ArticleScientific reports2021
Data-driven detection of subtype-specific differentially expressed genes.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Real-Time Detection System for Road Roughness Based on Ultrasonic Technology.Sensors (Basel, Switzerland) · 2026Article
- drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph.BMC bioinformatics · 2026Article
- DeepProtein: deep learning library and benchmark for protein sequence learning.Bioinformatics (Oxford, England) · 2025Article
- TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction.Scientific data · 2025Article
- drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.ArXiv · 2025Article
- DruGagent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction.ArXiv · 2025Article
- Embracing the informative missingness and silent gene in analyzing biologically diverse samples.Scientific reports · 2024Article
- ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.Research square · 2024Article
- Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction.Health data science · 2024Article
- CLINICAL HETEROGENEITY IN THE AGE OF BIG DATA, ADVANCED ANALYTICS, AND COMPLEXITY THEORY.Transactions of the American Clinical and Climatological Association · 2023Article
- PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning.Frontiers in molecular biosciences · 2022Article
- COT: an efficient and accurate method for detecting marker genes among many subtypes.Bioinformatics advances · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular complex tissues. Classic differential analysis assumes a null hypothesis whose test statistic is not subtype-specific, thus can produce a high false positive rate and/or lower detection power. Here we first introduce a One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. We then propose a scaled test statistic (OVE-sFC) for assessing the statistical significance of SDEGs that applies a mixture null distribution model and a tailored permutation test. The OVE-FC/sFC test was validated on both type 1 error rate and detection power using extensive simulation data sets generated from real gene expression profiles of purified subtype samples. The OVE-FC/sFC test was then applied to two benchmark gene expression data sets of purified subtype samples and detected many known or previously unknown SDEGs. Subsequent supervised deconvolution results on synthesized bulk expression data, obtained using the SDEGs detected from the independent purified expression data by the OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.