cluster_real_data: Cluster real data

In wikiselev/clustools: Tools for cluster analysis of single cell high throughput data

Description

Performs kmeans clustering of real data with a given combination of filtering 1, filtering 2, distance metrics, transformation, number of clusters and number of dimensions used in clustering.

Usage

cluster_real_data(d, min.cells, max.cells, min.reads, filter1, filter2, distan,
  clust, k, n.dim)

Arguments

d	Single-cell RNA-Seq dataset.
min.cells	Minimum number of cells in which a given gene is expressed.
max.cells	Maximum number of cells in which a given gene is expressed.
min.reads	Minimum number of reads per gene per cell.
filter1	Defines whether or not to perform filter1 (see ?gene_filter1 for description)
filter2	Selection method used by gene_filter2() function (either "none", "correlation", "variance", "variance_weight", "shannon_weight")
distan	Distance metrics for calculating a distance matrix (either "pearson", "spearman", "euclidean", "manhattan" or "minkowski").
clust	Distance matrix transformation method (either "pca", "spectral", "spectral_reg" or "mds")
k	Number of clusters
n.dim	Number of dimension of the transformed distance matrix which is used in kmeans clustering.

Value

kmeans results

Examples

res <- cluster_real_data(quake, 3, 3, 2, "none", "spearman", "spectral", 5, 4)

wikiselev/clustools documentation built on May 4, 2019, 5:25 a.m.