rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000383749600001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1186/s12859-016-1244-x
Externe Identifier
  • Clarivate Analytics Document Solution ID: DW6GY
  • PubMed Identifier: 27663265
ISSN
1471-2105
Zeitschrift
BMC BIOINFORMATICS
Schlüsselwörter
  • CUDA
  • Gene set enrichment analysis
  • Gene expression data
  • Resampling statistics
Artikelnummer
ARTN 394
Datum der Veröffentlichung
2016
Status
Published
Titel
rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs
Sub types
  • Article
Ausgabe der Zeitschrift
17

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Gene Set Enrichment Analysis (GSEA) is a popular method to reveal significant dependencies between predefined sets of gene symbols and observed phenotypes by evaluating the deviation of gene expression values between cases and controls. An established measure of inter-class deviation, the enrichment score, is usually computed using a weighted running sum statistic over the whole set of gene symbols. Due to the lack of analytic expressions the significance of enrichment scores is determined using a non-parametric estimation of their null distribution by permuting the phenotype labels of the probed patients. Accordingly, GSEA is a time-consuming task due to the large number of required permutations to accurately estimate the nominal <jats:italic>p</jats:italic>-value – a circumstance that is even more pronounced during multiple hypothesis testing since its estimate is lower-bounded by the inverse number of samples in permutation space.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>We present rapidGSEA – a software suite consisting of two tools for facilitating permutation-based GSEA: cudaGSEA and ompGSEA. cudaGSEA is a CUDA-accelerated tool using fine-grained parallelization schemes on massively parallel architectures while ompGSEA is a coarse-grained multi-threaded tool for multi-core CPUs. Nominal <jats:italic>p</jats:italic>-value estimation of 4,725 gene sets on a data set consisting of 20,639 unique gene symbols and 200 patients (183 cases + 17 controls) each probing one million permutations takes 19 hours on a Xeon CPU and less than one hour on a GeForce Titan X GPU while the established GSEA tool from the Broad Institute (broadGSEA) takes roughly 13 days.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>cudaGSEA outperforms broadGSEA by around two orders-of-magnitude on a single Tesla K40c or GeForce Titan X GPU. ompGSEA provides around one order-of-magnitude speedup to broadGSEA on a standard Xeon CPU. The rapidGSEA suite is open-source software and can be downloaded at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/gravitino/cudaGSEA">https://github.com/gravitino/cudaGSEA</jats:ext-link>as standalone application or package for the R framework.</jats:p> </jats:sec>
Autoren
DOI
10.1186/s12859-016-1244-x
eISSN
1471-2105
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Bioinformatics
Sprache
en
Artikelnummer
394
Online publication date
2016
Status
Published online
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1186/s12859-016-1244-x
Datum der Datenerfassung
2024
Titel
rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs
Ausgabe der Zeitschrift
17

Datenquelle: Crossref

Abstract
<h4>Background</h4>Gene Set Enrichment Analysis (GSEA) is a popular method to reveal significant dependencies between predefined sets of gene symbols and observed phenotypes by evaluating the deviation of gene expression values between cases and controls. An established measure of inter-class deviation, the enrichment score, is usually computed using a weighted running sum statistic over the whole set of gene symbols. Due to the lack of analytic expressions the significance of enrichment scores is determined using a non-parametric estimation of their null distribution by permuting the phenotype labels of the probed patients. Accordingly, GSEA is a time-consuming task due to the large number of required permutations to accurately estimate the nominal p-value - a circumstance that is even more pronounced during multiple hypothesis testing since its estimate is lower-bounded by the inverse number of samples in permutation space.<h4>Results</h4>We present rapidGSEA - a software suite consisting of two tools for facilitating permutation-based GSEA: cudaGSEA and ompGSEA. cudaGSEA is a CUDA-accelerated tool using fine-grained parallelization schemes on massively parallel architectures while ompGSEA is a coarse-grained multi-threaded tool for multi-core CPUs. Nominal p-value estimation of 4,725 gene sets on a data set consisting of 20,639 unique gene symbols and 200 patients (183 cases + 17 controls) each probing one million permutations takes 19 hours on a Xeon CPU and less than one hour on a GeForce Titan X GPU while the established GSEA tool from the Broad Institute (broadGSEA) takes roughly 13 days.<h4>Conclusion</h4>cudaGSEA outperforms broadGSEA by around two orders-of-magnitude on a single Tesla K40c or GeForce Titan X GPU. ompGSEA provides around one order-of-magnitude speedup to broadGSEA on a standard Xeon CPU. The rapidGSEA suite is open-source software and can be downloaded at https://github.com/gravitino/cudaGSEA as standalone application or package for the R framework.
Addresses
  • Department of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany. hundt@uni-mainz.de.
Autoren
DOI
10.1186/s12859-016-1244-x
eISSN
1471-2105
Externe Identifier
  • PubMed Identifier: 27663265
  • PubMed Central ID: PMC5035472
Open access
true
ISSN
1471-2105
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC bioinformatics
Sprache
eng
Medium
Electronic
Online publication date
2016
Open access status
Open Access
Paginierung
394
Datum der Veröffentlichung
2016
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2016
Titel
rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs.
Sub types
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
17

Files

https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1244-x https://europepmc.org/articles/PMC5035472?pdf=render

Datenquelle: Europe PubMed Central

Abstract
BACKGROUND: Gene Set Enrichment Analysis (GSEA) is a popular method to reveal significant dependencies between predefined sets of gene symbols and observed phenotypes by evaluating the deviation of gene expression values between cases and controls. An established measure of inter-class deviation, the enrichment score, is usually computed using a weighted running sum statistic over the whole set of gene symbols. Due to the lack of analytic expressions the significance of enrichment scores is determined using a non-parametric estimation of their null distribution by permuting the phenotype labels of the probed patients. Accordingly, GSEA is a time-consuming task due to the large number of required permutations to accurately estimate the nominal p-value - a circumstance that is even more pronounced during multiple hypothesis testing since its estimate is lower-bounded by the inverse number of samples in permutation space. RESULTS: We present rapidGSEA - a software suite consisting of two tools for facilitating permutation-based GSEA: cudaGSEA and ompGSEA. cudaGSEA is a CUDA-accelerated tool using fine-grained parallelization schemes on massively parallel architectures while ompGSEA is a coarse-grained multi-threaded tool for multi-core CPUs. Nominal p-value estimation of 4,725 gene sets on a data set consisting of 20,639 unique gene symbols and 200 patients (183 cases + 17 controls) each probing one million permutations takes 19 hours on a Xeon CPU and less than one hour on a GeForce Titan X GPU while the established GSEA tool from the Broad Institute (broadGSEA) takes roughly 13 days. CONCLUSION: cudaGSEA outperforms broadGSEA by around two orders-of-magnitude on a single Tesla K40c or GeForce Titan X GPU. ompGSEA provides around one order-of-magnitude speedup to broadGSEA on a standard Xeon CPU. The rapidGSEA suite is open-source software and can be downloaded at https://github.com/gravitino/cudaGSEA as standalone application or package for the R framework.
Date of acceptance
2016
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/27663265
DOI
10.1186/s12859-016-1244-x
eISSN
1471-2105
Externe Identifier
  • PubMed Central ID: PMC5035472
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Bioinformatics
Schlüsselwörter
  • CUDA
  • Gene expression data
  • Gene set enrichment analysis
  • Resampling statistics
Sprache
eng
Country
England
Paginierung
394
PII
10.1186/s12859-016-1244-x
Datum der Veröffentlichung
2016
Status
Published online
Titel
rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
17

Datenquelle: PubMed

Autoren
Zeitschrift
BMC Bioinform.
Paginierung
394 - 394
Datum der Veröffentlichung
2016
Titel
rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs.
Ausgabe der Zeitschrift
17

Datenquelle: DBLP

Autoren
Zeitschrift
BMC bioinformatics
Artikelnummer
1
Paginierung
394 - 394
Datum der Veröffentlichung
2016
Herausgeber
BioMed Central
Datum der Datenerfassung
2020
Titel
rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs
Sub types
  • article
Ausgabe der Zeitschrift
17

Datenquelle: Manual

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