ArticleGenome biology2014
The importance of study design for detecting differentially abundant features in high-throughput experiments.
Article in Genome biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success.Journal of integrative bioinformatics · 2024Article
- Next-generation forward genetic screens: using simulated data to improve the design of mapping-by-sequencing experiments in Arabidopsis.Nucleic acids research · 2019Article
- Loading ceftriaxone, vancomycin, and Bifidobacteria bifidum TMC3115 to neonatal mice could differently and consequently affect intestinal microbiota and immunity in adulthood.Scientific reports · 2019Article
- Bamboo lignocellulose degradation by gut symbiotic microbiota of the bamboo snout beetleBiotechnology for biofuels · 2019Article
- HIPSTR and thousands of lncRNAs are heterogeneously expressed in human embryos, primordial germ cells and stable cell lines.Scientific reports · 2016Article
- Evaluation of methods for differential expression analysis on multi-group RNA-seq count data.BMC bioinformatics · 2015Article
- Cross Platform Standardisation of an Experimental Pipeline for Use in the Identification of Dysregulated Human Circulating MiRNAs.PloS one · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput assays, such as RNA-seq, to detect differential abundance are widely used. Variable performance across statistical tests, normalizations, and conditions leads to resource wastage and reduced sensitivity. EDDA represents a first, general design tool for RNA-seq, Nanostring, and metagenomic analysis, that rationally selects tests, predicts performance, and plans experiments to minimize resource wastage. Case studies highlight EDDA's ability to model single-cell RNA-seq, suggesting ways to reduce sequencing costs up to five-fold and improving metagenomic biomarker detection through improved test selection. EDDA's novel mode-based normalization for detecting differential abundance improves robustness by 10% to 20% and precision by up to 140%.
Indexed as
Identifiers
What OpenQuestion holds
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.