Author: Zuguang Gu ( email@example.com )
Package version: 1.3.4
Assume your matrix is stored in an object called
mat, to perform consensus
partitioning with cola, you only need to run following code:
# code only for demonstration mat = adjust_matrix(mat) # optional rl = run_all_consensus_partition_methods(mat, mc.cores = ...) cola_report(rl, output_dir = ..., mc.cores = ...)
In above code, there are three steps:
NAs are removed. Rows with very low variance are removed.
NAvalues are imputed if there are less than 50% in each row. Outliers are adjusted in each row.
hclust(hierarchical clustering with cutree),
skmeans::skmeans(spherical k-means clustering),
cluster::pam(partitioning around medoids) and
Mclust::mclust(model-based clustering). The default methods to extract top n rows are
CV(coefficient of variation),
MAD(median absolute deviation) and
ATC(ability to correlate to other rows).
There are examples on real datasets for cola analysis that can be found at https://jokergoo.github.io/cola_collection/.