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CONOR specifications

Information


Unique identifier OMICS_14604
Name CONOR
Alternative name CrOss-platform NOrmalization in R
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.0.2
Stability No
Requirements
CLSOCP, tseries, outliers, nortest, quadprog, zoo, preprocessCore, fields, fpc, flexclust, plyr
Maintained No

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Publication for CrOss-platform NOrmalization in R

CONOR citations

 (11)
call_split

Cyclin D1 mediated microRNA expression signature predicts breast cancer outcome

2018
Theranostics
PMCID: 5928887
PMID: 29721077
DOI: 10.7150/thno.23877
call_split See protocol

[…] to retain only the miRNA that are represented on all array platforms. Array-filtered, transformed and miRNA-filtered data is normalized across platforms using the XPN algorithm implemented with the CONOR package in the R Statistical Environment. Data summarization plots, including boxplots, two-dimensional PCA projections, and hierarchical clustering were used for quality control of cross-platf […]

library_books

Identification of key genes associated with rheumatoid arthritis with bioinformatics approach

2017
PMCID: 5626145
PMID: 28767591
DOI: 10.1097/MD.0000000000007673

[…] cessCore[] in R, and the gene expression matrix of each sample was acquired.All expression estimates were log2 transformed and merged using cross-platform normalization, which was performed using the CONOR[,] package in R. If different studies comprised similar or common gene symbols, 2 expression data of the same gene symbols were normalized for producing a new dataset. Then, the newly produced d […]

library_books

Microarray Meta Analysis and Cross Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

2015
Microarrays
PMCID: 4996376
PMID: 27600230
DOI: 10.3390/microarrays4030389

[…] listed in . Different experiments from multiple different arrays can be directly merged from the CEL files simultaneously using several packages implemented in R [] including inSilico Merging [], the CONOR [], and virtualArray []. The inSilico Merging package implements XPN, DWD, and Combat, and the package CONOR additionally implements the GQ method. The virtualArray package allows cross-platform […]

library_books

Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas

2015
Genome Med
PMCID: 4512160
PMID: 26207141
DOI: 10.1186/s13073-015-0187-6

[…] Z-score, rank and two more sophisticated methods, XPN and DWD [, ] implemented in the CONOR package [], were used to examine the effect of different cross-platform normalization methods. Z-score normalization operates for each gene independently, producing a normalized expression value […]

library_books

Protein protein interaction analysis of distinct molecular pathways in two subtypes of colorectal carcinoma

2014
PMCID: 4227423
PMID: 25242495
DOI: 10.3892/mmr.2014.2585

[…] put data (). Thus, cross-platform normalization (XPN) was used (). We selected the 12,779 common genes between the two datasets and their expression data were normalized with the XPN method using the CONOR package (). These data were used for further analyses. The DEGs were identified from the normalized final dataset using Student’s t-tests (P<0.05), followed by Benjamini-Hochberg (BH) multiple t […]

library_books

A Novel Model to Combine Clinical and Pathway Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer

2014
PLoS Comput Biol
PMCID: 4168973
PMID: 25233347
DOI: 10.1371/journal.pcbi.1003851

[…] sformed probe intensities as the gene expression. All the data sets were normalized independently between array using limma package . To minimize batch effects across different data sets, we used the CONOR package with the Bayesian method .We generated the PAM50 heatmap of the gene expression data and the correlation heatmap with hierarchical clustering, where Euclidean distance measure was employ […]

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CONOR institution(s)
Biomedical Informatics Research Center, San Diego State University, San Diego, CA, USA
CONOR funding source(s)
This work was supported in part by the National Institute of Health (NIH) Grant U54-HL108460.

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