ArticleBriefings in bioinformatics2019
A rank-based algorithm of differential expression analysis for small cell line data with statistical control.
Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- A gene interaction network-based molecular taxonomy of dilated cardiomyopathy highlighting immune and inflammatory heterogeneity.Clinical and experimental medicine · 2026Article
- Proliferative diabetic retinopathy subtypes defined by immune defense and endothelial mitochondrial dysfunction.Signal transduction and targeted therapy · 2025Article
- Metabolic genes interaction perturbation network identified and validated CD24 as a novel prognostic gene in hepatocellular carcinoma.Discover oncology · 2025Article
- scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference.Scientific reports · 2025Article
- Less is more: relative rank is more informative than absolute abundance for compositional NGS data.Briefings in functional genomics · 2025Review
- Targeting Matrix Metalloproteinase-9 to Alleviate T Cell Exhaustion and Improve Sepsis Prognosis.Research (Washington, D.C.) · 2025Article
- RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.BMC bioinformatics · 2024Article
- Crosstalk of cell death pathways unveils an autophagy-related gene AOC3 as a critical prognostic marker in colorectal cancer.Communications biology · 2024Article
- Multi-omics integration analysis unveils heterogeneity in breast cancer at the individual level.Cell cycle (Georgetown, Tex.) · 2023Article
- Comprehensive analyses of brain cell communications based on multiple scRNA-seq and snRNA-seq datasets for revealing novel mechanism in neurodegenerative diseases.CNS neuroscience & therapeutics · 2023Review
- Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer.eLife · 2022Article
- Multi-time scale transcriptomic analysis on the dynamic process of tamoxifen resistance development in breast cancer cell lines.Breast cancer (Tokyo, Japan) · 2022Article
- Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes.International journal of molecular sciences · 2022Article
- Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network.Frontiers in immunology · 2022Article
- Performance of a scalable RNA extraction-free transcriptome profiling method for adherent cultured human cells.Scientific reports · 2021Article
- Sample-specific perturbation of gene interactions identifies breast cancer subtypes.Briefings in bioinformatics · 2021Article
- A novel riboswitch classification based on imbalanced sequences achieved by machine learning.PLoS computational biology · 2020Article
- PenDA, a rank-based method for personalized differential analysis: Application to lung cancer.PLoS computational biology · 2020Article
- The Effects of Age, Cigarette Smoking, Sex, and Race on the Qualitative Characteristics of Lung Transcriptome.BioMed research international · 2020Article
- Qualitative transcriptional signatures for evaluating the maturity degree of pluripotent stem cell-derived cardiomyocytes.Stem cell research & therapy · 2019Article
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11 authors.
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Abstract
To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data.
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