A Quick Start of cola Package

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:

  1. Adjust the matrix. In this step, rows with too many 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).
  2. Generate a detailed HTML report for the complete analysis.

There are examples on real datasets for cola analysis that can be found at https://jokergoo.github.io/cola_collection/.