Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection onto the Most Interesting Statistical Evidence with adaptive permutation testing

Stan Pounds, Xueyuan Cao, Cheng Cheng, Jun J. Yang, Dario Campana, Ching Hon Pui, William E. Evans, Mary V. Relling

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We recently developed the Projection Onto the Most Interesting Statistical Evidence (PROMISE) procedure that uses prior biological knowledge to guide an integrated analysis of gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical and genome-wide genotype data. An efficient permutation-testing algorithm is introduced so that PROMISE is computationally feasible in this higher-dimension setting. In the analysis of a paediatric leukaemia data set, PROMISE effectively identifies genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables.

Original languageEnglish (US)
Pages (from-to)143-157
Number of pages15
JournalInternational Journal of Data Mining and Bioinformatics
Volume5
Issue number2
DOIs
StatePublished - Mar 1 2011

Fingerprint

Pediatrics
Microarrays
Gene expression
Single Nucleotide Polymorphism
projection
Leukemia
Genes
Genotype
Association reactions
Genome
Gene Expression
Testing
evidence
Datasets

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

Cite this

Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection onto the Most Interesting Statistical Evidence with adaptive permutation testing. / Pounds, Stan; Cao, Xueyuan; Cheng, Cheng; Yang, Jun J.; Campana, Dario; Pui, Ching Hon; Evans, William E.; Relling, Mary V.

In: International Journal of Data Mining and Bioinformatics, Vol. 5, No. 2, 01.03.2011, p. 143-157.

Research output: Contribution to journalArticle

Pounds, Stan ; Cao, Xueyuan ; Cheng, Cheng ; Yang, Jun J. ; Campana, Dario ; Pui, Ching Hon ; Evans, William E. ; Relling, Mary V. / Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection onto the Most Interesting Statistical Evidence with adaptive permutation testing. In: International Journal of Data Mining and Bioinformatics. 2011 ; Vol. 5, No. 2. pp. 143-157.
@article{0294a15404b84cc6b18e18234c7347d6,
title = "Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection onto the Most Interesting Statistical Evidence with adaptive permutation testing",
abstract = "We recently developed the Projection Onto the Most Interesting Statistical Evidence (PROMISE) procedure that uses prior biological knowledge to guide an integrated analysis of gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical and genome-wide genotype data. An efficient permutation-testing algorithm is introduced so that PROMISE is computationally feasible in this higher-dimension setting. In the analysis of a paediatric leukaemia data set, PROMISE effectively identifies genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables.",
author = "Stan Pounds and Xueyuan Cao and Cheng Cheng and Yang, {Jun J.} and Dario Campana and Pui, {Ching Hon} and Evans, {William E.} and Relling, {Mary V.}",
year = "2011",
month = "3",
day = "1",
doi = "10.1504/IJDMB.2011.039174",
language = "English (US)",
volume = "5",
pages = "143--157",
journal = "International Journal of Data Mining and Bioinformatics",
issn = "1748-5673",
publisher = "Inderscience Enterprises Ltd",
number = "2",

}

TY - JOUR

T1 - Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection onto the Most Interesting Statistical Evidence with adaptive permutation testing

AU - Pounds, Stan

AU - Cao, Xueyuan

AU - Cheng, Cheng

AU - Yang, Jun J.

AU - Campana, Dario

AU - Pui, Ching Hon

AU - Evans, William E.

AU - Relling, Mary V.

PY - 2011/3/1

Y1 - 2011/3/1

N2 - We recently developed the Projection Onto the Most Interesting Statistical Evidence (PROMISE) procedure that uses prior biological knowledge to guide an integrated analysis of gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical and genome-wide genotype data. An efficient permutation-testing algorithm is introduced so that PROMISE is computationally feasible in this higher-dimension setting. In the analysis of a paediatric leukaemia data set, PROMISE effectively identifies genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables.

AB - We recently developed the Projection Onto the Most Interesting Statistical Evidence (PROMISE) procedure that uses prior biological knowledge to guide an integrated analysis of gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical and genome-wide genotype data. An efficient permutation-testing algorithm is introduced so that PROMISE is computationally feasible in this higher-dimension setting. In the analysis of a paediatric leukaemia data set, PROMISE effectively identifies genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables.

UR - http://www.scopus.com/inward/record.url?scp=79953209727&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953209727&partnerID=8YFLogxK

U2 - 10.1504/IJDMB.2011.039174

DO - 10.1504/IJDMB.2011.039174

M3 - Article

C2 - 21516175

AN - SCOPUS:79953209727

VL - 5

SP - 143

EP - 157

JO - International Journal of Data Mining and Bioinformatics

JF - International Journal of Data Mining and Bioinformatics

SN - 1748-5673

IS - 2

ER -