heatmap_clustering

Hierarchical clustering of lipid species or characteristics derived from two/multiple groups.

Usage

heatmap_clustering(
  de_se,
  char,
  distfun = c("pearson", "kendall", "spearman"),
  hclustfun = c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty",
    "median", "centroid"),
  type = c("all", "sig")
)

Arguments

de_se

The resulting SummarizedExperiment object from the differential expression analysis function, such as deSp_twoGroup, deSp_multiGroup, deChar_twoGroup, and deChar_multiGroup.

char

Character. A lipid characteristic selected from the common list returned by list_lipid_char.

distfun

Character. The distance measure for computing correlation coefficient (or covariance). Allowed methods include "pearson", "kendall", and "spearman". Default is 'pearson'

hclustfun

Character. The agglomeration method. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC), or "centroid" (= UPGMC). Default is 'complete'

type

Character. It must be 'all' or 'sig.' 'all' for output the results of all lipid species or characteristics, and 'sig' for output the results of significant lipid species or characteristics.

Value

Return a list with 1 figure and 1 matrix.

1. interactive_heatmap & static_heatmap: a heatmap provides an overview of all/significant lipid species or characteristics that illustrates the differences between groups.

2. corr_coef_matrix: the matrix of the heatmap.

Examples

data("de_data_twoGroup")
processed_se <- data_process(
    de_data_twoGroup, exclude_missing=TRUE, exclude_missing_pct=70,
    replace_na_method='min', replace_na_method_ref=0.5,
    normalization='Percentage', transform='log10')
deSp_se <- deSp_twoGroup(processed_se, ref_group='ctrl', test='t-test',
    significant='padj', p_cutoff=0.05, FC_cutoff=2, transform='log10')
result <- heatmap_clustering(de_se=deSp_se, char='class', distfun='pearson',
    hclustfun='complete', type='sig')