Dendro: Compare Groups Based on Dendrogram

In statVisual: Statistical Visualization Tools

Description

Compare groups based on dendrogram. The nodes of the dendrogram will be colored by group.

Usage

Dendro(
    x, 
    group = NULL, 
    xlab = NULL, 
    ylab = NULL, 
    title = NULL, 
    cor.use = "pairwise.complete.obs", 
    cor.method = "pearson", 
    distance = "rawdata", 
    distance.method = "euclidean", 
    hclust.method = "complete", 
    yintercept = NULL, 
    theme_classic = TRUE, 
    addThemeFlag = TRUE,
    ...)

Arguments

x	A data frame. Rows are subjects; Columns are variables describing the subjects.
group	character. The column name of data that indicates the subject groups. The nodes of the dendrogram will be colored by info provided by group.
xlab	x axis label
ylab	y axis label
title	title of the plot
cor.use	character. Indicate which data will be used to compute correlation coefficients. It can take values “everything”, “all.obs”, “complete.obs”, “na.or.complete”, “pairwise.complete.obs”.
cor.method	character. Indicate which type of correlation coefficients will be calculated: “pearson”, “kendall”, “spearman”.
distance	character. Indicate which type of data will be used to calculate distance: “rawdata” (i.e., using raw data to calculate distance), “cor” (i.e., using correlation coefficients as distance), “1-cor” (i.e., using (1-correlation coefficients) as distance), “1-|cor|” (i.e., using (1-|correlation coefficients|) as distance).
distance.method	character. Available when ‘distance = "rawdata"’. Indicate the definition of distance: distance used in calculate dist “rawdata” (i.e., using raw data to calculate distance), “cor” (i.e., using correlation coefficients as distance), “1-cor” (i.e., using (1-correlation coefficients) as distance), “1-|cor|” (i.e., using (1-|correlation coefficients|) as distance).
hclust.method	character. Indicate which agglomeration method will be used to perform hierarchical clustering. This should be (an unambiguous abbreviation of) one of “ward.D”, “ward.D2”, “single”, “complete”, “average”, “mcquitty”, “median”, or “centroid”. Please refer to hclust.
yintercept	numeric. A line indicating the height of the dendrogram, for example, indicating where the dendrogram should be cut to obtain clusters.
theme_classic	logical. Use classic background without grids (default: TRUE).
addThemeFlag	logical. Indicates if light blue background and white grid should be added to the figure.
...	other input parameters for facet & theme

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet plot_env, and labels.

Author(s)

Wenfei Zhang <[e-mail]>, Weiliang Qiu <[e-mail]>, Xuan Lin <[e-mail]>, Donghui Zhang <[e-mail]>

Examples

data(esSim)
print(esSim)

# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])

# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])

# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])

# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]

print(pDat[1:2, ])

# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))

pDat$grp = factor(pDat$grp)

statVisual(type = 'Dendro', 
           x = pDat[, c(3:8)], 
           group = pDat$grp)

Dendro(
       x = pDat[, c(3:8)], 
       group = pDat$grp)

statVisual documentation built on Feb. 21, 2020, 1:08 a.m.