Evidence map›Paper›PMID 25793520›Full record

ArticlePLoS computational biology2015

Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data.

Alan L Hutchison, Mark Maienschein-Cline, Andrew H Chiang, S M Ali Tabei, Herman Gudjonson, Neil Bahroos, Ravi Allada, Aaron R Dinner

Abstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

64 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Alan L HutchisonMedical Scientist Training Program, University of Chicago, Chicago, Illinois, United States of America; Graduate Program in the Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America; James Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
Mark Maienschein-ClineCenter for Research Informatics, University of Illinois at Chicago, Chicago, Illinois, United States of America.
Andrew H ChiangJames Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
S M Ali TabeiJames Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
Herman GudjonsonGraduate Program in the Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America; James Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
Neil BahroosCenter for Research Informatics, University of Illinois at Chicago, Chicago, Illinois, United States of America.
Ravi AlladaDepartment of Neurobiology, Northwestern University, Evanston, Illinois, United States of America.
Aaron R DinnerGraduate Program in the Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America; James Franck Institute, University of Chicago, Chicago, Illinois, United States of America; Department of Chemistry, University of Chicago, Chicago, Illinois, United States of America.

Funding

MEDICAL SCIENTIST TRAININGT32GM007281 · NIGMS · UNIVERSITY OF CHICAGO · PI CLARK, MARCUS RAMSAY · 1985 to 2022
$32.1M
Graduate Program in Biophysical Sciences at the University of Chicago - Renewal 0T32EB009412 · NIBIB · UNIVERSITY OF CHICAGO · PI ENGEL, GREGORY S, SOSNICK, TOBIN R · 2009 to 2023
$4.9M
NIBIB NIH HHS T32 EB009412NIGMS NIH HHS T32 GM007281
6 · The paper itself

Abstract

Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.

Indexed as

Models, GeneticModels, StatisticalAnimalsCircadian RhythmComputer SimulationDrosophila melanogasterGene Expression ProfilingGenome, InsectGenomics

Identifiers

PMID25793520
PMCPMC4368642

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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.