plot.eclust {eclust} | R Documentation |
Plot Heatmap of Cluster Summaries by Exposure Status
Description
Plots cluster summaries such as the 1st principal component or
average by exposure status. This is a plot method for object of class
eclust returned by the r_cluster_data
function. Two heatmaps,
side-by-side are returned, where the first heatmap corresponds to the
unexposed subjects and the second heatmap corresponds to the exposed
subjects.
Usage
## S3 method for class 'eclust'
plot(x, type = c("ECLUST", "CLUST"), summary = c("pc",
"avg"), sample = c("training", "test"), unexposed_title = "E=0",
exposed_title = "E=1", ...)
Arguments
x |
object of class |
type |
show results from the "ECLUST" (which considers the environment)
or "CLUST" (which ignores the environment) methods. Default is "ECLUST".
See |
summary |
show the 1st principal component or the average of each cluster. Default is "pc". |
sample |
which sample to show, the "training" or the "test" set. Default
is "training". This is determined by the |
unexposed_title |
The title for the unexposed subjects heatmap. Default is "E=0". |
exposed_title |
The title for the exposed subjects heatmap. Default is "E=1". |
... |
other arguments passed to the
|
Details
Rows are the cluster summaries and columns are the subjects. This
function determines the minimum and maximum value for the whole dataset and
then creates a color scale using those values with the
colorRamp2
. This is so that both heatmaps are on
the same color scale, i.e., each color represents the same value in both
heatmaps. This is done for being able to visually compare the results.
Value
a plot of two Heatmaps, side-by-side, of the cluster summaries by exposure status
Examples
## Not run:
data("tcgaov")
tcgaov[1:5,1:6, with = FALSE]
Y <- log(tcgaov[["OS"]])
E <- tcgaov[["E"]]
genes <- as.matrix(tcgaov[,-c("OS","rn","subtype","E","status"),with = FALSE])
trainIndex <- drop(caret::createDataPartition(Y, p = 1, list = FALSE, times = 1))
testIndex <- setdiff(seq_len(length(Y)),trainIndex)
cluster_res <- r_cluster_data(data = genes,
response = Y,
exposure = E,
train_index = trainIndex,
test_index = testIndex,
cluster_distance = "tom",
eclust_distance = "difftom",
measure_distance = "euclidean",
clustMethod = "hclust",
cutMethod = "dynamic",
method = "average",
nPC = 1,
minimum_cluster_size = 60)
class(cluster_res)
plot(cluster_res, show_column_names = FALSE)
## End(Not run)