validate {GenomicSuperSignature} | R Documentation |
Validate new datasets
validate( dataset, RAVmodel, method = "pearson", maxFrom = "PC", level = "max", scale = FALSE )
dataset |
Single or a named list of SummarizedExperiment (RangedSummarizedExperiment, ExpressionSet or matrix) object(s). Gene names should be in 'symbol' format. Currently, each dataset should have at least 8 samples. |
RAVmodel |
PCAGenomicSignatures object. |
method |
A character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated. |
maxFrom |
Select whether to display the maximum value from dataset's PCs
or avgLoadings. Under the default ( |
level |
Output format of validated result. Two options are available:
|
scale |
Default is |
A data frame containing the maximum pearson correlation coefficient
between the top 8 PCs of the dataset and pre-calculated average loadings
(in row) of training datasets (score
column). It also contains other
metadata associated with each RAV: PC
for one of the top 8 PCs of the
dataset that results in the given score
, sw
for the average
silhouette width of the RAV, cl_size
for the size of each RAV.
If the input for dataset
argument is a list of different datasets,
each row of the output represents a new dataset for test, and each column
represents clusters from training datasets. If level = "all"
, a list
containing the matrices of the pearson correlation coefficient between all
top 8 PCs of the datasets and avgLoading.
data(miniRAVmodel) library(bcellViper) data(bcellViper) validate(dset, miniRAVmodel) validate(dset, miniRAVmodel, maxFrom = "avgLoading")