Plot_column_joint: Plot the joint distribution between matrix columns.
In ZhenWei10/meripQC: Quality control and inference for m6A-seq datasets.

Description Usage Arguments Details Value See Also Examples

View source: R/Plot_column_joint.R

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

1 2	Plot_column_joint(M, METRIC = "euclidean", VISUAL = "MDS", HDER = "", GROUP_LABEL = NULL, ROW_VAR_Q = NULL)

`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".

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).

A plot for column wised clustering analysis.

Plot_column_marginal

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 )