ReviewBiophysical reviews2019
The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond.
Review in Biophysical reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Altered gene expression and PTSD symptom dimensions in World Trade Center responders.Molecular psychiatry · 2022Pooled it
- SIEVEseq: unified differential expression, variability, and skewness analyses using RNA-Seq data.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026Article
- Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization.bioRxiv : the preprint server for biology · 2026Article
- A perspective on the reliability and relevance of research results.Transgenic research · 2026Article
- Inter-individual gene expression variability implies stable regulation of brain-biased genes across organs in three ray-finned fishes.bioRxiv : the preprint server for biology · 2026Article
- GEDO: topology-based inference of gene module activity in Sjögren's disease.Briefings in bioinformatics · 2026Article
- Trajectory inference for a branching SDE model of cell differentiation via lineage tracing.Journal of mathematical biology · 2026Article
- Medical statistics education in the big data era: focusing on critical thinking and ethical competence.Frontiers in public health · 2026Article
- Unico: a unified model for cell-type resolution genomics from heterogeneous omics data.Genome biology · 2025Article
- Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data.BMC bioinformatics · 2024Article
- TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.BMC bioinformatics · 2024Article
- Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Integrative analysis of mRNA and miRNA expression profiles and somatic variants in oxysterol signaling in early-stage luminal breast cancer.Molecular oncology · 2023Article
- Rapid and biased evolution of canalization during adaptive divergence revealed by dominance in gene expression variability during Arctic charr early development.Communications biology · 2023Article
- Unraveling the role of resistin, retinol-binding protein 4 and adiponectin produced by epicardial adipose tissue in cardiac structure and function: evidence of a paracrine effect.Hormones (Athens, Greece) · 2023Article
- One model fits all: Combining inference and simulation of gene regulatory networks.PLoS computational biology · 2023Article
- scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data.GigaScience · 2022Article
- Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles.Entropy (Basel, Switzerland) · 2022Article
- Identification of key genes of the ccRCC subtype with poor prognosis.Scientific reports · 2022Article
- Statistical Analysis of Rodent Body Weight Data is Robust to Departures from Normality in Historical National Toxicology Program Studies Dated 1980-2013.Journal of young investigators · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more.
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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.