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 |

`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.

1 2 3 4 | ```
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 )
``` |

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