Author: Zuguang Gu ( z.gu@dkfz.de )
Date: 2019-12-31
Package version: 1.2.1
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. NA values are imputed if there are
not too many in each row. Outliers are adjusted in each row. This step is
partition methods are hclust (hierarchical clustering with cutree),
kmeans (k-means clustering), skmeans::skmeans (spherical k-means
clustering), cluster::pam (partitioning around medoids clustering) and
Mclust::mclust (model-based clustering). The default methods to extract
top n rows are SD (standard deviation), 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/.