**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:

- Adjust the matrix. In this step, rows with too many
`NA`

s 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). - 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/.