Plot_column_joint: Plot the joint distribution between matrix columns.

Description Usage Arguments Details Value See Also Examples

View source: R/Plot_column_joint.R

Description

Plot_column_joint is a function used to non-supervised learn / cluster the relationship between columns. (Insight into the joint distribution between samples)

Usage

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Plot_column_joint(M, METRIC = "euclidean", VISUAL = "MDS", HDER = "",
  GROUP_LABEL = NULL, ROW_VAR_Q = NULL)

Arguments

M

A matrix.

METRIC

The metric used in clustering, can be one in "euclidean", "pearson", and "binary". The pearson metric is calculated as the euclidean metric after rescaling the columns.

HDER

The subtitle and the file name of the plot.

GROUP_LABEL

Optional, a vector used for colour labeling or faceting the dendrogram or MDS plot.

ROW_VAR_Q

Optional, a real value between 0 and 1, indicating the quantile of row variance used to filter the rows.

CLUSTER

The clustering method, can be one in "MDS" and "dendrogram".

Details

By default, the column names of the matrix M will be used as the sample labels, other wise, it will use V_1:ncol(M).

Value

A plot for column wised clustering analysis.

See Also

Plot_column_marginal

Examples

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Matrix_ex <- matrix(rnorm(9000),300,30)
Group_lab <- paste0( "V_",rep(1:10,each = 3) )
Plot_column_joint( Matrix_ex, "euclidean", "dendrogram", "Test1", Group_lab )
Plot_column_joint( Matrix_ex, "pearson", "MDS", "Test2", Group_lab )

ZhenWei10/meripQC documentation built on April 18, 2018, 9:37 p.m.