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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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 <Wenfei.Zhang@sanofi.com>, Weiliang Qiu <Weiliang.Qiu@sanofi.com>, Xuan Lin <Xuan.Lin@sanofi.com>, Donghui Zhang <Donghui.Zhang@sanofi.com>

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40``` ```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.