ArticleBioinformatics (Oxford, England)2016
EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- TAPINTO: A Novel Algorithm for Tumor-Associated Antigen Prediction Based on Information about Target Overexpression.Computational and structural biotechnology journal · 2026Article
- From bulk RNA sequencing to spatial transcriptomics: a comparative review of differential gene expression analysis methods.Human genomics · 2025Review
- Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD).Communications biology · 2025Article
- Adaptive dynamics of extrachromosomal circular DNA in rice under nutrient stress.Nature communications · 2025Article
- Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.Genome research · 2024Article
- q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics.Communications biology · 2024Article
- Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1.Neuron · 2024Article
- Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI.PLoS computational biology · 2023Article
- Combining TSS-MPRA and sensitive TSS profile dissimilarity scoring to study the sequence determinants of transcription initiation.Nucleic acids research · 2023Article
- A universal tool for predicting differentially active features in single-cell and spatial genomics data.Scientific reports · 2023Article
- Single-cell analysis reveals inflammatory interactions driving macular degeneration.Nature communications · 2023Article
- Two-sample test with g-modeling and its applications.Statistics in medicine · 2023Article
- Decoding brain memory formation by single-cell RNA sequencing.Briefings in bioinformatics · 2022Article
- CONTRASTIVE LATENT VARIABLE MODELING WITH APPLICATION TO CASE-CONTROL SEQUENCING EXPERIMENTS.The annals of applied statistics · 2022Article
- Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance.Biomolecules · 2022Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
- BSDE: barycenter single-cell differential expression for case-control studies.Bioinformatics (Oxford, England) · 2022Article
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5 authors.
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Abstract
motivationA major goal of biomedical research is to identify molecular features associated with a biological or clinical class of interest. Differential expression analysis has long been used for this purpose; however, conventional methods perform poorly when applied to data with high within class heterogeneity.
resultsTo address this challenge, we developed EMDomics, a new method that uses the Earth mover's distance to measure the overall difference between the distributions of a gene's expression in two classes of samples and uses permutations to obtain q-values for each gene. We applied EMDomics to the challenging problem of identifying genes associated with drug resistance in ovarian cancer. We also used simulated data to evaluate the performance of EMDomics, in terms of sensitivity and specificity for identifying differentially expressed gene in classes with high within class heterogeneity. In both the simulated and real biological data, EMDomics outperformed competing approaches for the identification of differentially expressed genes, and EMDomics was significantly more powerful than conventional methods for the identification of drug resistance-associated gene sets. EMDomics represents a new approach for the identification of genes differentially expressed between heterogeneous classes and has utility in a wide range of complex biomedical conditions in which sample classes show within class heterogeneity. AVAILABILITY AND IMPLEMENTATION: The R package is available at http://www.bioconductor.org/packages/release/bioc/html/EMDomics.html.
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