template.CMS.B: Mouse CMS template (option B)
In MolecularPathologyLab/MmCMS: Calling CMS for mouse tissue
template.CMS.B R Documentation
Mouse CMS template (option B)
Description
Mouse CMS template (option B)
Usage
template.CMS.B
Format
An object of class data.frame with 974 rows and 2 columns.
Details
CMS prediction template for NTP method. Marker genes were
identified via a biological knowledge-based approach instead of having sole emphesis on individual genes.
ssGSEA was performed on TCGA RNA-sequencing data from original CMSclassifier
with assigned CMS subtypes, to provide GO_BP scores for each sample.
Then CMS-related GO_BP terms were converted to mouse GO_BP terms orthologe using msigdbr.
Instead of individual genes, the GO_BP terms used as a template.
templates$probe refers to GO_BP terms.
References
Eide PW, Bruun J, Lothe RA, Sveen A. CMScaller:
an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Scientific reports (2017)
Guinney J, Dienstmann R, Wang X, de Reynies A, et al. The consensus molecular subtypes of colorectal cancer. Nat Med (2015)
Hoshida Y. Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment. PLoS ONE. 2010;5:e15543.
Liberzon et al. (2015) The Molecular Signatures Database Hallmark Gene Set Collection. DOI:https://doi.org/10.1016/j.cels.2015.12.004
Hänzelmann S., Castelo R. and Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics, 14:7, 2013.
Examples
emat <- TestData_gemm
re <- MmCMS(emat, templates=MmCMS::template.CMS.B, Genesets = c("template.CMS.B"), seed=120)
MolecularPathologyLab/MmCMS documentation built on Oct. 18, 2023, 10:42 p.m.
R Package Documentation
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| template.CMS.B | R Documentation |
Mouse CMS template (option B)
Description
Mouse CMS template (option B)
Usage
template.CMS.B
Format
An object of class data.frame with 974 rows and 2 columns.
Details
CMS prediction template for NTP method. Marker genes were
identified via a biological knowledge-based approach instead of having sole emphesis on individual genes.
ssGSEA was performed on TCGA RNA-sequencing data from original CMSclassifier
with assigned CMS subtypes, to provide GO_BP scores for each sample.
Then CMS-related GO_BP terms were converted to mouse GO_BP terms orthologe using msigdbr.
Instead of individual genes, the GO_BP terms used as a template.
templates$probe refers to GO_BP terms.
References
Eide PW, Bruun J, Lothe RA, Sveen A. CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Scientific reports (2017)
Guinney J, Dienstmann R, Wang X, de Reynies A, et al. The consensus molecular subtypes of colorectal cancer. Nat Med (2015)
Hoshida Y. Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment. PLoS ONE. 2010;5:e15543.
Liberzon et al. (2015) The Molecular Signatures Database Hallmark Gene Set Collection. DOI:https://doi.org/10.1016/j.cels.2015.12.004
Hänzelmann S., Castelo R. and Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics, 14:7, 2013.
Examples
emat <- TestData_gemm
re <- MmCMS(emat, templates=MmCMS::template.CMS.B, Genesets = c("template.CMS.B"), seed=120)
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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