| Title: | Gene Set Benchmark |
|---|---|
| Description: | Benchmarks for Machine Learning Analysis of the Gene Sets. The package contains a list of pathways and gene expression data sets used in "Identifying Tightly Regulated and Variably Expressed Networks by Differential Rank Conservation (DIRAC)" (2010) by Eddy et al. |
| Authors: | Bahman Afsari <[email protected]>, Elana J. Fertig <[email protected]> |
| Maintainer: | Bahman Afsari <[email protected]> |
| License: | GPL-2 |
| Version: | 1.33.0 |
| Built: | 2026-10-10 10:35:45 UTC |
| Source: | https://github.com/bioc/GSBenchMark |
The GSBenchMark package contains data for testing gene set analysis with two phenotypes. Specially, the user can use the datasets used for validating Differential Rank Conservation (DIRAC) (Eddy et al. 2010). This datasets are used in the package GSReg which implements DIRAC and a more efficient alternative called GSVReg.
The dataset are included:
leukemia_GSEA marfan_GDS2960 melanoma_GDS2735 parkinsons_GDS2519 prostate_GDS2545_m_nf prostate_GDS2545_m_p prostate_GDS2545_p_nf sarcoma_data squamous_GDS2520 breast_GDS807 bipolar_GDS2190 The phenotypes has also been provided and the pathways. For more details, please see the individual help of each dataset as well as (Eddy et al. 2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Bipolar dataset: Gene expression matrix and a factor with binary ("Normal", "Bipolar") phenotypes corresponding to the samples.
data(bipolar_GDS2190)data(bipolar_GDS2190)
The bipolar_GDS2190 exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to Bipolar disorder (Bipolar or Normal) in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally from Ryan et al. (2006)
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5) Ryan, M. M., et al. "Gene expression analysis of bipolar disorder reveals downregulation of the ubiquitin cycle and alterations in synaptic genes." Molecular psychiatry 11.10 (2006): 965-978.
### Load gene expression data data(squamous_GDS2520) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(squamous_GDS2520) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
Squamous dataset: Gene expression matrix and a factor with binary ("Responsive", "non-Responsive") phenotypes corresponding to the samples.
data(breast_GDS807)data(breast_GDS807)
The breast_GDS807 exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the breast cancer data set in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally from Ma et al. (2004)
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Ma, Xiao-Jun, et al. "A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen." Cancer cell 5.6 (2004): 607-616.
### Load gene expression data data(squamous_GDS2520) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(squamous_GDS2520) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
A list of gene sets representing pathways in Eddy et al (2010).
data(diracpathways)data(diracpathways)
The diracpathways list contains the list of pathways. Each element is a vector of gene names.
This list has been extracted from Eddy et al (2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
### Load pathways data(diracpathways) ### The number of pathways coming from Eddy et al. length(diracpathways) ### It is a list of pathways class(diracpathways) ### Genes in the pathway TERCPATHWAY diracpathways[["TERCPATHWAY"]]### Load pathways data(diracpathways) ### The number of pathways coming from Eddy et al. length(diracpathways) ### It is a list of pathways class(diracpathways) ### Genes in the pathway TERCPATHWAY diracpathways[["TERCPATHWAY"]]
Gene expression matrices from different datasets will have this name. When the user loads a dataset from GSBenchMark, one of the variables loaded has this name.
exprsdataexprsdata
The exprsdata contains normalized of any dataset loaded from GSBenckMark package. The rows represent genes and the columns samples.
This dataset corresponds to the Prostate cancer in Eddy and colleagues in PLoS Comp. Bio. (2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
diracpathways,GSBenchMarkDatasets,phenotypes
### Load gene expression data data(prostate_GDS2545_p_nf) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10)### Load gene expression data data(prostate_GDS2545_p_nf) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10)
The list of datasets used in Eddy et al (2010).
data(GSBenchMarkDatasets)data(GSBenchMarkDatasets)
The GSBenchMark.Dataset.names list contains the names of the datasets with gene expressions and phenotypes.
This list gene expression datasets provided in Eddy et al (2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
### Loading the names of the gene expression dataset data(GSBenchMarkDatasets) ### The names of datasets print(GSBenchMark.Dataset.names) ### Loading "leukemia_GSEA" data(list=GSBenchMark.Dataset.names[[1]])### Loading the names of the gene expression dataset data(GSBenchMarkDatasets) ### The names of datasets print(GSBenchMark.Dataset.names) ### Loading "leukemia_GSEA" data(list=GSBenchMark.Dataset.names[[1]])
Leukaemia dataset: Gene expression matrix and a factor with binary (AML, ALL) phenotypes corresponding to the samples.
data(leukemia_GSEA)data(leukemia_GSEA)
The leukemia_GSEA exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the Leukemia in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally Armstrong and et al (2002).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Armstrong, Scott A., et al. "MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia." Nature genetics 30.1 (2002): 41-47.
### Load gene expression data data(leukemia_GSEA) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(leukemia_GSEA) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
The log of gene expressions are saved in matrices with this name. When the user loads a dataset from GSBenchMark, one of the variables loaded has this name.
logexprsdatalogexprsdata
The logexprsdata contains the log of normalized of any dataset loaded from GSBenckMark package. The rows represent genes and the columns samples.
This dataset corresponds to the Prostate cancer in Eddy and colleagues in PLoS Comp. Bio. (2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
diracpathways,GSBenchMarkDatasets,phenotypes,exprsdata
### Load gene expression data data(prostate_GDS2545_p_nf) ### Number of genes nrow(logexprsdata) ### Number of samples ncol(logexprsdata) ### Show the first 10 gene names of the ``logexprsdata'' matrix head(rownames(logexprsdata), n=10)### Load gene expression data data(prostate_GDS2545_p_nf) ### Number of genes nrow(logexprsdata) ### Number of samples ncol(logexprsdata) ### Show the first 10 gene names of the ``logexprsdata'' matrix head(rownames(logexprsdata), n=10)
Marfan dataset: Gene expression matrix and a factor with binary ("non-MFS","MFS") phenotypes corresponding to the samples.
data(marfan_GDS2960)data(marfan_GDS2960)
The marfan_GDS2960 exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to Marfan disease in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally Yao (2007).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Yao, Zizhen, et al. "A Marfan syndrome gene expression phenotype in cultured skin fibroblasts." BMC Genomics 8.1 (2007): 319.
### Load gene expression data data(marfan_GDS2960) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(marfan_GDS2960) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
Melanoma dataset: Gene expression matrix and a factor with binary phenotypes ("Normal", "Metastasis") corresponding to the samples.
data(melanoma_GDS2735)data(melanoma_GDS2735)
The melanoma_GDS2735 exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the melanoma cancer in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally from Critchley-Thorne (2007).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Critchley-Thorne, Rebecca J., et al. "Down-regulation of the interferon signaling pathway in T lymphocytes from patients with metastatic melanoma." PLoS Medicine 4.5 (2007): e176.
### Load gene expression data data(melanoma_GDS2735) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(melanoma_GDS2735) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
Parkinson's dataset: Gene expression matrix and a factor with binary ("Normal", "Parkinson's") phenotypes corresponding to the samples.
data(parkinsons_GDS2519)data(parkinsons_GDS2519)
The parkinsons_GDS2519 exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the Parkinson's disease in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally Scherzer (2007).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Scherzer, Clemens R., et al. "Molecular markers of early Parkinson's disease based on gene expression in blood." Proceedings of the National Academy of Sciences 104.3 (2007): 955-960.
### Load gene expression data data(parkinsons_GDS2519) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(parkinsons_GDS2519) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
a factor for binary phenotypes corresponding to the samples and a two-element vector with the real label names. When the user loads one of data set from the GSBenchMark it will contain these two variables.
phenotypes phenotypesLevelsphenotypes phenotypesLevels
The phenotypes is a binary ("0" or "1") factor containing the phenotypes of the the samples in any dataset loaded from GSBenckMark package. "0" corresponds to less dangerous phenotype and "1" to more dangerous phenotype.
The phenotypesLevels contains the real labels instead of "0" and "1".
This dataset corresponds to the Prostate cancer in Eddy and colleagues in PLoS Comp. Bio. (2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
diracpathways,GSBenchMarkDatasets,exprsdata
### Load gene expression data data(prostate_GDS2545_p_nf) ### Show group variable print(table(phenotypes)) print(phenotypesLevels)### Load gene expression data data(prostate_GDS2545_p_nf) ### Show group variable print(table(phenotypes)) print(phenotypesLevels)
Prostate dataset: Gene expression matrix and a factor with binary phenotypes ("Normal" and "Metastasis") corresponding to the samples.
data(prostate_GDS2545_m_nf)data(prostate_GDS2545_m_nf)
The prostate_GDS2545_m_nf exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the Prostate cancer in Eddy and colleagues in PLoS Comp. Bio. (2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Chandran, Uma R., et al. "Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process." BMC cancer 7.1 (2007): 64.
### Load gene expression data data(prostate_GDS2545_m_nf) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(prostate_GDS2545_m_nf) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
Prostate dataset: Gene expression matrix and a factor with binary phenotypes ("Primary" and "Metastasis") corresponding to the samples.
data(prostate_GDS2545_m_p)data(prostate_GDS2545_m_p)
The prostate_GDS2545_m_p exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the Prostate cancer in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally from Chandran (2007).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Chandran, Uma R., et al. "Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process." BMC cancer 7.1 (2007): 64.
### Load gene expression data data(prostate_GDS2545_m_p) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(prostate_GDS2545_m_p) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
Prostate dataset: Gene expression matrix and a factor with binary phenotypes ("Normal","Primary") corresponding to the samples.
data(prostate_GDS2545_p_nf)data(prostate_GDS2545_p_nf)
The prostate_GDS2545_p_nf exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the Prostate cancer in Eddy and colleagues in PLoS Comp. Bio. (2010).
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Chandran, Uma R., et al. "Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process." BMC cancer 7.1 (2007): 64.
### Load gene expression data data(prostate_GDS2545_p_nf) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(prostate_GDS2545_p_nf) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
Sarcoma (Gastrointestinal cancer) dataset: Gene expression matrix and a factor with binary phenotypes (GIST, LMS) corresponding to the samples.
data(sarcoma_data)data(sarcoma_data)
The sarcoma_data exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the Sarcoma cancer in Eddy and colleagues in PLoS Comp. Bio. (2010) abd originally data from Price et al.
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5)
Price, Nathan D., et al. "Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas." Proceedings of the National Academy of Sciences 104.9 (2007): 3414-3419.
### Load gene expression data data(sarcoma_data) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(sarcoma_data) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)
Squamous dataset: Gene expression matrix and a factor with binary phenotypes (Normal, HNSCC) corresponding to the samples.
data(squamous_GDS2520)data(squamous_GDS2520)
The squamous_GDS2520 exprsdata contains normalized
expression values (rows) across samples (columns).
Group information is in the factor phenotypes.
This dataset corresponds to the Squamous (Head and Neck) cancer in Eddy and colleagues in PLoS Comp. Bio. (2010) and originally from Kuriakose et al.
Bahman Afsari [email protected], Elana J. Fertig [email protected]
http://www.ncbi.nlm.nih.gov/pubmed/20523739
Eddy et al., "Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).", PLoS Comp. Bio., 2010, 6(5) Kuriakose, M. A., et al. "Selection and validation of differentially expressed genes in head and neck cancer." Cellular and Molecular Life Sciences CMLS 61.11 (2004): 1372-1383.
### Load gene expression data data(squamous_GDS2520) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)### Load gene expression data data(squamous_GDS2520) ### Number of genes nrow(exprsdata) ### Number of samples ncol(exprsdata) ### Show the first 10 gene names of the ``exprsdata'' matrix head(rownames(exprsdata), n=10) ### Show group variable table(phenotypes)