but overlap alone cannot distinguish stable classifications from brittle ones, program-specific signals from generic enrichment, or genuine longevity biology from confounders such as inflammation, hypoxia, or apoptosis. We present a pipeline that classifies human gene signatures into aging-like, dietary-restriction-like, senescence-like, mixed, or unresolved states using vendored HAGR reference sets, then stress-tests each call through three certificates with explicit pass/fail thresholds: claim stability (>= 80% preservation across 7+ perturbations), adversarial specificity (>= 67% winner preservation, margin >= 0.
Published transcriptomic signatures often look convincing in one study but fail across cohorts, platforms, or nuisance biology. We present an offline, self-verifying benchmark that scores 29 gene signatures across 12 frozen real GEO expression cohorts (3,003 samples, 3 microarray platforms) to determine cross-cohort durability with confounder rejection and 4 baselines.
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and transcriptomic landscapes. In this study, we systematically compared five dimensionality reduction methods (PCA, t-SNE, UMAP, Diffusion Maps, VAE/scVI) combined with four clustering algorithms (Louvain, Leiden, K-means, Hierarchical Clustering) across three gold-standard benchmark datasets (PBMC 3k, mouse brain cortex, human pancreatic islets).
Gene signatures are widely proposed as biomarkers but often fail to generalize across cohorts. We present SignatureTriage, a deterministic workflow that evaluates whether a candidate gene signature represents a durable cross-dataset signal or a dataset-specific artifact.