Evidence map›Paper›PMID 24489370›Full record

ArticleBioinformatics (Oxford, England)2014

Integrative gene set analysis of multi-platform data with sample heterogeneity.

Jun Hu, Jung-Ying Tzeng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
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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

2 authors.

Jun HuBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, TaiwanBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, Taiwan.
Jung-Ying TzengBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, TaiwanBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, TaiwanBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, Taiwan.

Funding

Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Genome-wide Haplotype Association Analysis in Mental DisordersR01MH084022 · NIMH · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI TZENG, JUNG-YING · 2009 to 2011
$1.1M
NCI NIH HHS P01 CA142538NIMH NIH HHS R01 MH084022NIMH NIH HHS R01MH084022
6 · The paper itself

Abstract

motivationGene set analysis is a popular method for large-scale genomic studies. Because genes that have common biological features are analyzed jointly, gene set analysis often achieves better power and generates more biologically informative results. With the advancement of technologies, genomic studies with multi-platform data have become increasingly common. Several strategies have been proposed that integrate genomic data from multiple platforms to perform gene set analysis. To evaluate the performances of existing integrative gene set methods under various scenarios, we conduct a comparative simulation analysis based on The Cancer Genome Atlas breast cancer dataset.

resultsWe find that existing methods for gene set analysis are less effective when sample heterogeneity exists. To address this issue, we develop three methods for multi-platform genomic data with heterogeneity: two non-parametric methods, multi-platform Mann-Whitney statistics and multi-platform outlier robust T-statistics, and a parametric method, multi-platform likelihood ratio statistics. Using simulations, we show that the proposed multi-platform Mann-Whitney statistics method has higher power for heterogeneous samples and comparable performance for homogeneous samples when compared with the existing methods. Our real data applications to two datasets of The Cancer Genome Atlas also suggest that the proposed methods are able to identify novel pathways that are missed by other strategies. AVAILABILITY AND IMPLEMENTATION: http://www4.stat.ncsu.edu/∼jytzeng/Software/Multiplatform_gene_set_analysis/

Indexed as

Breast NeoplasmsDNA Copy Number VariationsDNA MethylationFemaleGene Expression ProfilingGenesGenomicsHumansSequence Analysis, RNAStatistics, Nonparametric

Identifiers

PMID24489370
PMCPMC4029033

What OpenQuestion holds

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Registered trials

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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.