CorrelationHeatmap_Samples: Function that given a data base of continuos variables,...
In mssm-msf-2019/BiostatsALL:

Description Usage Arguments Examples

Function that given a data base of continuos variables, clusters it by variable and represents the associations as a heatmap-colored correlation matrix in the order established by undupervised clustering analysis. The correlation coeficient appears in the lower diagonal and the p-values based * are in the upper diagonal.

1 2	CorrelationHeatmap_Samples(db, method = "spearman", abs.cor = FALSE, hmeth = "average", max.stars = 0.05, breaks = seq(-1, 1, 0.25), ...)

`db:`	data base of continuos variables; columns are variables to be clustered.
`method:`	correlation method (spearman, perason etc, default to spearman)
`abs.cor:`	should the distance be 1-cor (FALSE,default) or 1-\|cor\| (TRUE). In teh later case, variables negative correlated will be as near as positive correlated
`hmeth:`	aglomeration method to do the k-means clustering. default to average
`max.stars:`	pvalue that will be represented by one asterisc () . If max.stars=0.05 (default), =0.001,=0.01, *=0.05
`breaks:`	color breaks for heatmap, defualt seq(-1,1,0.25);
`print.corrcoef:`	Parameters to control the heatmap aestetics. Logicalin dicating if correlation coeficiets should be printed. Default to TRUE.
`title:`	Parameters to control the heatmap aestetics. title for the plot
`stars.size:`	Parameters to control the heatmap aestetics. size of the text overlay in the figure (correlations and p values)

# in this examples, I want to cluster samples
CorrelationHeatmap_Samples(exprs(reset)[1:10,],print.corrcoef=F) #supress text
# in this examples, I want to cluster genes
CorrelationHeatmap_Samples(t(exprs(reset)[1:10,]), max.stars=0.1,print.corrcoef=F) #***=0.01,**=0.05, *=0.1