MmCMS | R Documentation |
consensus molecular subtypes (CMS) classification for mouse tissues.
MmCMS(
emat,
templates = MmCMS::template.CMS.A,
Genesets = c("template.CMS.B", "template.CMS.C", "template.CMS.A"),
nPerm = 1000,
seed = NULL,
FDR = 0.05,
doPlot = TRUE,
verbose = TRUE
)
emat |
a numeric expression matrix with sample columns, and HGNC symbol rownames.
Data should be normalized.
see the example in |
templates |
a data frame with two columns; class (coerced to factor) and probe (coerced to character). There are 3 templates: 'template.CMS.A', 'template.CMS.B' and 'template.CMS.C'. Default is template.CMS.A |
Genesets |
Select the same as template |
nPerm |
an integer, number of permutations for |
seed |
an integer, for |
FDR |
a false discovery rate, sets prediction confidence threshold. |
doPlot |
a logical, whether to produce prediction subHeatmap. |
verbose |
a logical, whether console messages are to be displayed. |
MmCMS
provides 3 options to call CMS subtypes in mouse tissues.
The core algorithm is the CMScaller developed by Eide PW, et al. (2017)
and the Nearest Template Prediction (NTP) algorithm as proposed by Yujin Hoshida (2010).
a data frame with class predictions, template distances,
p
-values and false discovery rate adjusted p
-values
(p.adjust
). Rownames equal emat
colnames.
genes with missing values are discarded.
Eide PW, Bruun J, Lothe RA, Sveen A. (2017). CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. doi: 10.1038/s41598-017-16747-x.
Hoshida, Y. (2010). Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment. PLoS ONE 5, e15543.
Guinney J, Dienstmann R, Wang X, de Reynies A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350-6.
template.CMS.A
, template.CMS.B
, template.CMS.C
emat <- TestData_gemm
re <- MmCMS(emat, templates=MmCMS::template.CMS.A, Genesets = c("template.CMS.A"), seed=120)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.