The CaDrA package currently supports four scoring functions to search for subsets of genomic features that are likely associated with a specific outcome of interest (e.g., protein expression, pathway activity, etc.)
ks
)wilcox
)revealer
)knnmi
)correlation
)custom
)Below, we run candidate_search()
over the top 3 starting
features using each of the scoring functions described above.
Important Notes:
topn_eval()
is
equivalent to the new and recommended candidate_search()
functionlibrary(CaDrA)
library(pheatmap)
library(SummarizedExperiment)
binary features matrix
also known as
Feature Set
(such as somatic mutations, copy number
alterations, chromosomal translocations, etc.) The 1/0 row vectors
indicate the presence/absence of ‘omics’ features in the samples. The
Feature Set
can be a matrix or an object of class
SummarizedExperiment from
SummarizedExperiment package)Input Scores
)
representing a functional response of interest (such as protein
expression, pathway activity, etc.)# Load pre-computed feature set
data(sim_FS)
# Load pre-computed input scores
data(sim_Scores)
The simulated dataset, sim_FS
, comprises of 1000 genomic
features and 100 sample profiles. There are 10 left-skewed (i.e. True
Positive or TP) and 990 uniformly-distributed (i.e. True Null or TN)
features simulated in the dataset. Below is a heatmap of the first 100
features.
mat <- SummarizedExperiment::assay(sim_FS)
pheatmap::pheatmap(mat[1:100, ], color = c("white", "red"), cluster_rows = FALSE, cluster_cols = FALSE)