machine_learning_pipeline: Learning pipeline

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

Description

Performs and evaluates kmeans clustering with a given combination of gene filtering 2, distance metrics, transformation and number of dimensions used in clustering.

Usage

machine_learning_pipeline(dataset, sel, distan, clust, n.dim)

Arguments

dataset	Name of the dataset. Either "quake", "sandberg", "bernstein" or "linnarsson"
sel	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")
n.dim	Number of dimension of the transformed distance matrix which is used in kmeans clustering.

Value

Two small files (depending on the transformation method) containing a set of clustering evaluation indecies (*-inds.txt) together with known and calculated clustering labels of the cells (*-labs.txt). Evaluation indecies are the following:

ari

Adjusted Rand Index

rand

Rand Index

jaccard

Jaccard Index

dunn

Dunn Index

davies_bouldin

Davies Bouldin Index

silhouette

Silhouette Index

Examples

machine_learning_pipeline("quake", "none", "spearman", "spectral", 4)

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