Tspair statistics

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Citations per year

Number of citations per year for the bioinformatics software tool Tspair
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Tool usage distribution map

This map represents all the scientific publications referring to Tspair per scientific context
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Associated diseases

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Popular tool citations

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Tspair specifications

Information


Unique identifier OMICS_12484
Name Tspair
Alternative name Top Scoring Pairs
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Either (i) a gene expression matrix or an expression set and a group indicator vector, or (ii) an expression set object and a column number, indicating which column of the annotation data to use as the group indicator.
Output data A tsp object which gives the TSP score, indices, gene expression data and group labels for the TSP. If there are multiple pairs that achieve the top score, then the tie-breaking score developed by Tan et al. (2005) is reported.
Operating system Unix/Linux, Mac OS, Windows
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.38.0
Stability Stable
Requirements
R(>=2.10), Biobase(>=2.4.0)
Maintained Yes

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Documentation


Maintainer


  • person_outline Jeffrey T. Leek

Publication for Top Scoring Pairs

Tspair citations

 (2)
library_books

Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer

2018
BMC Genomics
PMCID: 5789529
PMID: 29378509
DOI: 10.1186/s12864-018-4446-y

[…] P, could be applied to the data measured by different laboratories or platforms. Here, we additionally evaluated other rank based approaches using the same training and validation datasets. Using the tspair R package (version 3.3.3), we trained the TSP classifier in the training samples directly combined from data measured by the Affymetrix and Illumina platforms. In the training set, 97.0% of the […]

library_books

Evolutionary Approach for Relative Gene Expression Algorithms

2014
Sci World J
PMCID: 3982252
PMID: 24790574
DOI: 10.1155/2014/593503

[…] specialized operators to mutate and cross-over individuals and a fitness function that helps mitigating the overfitting problem. With the new weighted gene pairs voting and extended representation of top-scoring pairs that involve different variants of TSP, we were able to significantly improve TSP accuracy with still relatively small size of classification model. Application of EAs allows explori […]


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Tspair institution(s)
Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA
Tspair funding source(s)
National Science Foundation (DMS034211); National Institutes of Health (1UL1RR025005-01).

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