TreeSummarizedExperiment-accessors

Description

All accessor functions that work on SingleCellExperiment should work on TreeSummarizedExperiment. Additionally, new accessors rowLinks colLinks, rowTree and colTree accessor function are available for TreeSummarizedExperiment.

Usage

rowLinks(x)

## S4 method for signature 'TreeSummarizedExperiment'
rowLinks(x)

colLinks(x)

## S4 method for signature 'TreeSummarizedExperiment'
colLinks(x)

rowTree(x)

## S4 method for signature 'TreeSummarizedExperiment'
rowTree(x)

colTree(x)

## S4 method for signature 'TreeSummarizedExperiment'
colTree(x)

## S4 method for signature 'TreeSummarizedExperiment,ANY,ANY,ANY'
x[i, j, ...,
  drop = TRUE]

Arguments

Value

Elements from TreeSummarizedExperiment.

Author(s)

Ruizhu HUANG

See Also

TreeSummarizedExperiment SingleCellExperiment

Examples

# the assay table
set.seed(1)
y <- matrix(rnbinom(300,size=1,mu=10),nrow=10)
colnames(y) <- paste(rep(LETTERS[1:3], each = 10), rep(1:10,3), sep = "_")
rownames(y) <- tinyTree$tip.label

# the row data
rowInf <- DataFrame(var1 = sample(letters[1:3], 10, replace = TRUE),
                    var2 = sample(c(TRUE, FALSE), 10, replace = TRUE))
# the column data
colInf <- DataFrame(gg = factor(sample(1:3, 30, replace = TRUE)),
                    group = rep(LETTERS[1:3], each = 10))

# the tree structure on the rows of assay tables
data("tinyTree")

# the tree structure on the columns of assay tables
sampTree <- ape::rtree(30)
sampTree$tip.label <- colnames(y)

# create the TreeSummarizedExperiment object
toy_tse <- TreeSummarizedExperiment(assays = list(y),
                                    rowData = rowInf,
                                    colData = colInf,
                                    rowTree = tinyTree,
                                    colTree = sampTree)

## extract the rowData
(rowD <- rowData(x = toy_tse))

## extract the colData
(colD <- colData(x = toy_tse))

## extract the linkData
# on rows
(rowL <- rowLinks(x = toy_tse))
# on columns
(colL <- colLinks(x = toy_tse))

 ## extract the treeData
# on rows
(rowT <- rowTree(x = toy_tse))
# on columns
(colT <- colTree(x = toy_tse))