node_partition: Partition the matrix

In jokergoo/simplifyEnrichment: Simplify Functional Enrichment Results

Partition the matrix

Description

Partition the matrix

Usage

partition_by_kmeans(mat, n_repeats = 10)

partition_by_pam(mat)

partition_by_hclust(mat)

partition_by_kmeanspp(mat)

Arguments

mat	The submatrix in the binary cut clustering process.
n_repeats	Number of repeated runs of k-means clustering.

Details

These functions can be set to the partition_fun argument in binary_cut().

partition_by_kmeans(): Since k-means clustering brings randomness, this function performs k-means clustering several times (controlled by n_repeats) and uses the final consensus partitioning results.

partition_by_pam(): The clustering is performed by cluster::pam() with the pamonce argument set to 5.

partition_by_hclust(): The "ward.D2" clusering method was used.

partition_by_kmeanspp(): It uses the kmeanspp method from the flexclust package.

Value

All partitioning functions split the matrix into two groups and return a categorical vector of labels of 1 and 2.

jokergoo/simplifyEnrichment documentation built on Sept. 16, 2024, 8:39 a.m.