Validation result in data frame
validatedSignatures(
val_all,
RAVmodel,
num.out = 5,
scoreCutoff = NULL,
swCutoff = NULL,
clsizeCutoff = NULL,
indexOnly = FALSE,
whichPC = NULL,
filterMessage = TRUE
)An output matrix from validate function. If this
input is from multiple datasets, only scoreCutoff argument will be
considered and other inputs will be ignored.
PCAGenomicSignatures-class object. RAVmodel used to prepare
val_all input.
A number of highly validated RAVs to output. Default is 5.
If any of the cutoff parameters are provided, num.out or the number of
filtered RAVs, whichever smaller, will be chosen.
A numeric value for the minimum correlation. For multi-studies case, the default is 0.7.
A numeric value for the minimum average silhouette width.
An integer value for the minimum cluster size.
A logical. Under the default (= FALSE), the detailed
information on validated RAVs, such as score, average silhouette width,
cluster size, is printed. If it is set TRUE, only the RAV number will be
printed.
An integer value between 1 and 8. PC number of your data to
check the validated signatures with. Under the default (NULL), it
outputs top scored signatures with any PC of your data.
A logical. Under the default TRUE, any output
RAV belong to the filtering list will give a message. Silence this message
with filterMessage=FALSE. You can check the filter list using
data("filterList").
A subset of the input matrix, which meets the given condition.
data(miniRAVmodel)
library(bcellViper)
data(bcellViper)
val_all <- validate(dset, miniRAVmodel)
validatedSignatures(val_all, miniRAVmodel, num.out = 3, scoreCutoff = 0)
#> RAV1076 can be filtered based on GSEA_PLIERpriors
#> RAV2538 can be filtered based on GSEA_PLIERpriors
#> RAV338 can be filtered based on GSEA_PLIERpriors
#> score PC sw cl_size cl_num
#> RAV1076 0.5950767 2 -0.04447124 10 1076
#> RAV2538 0.5838616 2 0.06996166 4 2538
#> RAV338 0.5709072 2 -0.04683319 21 338