Author: Zuguang Gu ( email@example.com )
Package version: 1.2.0
Assume your matrix is stored in an object called
mat, to perform consensus partitioning,
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 not too many in each row. Outliers are adjusted in each row. This step is optional.
Mclust::mclust. The default methods to extract top n rows are
To perform hierarchical partitioning, run following code:
# code only for demonstration rh = hierarchical_partition(mat, mc.cores = ...) cola_report(rh, output_dir = ..., mc.cores = ...)
For the hierarchical partition, you can only select one partition method and one top-value method.
The default partition method is
kmeans and the default top-value method is
There are examples on real datasets for cola analysis that can be found at https://jokergoo.github.io/cola_examples/.