QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards

Tools

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
253kB

Item Type:	Article
Title:	QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards
Creators Name:	Fontaine, J.F., Suter, B. and Andrade-Navarro, M.A.
Abstract:	BACKGROUND: High-throughput biological experiments can produce a large amount of data showing little overlap with current knowledge. This may be a problem when evaluating alternative scoring mechanisms for such data according to a gold standard dataset because standard statistical tests may not be appropriate. FINDINGS: To address this problem we have implemented the QiSampler tool that uses a repetitive sampling strategy to evaluate several scoring schemes or experimental parameters for any type of high-throughput data given a gold standard. We provide two example applications of the tool: selection of the best scoring scheme for a high-throughput protein-protein interaction dataset by comparison to a dataset derived from the literature, and evaluation of functional enrichment in a set of tumour-related differentially expressed genes from a thyroid microarray dataset. CONCLUSIONS: QiSampler is implemented as an open source R script and a web server, which can be accessed at http://cbdm.mdc-berlin.de/tools/sampler/.
Source:	BMC Research Notes
ISSN:	1756-0500
Publisher:	BioMed Central
Volume:	4
Number:	1
Page Range:	57
Date:	9 March 2011
Official Publication:	https://doi.org/10.1186/1756-0500-4-57
PubMed:	View item in PubMed

Repository Staff Only: item control page

Download Statistics

Downloads

Downloads per month over past year