ReviewCancer informatics2014
Learning dysregulated pathways in cancers from differential variability analysis.
Review in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
- Improving data interpretability with new differential sample variance gene set tests.BMC bioinformatics · 2025Article
- Symmetric inheritance of parental histones contributes to safeguarding the fate of mouse embryonic stem cells during differentiation.Nature genetics · 2023Article
- Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation.Molecular medicine (Cambridge, Mass.) · 2023Article
- Integrated single-cell and bulk gene expression and ATAC-seq reveals heterogeneity and early changes in pathways associated with resistance to cetuximab in HNSCC-sensitive cell lines.British journal of cancer · 2020Article
- Identifying the mRNAs associated with Bladder cancer recurrence.Cancer biomarkers : section A of Disease markers · 2020Article
- Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data.Cancer research · 2019Article
- Article
- Identification of Differentially Expressed Genes and Long Noncoding RNAs Associated with Parkinson's Disease.Parkinson's disease · 2019Article
- Characterization of Alternative Splicing Events in HPV-Negative Head and Neck Squamous Cell Carcinoma Identifies an Oncogenic DOCK5 Variant.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018Article
- Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.Bioinformatics (Oxford, England) · 2018Article
- Digitizing omics profiles by divergence from a baseline.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Epigenetic regulation of gene expression in cancer: techniques, resources and analysis.Briefings in functional genomics · 2018Article
- Integrated Analysis of Whole-Genome ChIP-Seq and RNA-Seq Data of Primary Head and Neck Tumor Samples Associates HPV Integration Sites with Open Chromatin Marks.Cancer research · 2017Article
- A Novel Functional Splice Variant ofCancer research · 2017Article
- Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation.Molecular medicine reports · 2017Article
- GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles.BMC bioinformatics · 2017Article
- Identifying key genes associated with Hirschsprung's disease based on bioinformatics analysis of RNA-sequencing data.World journal of pediatrics : WJP · 2017Article
- Integrative computational analysis of transcriptional and epigenetic alterations implicates DTX1 as a putative tumor suppressor gene in HNSCC.Oncotarget · 2017Article
- Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Identification of targets of miRNA-221 and miRNA-222 in fulvestrant-resistant breast cancer.Oncology letters · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.
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Registered trials
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.