ArticleInternational journal of biological sciences2018
Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings.
Article in International journal of biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Vorinostat Mitigates Early Brain Injury Following Subarachnoid Hemorrhage Through Modulation of the C3aR1/TGF-β/Smad Pathway.CNS neuroscience & therapeutics · 2026Article
- Integrative multi-omics reveals common and distinct pathogenic mechanisms and preoperative diagnostic signatures in breast fibroepithelial lesions.Breast cancer research : BCR · 2025Article
- Activin A Prevents Hyperresponsiveness to Vascular Endothelial Growth Factor in Pathologic Blood Vessels by Perturbing the Trafficking of Activated Vascular Endothelial Growth Factor Receptor 2.The American journal of pathology · 2025Article
- Applications of gene pair methods in clinical research: advancing precision medicine.Molecular biomedicine · 2025Review
- Less is more: relative rank is more informative than absolute abundance for compositional NGS data.Briefings in functional genomics · 2025Review
- Personalized analysis of human cancer multi-omics for precision oncology.Computational and structural biotechnology journal · 2024Review
- Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.IET systems biology · 2024Article
- RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.BMC bioinformatics · 2024Article
- APPLICATION OF QUANTILE DISCRETIZATION AND BAYESIAN NETWORK ANALYSIS TO PUBLICLY AVAILABLE CYSTIC FIBROSIS DATA SETS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis.Frontiers in genetics · 2024Article
- MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients.Briefings in bioinformatics · 2023Article
- Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.BMC bioinformatics · 2022Article
- Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA.Journal of cellular and molecular medicine · 2021Article
- Identification of population-level differentially expressed genes in one-phenotype data.Bioinformatics (Oxford, England) · 2020Article
- A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy.The Prostate · 2020Article
- A Five-Gene-Pair-Based Prognostic Signature for Predicting the Relapse Risk of Early Stage ER+ Breast Cancer.Frontiers in genetics · 2020Article
- The Effects of Age, Cigarette Smoking, Sex, and Race on the Qualitative Characteristics of Lung Transcriptome.BioMed research international · 2020Article
- Identification and characterization of genes with absolute mRNA abundances changes in tumor cells with varied transcriptome sizes.BMC genomics · 2019Article
- A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection.Frontiers in oncology · 2019Article
- Special issue on Computational Resources and Methods in Biological Sciences.International journal of biological sciences · 2018Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes and introduce unacceptable high false rates, as demonstrated in this study. These problems pose critical obstacles for analyzing the transcriptional data in The Cancer Genome Atlas where there are many small-scale batches of data. Previously, we developed RankComp to detect DEGs for individual disease samples through exploiting the incongruous relative expression orderings between two phenotypes and further improved it here to identify DEGs using multiple independent datasets. We demonstrated the improved RankComp can directly analyze integrated cross-site data to detect DEGs between two phenotypes without the need of batch effect adjustments. Its usage was illustrated in detecting weak differential expression signals of breast cancer drug-response data using combined datasets from multiple experiments.
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