Package 'scTreeViz' reference manual

Title:	R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations
Description:	scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters.
Authors:	Jayaram Kancherla [aut, cre], Hector Corrada Bravo [aut], Kazi Tasnim Zinat [aut], Stephanie Hicks [aut]
Maintainer:	Jayaram Kancherla <[email protected]>
License:	Artistic-2.0
Version:	1.13.0
Built:	2024-12-19 04:30:23 UTC
Source:	https://github.com/bioc/scTreeViz

replace if there are NA's in the hierarchy

Description

replace if there are NA's in the hierarchy

Usage

.replaceNAFeatures(replacing_na_obj_fData, feature_order)
.replaceNAFeatures(replacing_na_obj_fData, feature_order)

Arguments

`replacing_na_obj_fData`	hierarchy data table
`feature_order`	order of the tree if different from colnames

Value

a data frame object

Subset TreeIndex

Description

Subset TreeIndex

Generic method to get nodes at a tree level

Method to get nodes at a tree level

Generic method for possible node states

Method to get possible node states a node state is 0 if removed, 1 if expanded to show children & 2 if counts are aggregated to the node

Generic method to split the tree

splitAt divides the TreeIndex into groups defined by the level, node selections and filters(start, end)

Show the TreeIndex object

Usage

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

getNodes(x, ...)

## S4 method for signature 'TreeIndex'
getNodes(x, selectedLevel = NULL)

getNodeStates(x)

## S4 method for signature 'TreeIndex'
getNodeStates(x)

splitAt(x, ...)

## S4 method for signature 'TreeIndex'
splitAt(
  x,
  selectedLevel = 3,
  selectedNodes = NULL,
  start = 1,
  end = NULL,
  format = "list"
)

## S4 method for signature 'TreeIndex'
show(object)
## S4 method for signature 'TreeIndex,ANY,ANY,ANY'
x[i, j, ..., drop = FALSE]

getNodes(x, ...)

## S4 method for signature 'TreeIndex'
getNodes(x, selectedLevel = NULL)

getNodeStates(x)

## S4 method for signature 'TreeIndex'
getNodeStates(x)

splitAt(x, ...)

## S4 method for signature 'TreeIndex'
splitAt(
  x,
  selectedLevel = 3,
  selectedNodes = NULL,
  start = 1,
  end = NULL,
  format = "list"
)

## S4 method for signature 'TreeIndex'
show(object)

Arguments

`x`	TreeIndex object
`i`, `j`	indices to subset or keep
`...`	other parameters
`drop`	drop the dimensions of the object. defaults to FALSE
`selectedLevel`	tree level to select nodes from
`selectedNodes`	used to set states on individual nodes to define a cut on the tree
`start`, `end`	indices to filter nodes by
`format`	return format can be one of "list" or "TreeIndex"
`object`	TreeIndex object

Value

a 'TreeIndex' subset object

a generic

levels at node cut

node state

node states

a generic

a 'TreeIndex' object or type set in format

object description of the 'TreeIndex' object

Examples

library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
getNodes(tree)
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
getNodes(tree)
 
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
splitAt(tree)

 
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
splitAt(tree)

library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
getNodes(tree)
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
getNodes(tree)
 
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
splitAt(tree)

 
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
splitAt(tree)

Creates a new ClusterHierarchy object.

Description

Works as a validation check for multiple issues user passed dataframe might have. For example, multiple root nodes, incompatible naming, multiple parents of a single node, etc This function performs all this checks and tries to resolve the issues by making changes in cluster assignment User can give either col_regex or columns option to filter the columns or specify the column order

Usage

ClusterHierarchy(hierarchy, col_regex = NULL, columns = NULL)
ClusterHierarchy(hierarchy, col_regex = NULL, columns = NULL)

Arguments

`hierarchy`	hierarchy as a dataFrame
`col_regex`	Regular Expression for choosing columns
`columns`	Vector containing list of columns to choose from with ordering

Value

'ClusterHierarchy“ return an object of class ClusterHierarchy containing cluster information that ensures a valid dataframe for treeviz input

Examples

n=64
# create a hierarchy
df<- data.frame(cluster0=rep(1,n))
for(i in seq(1,5)){
  df[[paste0("cluster",i)]]<- rep(seq(1:(2**i)),each=ceiling(n/(2**i)),len=n)
}
clus_hier<-ClusterHierarchy(df, col_regex = "clus")

n=64
# create a hierarchy
df<- data.frame(cluster0=rep(1,n))
for(i in seq(1,5)){
  df[[paste0("cluster",i)]]<- rep(seq(1:(2**i)),each=ceiling(n/(2**i)),len=n)
}
clus_hier<-ClusterHierarchy(df, col_regex = "clus")

ClusterHierarchy class to manage treeviz cluster data

Description

ClusterHierarchy class to manage treeviz cluster data

Creates a 'TreeViz“ object from 'SingleCellExperiment'. Generates clusters based on Walktrap algorithm if no default is provided

Description

Creates a 'TreeViz“ object from 'SingleCellExperiment'. Generates clusters based on Walktrap algorithm if no default is provided

Usage

createFromSCE(
  object,
  check_coldata = FALSE,
  col_regex = NULL,
  columns = NULL,
  reduced_dim = c("TSNE")
)
createFromSCE(
  object,
  check_coldata = FALSE,
  col_regex = NULL,
  columns = NULL,
  reduced_dim = c("TSNE")
)

Arguments

`object`	'SingleCellExperiment' object to be visualized
`check_coldata`	whether to colData of 'SingeCellExperiment' object for cluster information or not
`col_regex`	common regular expression shared across all columns with cluster information
`columns`	vector containing columns with cluster information
`reduced_dim`	Vector of Dimensionality reduction information provided in 'SingeCellExperiment' object to be added in 'TreeViz' (if exists)

Value

'TreeViz' Object

Examples

library(SingleCellExperiment)
library(scater)
sce <- mockSCE()
sce <- logNormCounts(sce)
sce <- runTSNE(sce)
sce <- runUMAP(sce)
set.seed(1000)
for (i in  seq_len(5)) {
clust.kmeans <- kmeans(reducedDim(sce, "TSNE"), centers = i)
sce[[paste0("clust", i)]] <- factor(clust.kmeans$cluster)
}
treeviz <-createFromSCE(sce, check_coldata = TRUE, col_regex = "clust", reduced_dim = c("TSNE", "UMAP"))

library(SingleCellExperiment)
library(scater)
sce <- mockSCE()
sce <- logNormCounts(sce)
sce <- runTSNE(sce)
sce <- runUMAP(sce)
set.seed(1000)
for (i in  seq_len(5)) {
clust.kmeans <- kmeans(reducedDim(sce, "TSNE"), centers = i)
sce[[paste0("clust", i)]] <- factor(clust.kmeans$cluster)
}
treeviz <-createFromSCE(sce, check_coldata = TRUE, col_regex = "clust", reduced_dim = c("TSNE", "UMAP"))

Creates a 'TreeViz' object from 'Seurat'

Description

Creates a 'TreeViz' object from 'Seurat'

Usage

createFromSeurat(
  object,
  check_metadata = FALSE,
  col_regex = "*snn*",
  columns = NULL,
  reduced_dim = c("TSNE")
)
createFromSeurat(
  object,
  check_metadata = FALSE,
  col_regex = "*snn*",
  columns = NULL,
  reduced_dim = c("TSNE")
)

Arguments

`object`	'Seurat' class containing cluster information at different resolutions
`check_metadata`	whether to metaData of 'Seurat' object for cluster information or not
`col_regex`	common regular expression shared across all columns with cluster information
`columns`	vector containing columns with cluster information
`reduced_dim`	Vector of Dimensionality reduction information provided in 'Seurat' object to be added in 'TreeViz' (if exists)

Value

'TreeViz' Object

Examples

library(Seurat)
data(pbmc_small)
pbmc <- pbmc_small
treeviz<- createFromSeurat(pbmc, check_metadata = TRUE, reduced_dim = c("pca","tsne"))
library(Seurat)
data(pbmc_small)
pbmc <- pbmc_small
treeviz<- createFromSeurat(pbmc, check_metadata = TRUE, reduced_dim = c("pca","tsne"))

Creates 'TreeViz' object from hierarchy and count matrix

Description

Provided with a count matrix and a dataframe or 'ClusterHierarchy' object, this module runs the necessary checks on the dataframe and tries to convert it to a tree by making necessary changes. Returns the 'TreeViz' object if a tree is successfully generated from dataframe, throws error otherwise

Usage

createTreeViz(clusters, counts)
createTreeViz(clusters, counts)

Arguments

`clusters`	'ClusterHierarchy' object or a dataframe containing cluster information at different resolutions
`counts`	matrix Dense or sparse matrix containing the count matrix

Value

'TreeViz“ Object

Examples

n=64
# create a hierarchy
df<- data.frame(cluster0=rep(1,n))
for(i in seq_len(5)){
  df[[paste0("cluster",i)]]<- rep(seq(1:(2**i)),each=ceiling(n/(2**i)),len=n)
}
# generate a count matrix
counts <- matrix(rpois(6400, lambda = 10), ncol=n, nrow=100)
colnames(counts)<- seq_len(64)
# create a `TreeViz` object
treeViz <- createTreeViz(df, counts)

n=64
# create a hierarchy
df<- data.frame(cluster0=rep(1,n))
for(i in seq_len(5)){
  df[[paste0("cluster",i)]]<- rep(seq(1:(2**i)),each=ceiling(n/(2**i)),len=n)
}
# generate a count matrix
counts <- matrix(rpois(6400, lambda = 10), ncol=n, nrow=100)
colnames(counts)<- seq_len(64)
# create a `TreeViz` object
treeViz <- createTreeViz(df, counts)

Data container for MRexperiment objects

Description

Used to serve hierarchical data (used in e.g., icicle plots and heatmaps).

Methods

df_to_tree(root, df)

Helper function to recursively build nested response for getHierarchy

root: Root of subtree
df: data.frame containing children to process

get_default_chart_type()

Get name of default chart type for this data type

get_measurements()

Get description of measurements served by this object

getCombined( measurements = NULL, seqName, start = 1, end = 1000, order = NULL, nodeSelection = NULL, selectedLevels = NULL )

Return the counts aggregated to selected nodes for the given samples

measurements: Samples to get counts for
seqName: name of datasource
start: Start of feature range to query
end: End of feature range to query
order: Ordering of nodes
nodeSelection: Node-id and selectionType pairs
selectedLevels: Current aggregation level

getHierarchy(nodeId = NULL)

Retrieve feature hierarchy information for subtree with specified root

nodeId: Feature identifier with level info

getReducedDim(method = NULL, gene = NULL)

Compute PCA over all features for given samples

method: which dimension to access
gene: send expression of a gene back with the dimensions

getRows( measurements = NULL, start = 1, end = 1000, selectedLevels = 3, selections = NULL )

Return the sample annotation and features within the specified range and level for a given sample and features

measurements: Samples to retrieve for
start: Start of feature range to query
end: End of feature range to query
selections: Node-id and selectionType pairs
selectedLevels: Current aggregation level

propagateHierarchyChanges( selection = NULL, order = NULL, selectedLevels = NULL, request_with_labels = FALSE )

Update internal state for hierarchy

selection: Node-id and selectionType pairs
order: Ordering of features
selectedLevels: Current aggregation level
request_with_labels: For handling requests using fData entries from MRexperiment

row_to_dict(row)

Helper function to format each node entry for getHierarchy response

row: Information for current node.

searchTaxonomy(query = NULL, max_results = 15)

Return list of features matching a text-based query

query: String of feature for which to search
max_results: Maximum results to return

Sets gene list for visualization

Description

Sets gene list for visualization

Usage

set_gene_list(treeviz, genes)
set_gene_list(treeviz, genes)

Arguments

`treeviz`	TreeViz object
`genes`	list of genes to use

Value

TreeViz object set with gene list

show object

Description

show object

Method to aggregate a TreeViz object

Generic method to register data to the epiviz data server

plot tree from TreeViz

Usage

## S4 method for signature 'TreeViz'
show(object)

aggregateTree(x, ...)

## S4 method for signature 'TreeViz'
aggregateTree(
  x,
  selectedLevel = 3,
  selectedNodes = NULL,
  aggFun = colSums,
  start = 1,
  end = NULL,
  by = "row",
  format = "TreeViz"
)

## S4 method for signature 'TreeViz'
register(object, tree = "row", columns = NULL, ...)

## S4 method for signature 'TreeViz,ANY'
plot(x, y)
## S4 method for signature 'TreeViz'
show(object)

aggregateTree(x, ...)

## S4 method for signature 'TreeViz'
aggregateTree(
  x,
  selectedLevel = 3,
  selectedNodes = NULL,
  aggFun = colSums,
  start = 1,
  end = NULL,
  by = "row",
  format = "TreeViz"
)

## S4 method for signature 'TreeViz'
register(object, tree = "row", columns = NULL, ...)

## S4 method for signature 'TreeViz,ANY'
plot(x, y)

Arguments

`object`	The object to register to data server
`x`	treeviz object
`...`	Additional arguments passed to object constructors
`selectedLevel`	level to select nodes from
`selectedNodes`	used to set states on individual nodes to define a cut on the tree
`aggFun`	aggregate function to use, by default colSums if by="row", rowSums if by="col"
`start`, `end`	indices to filter nodes
`by`	"row" to aggregate the TreeIndex on rowData, "col" to aggregate TreeIndex on colData
`format`	return format can be one of "counts" or "TreeViz"
`tree`	Is tree over rows or columns of the object (default: "row")
`columns`	Name of columns containing data to register
`y`	none

Value

describe a TreeIndex object

a generic

a Treeviz object or type specified by format

An EpivizTreeData-class object

Dataframe containing cluster information at different resolutions

Functions

show,TreeViz-method:
aggregateTree:
aggregateTree,TreeViz-method:
register,TreeViz-method:
plot,TreeViz,ANY-method:

Examples

 
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)
aggregateTree(mbiome)

 
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)
aggregateTree(mbiome)


library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)
plot(mbiome)

library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)
aggregateTree(mbiome)

 
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)
aggregateTree(mbiome)


library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)
plot(mbiome)

Start treeviz app and create `TreeVizApp` object to manage connection.

Description

Start treeviz app and create TreeVizApp object to manage connection.

Usage

startTreeviz(
  data = NULL,
  genes = NULL,
  top_genes = 100,
  host = "http://epiviz.cbcb.umd.edu/treeviz",
  register_function = .register_all_treeviz_things,
  delay = 2L,
  ...
)
startTreeviz(
  data = NULL,
  genes = NULL,
  top_genes = 100,
  host = "http://epiviz.cbcb.umd.edu/treeviz",
  register_function = .register_all_treeviz_things,
  delay = 2L,
  ...
)

Arguments

`data`	TreeViz object to explore
`genes`	(character vector) genes (rownames) to include in heatmap
`top_genes`	(integer) number of top variable genes to include in the heatmap
`host`	(character) host address to launch.
`register_function`	(function) function used to register actions and charts on the treeviz app.
`delay`	(integer) number of seconds to wait for application to load in browser
`...`	additional parameters passed to `startEpiviz`.

Value

An object of class TreeVizApp

Examples

# see package vignette for example usage
app <- startTreeviz(non_interactive=TRUE, open_browser=FALSE)
app$stop_app()

# see package vignette for example usage
app <- startTreeviz(non_interactive=TRUE, open_browser=FALSE)
app$stop_app()

create a new TreeIndex object

Description

create a new TreeIndex object

Usage

TreeIndex(hierarchy = NULL, feature_order = NULL)
TreeIndex(hierarchy = NULL, feature_order = NULL)

Arguments

`hierarchy`	hierarchy as a data.table
`feature_order`	order of the tree if different from colnames

Value

a 'TreeIndex' object

Examples

library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)

TreeIndex class to manage and query hierarchical data

Description

TreeIndex class to manage and query hierarchical data

The TreeViz class.

Description

SummarizedExperiment-like class for datasets that have hierarchies on either rowData or colData. For microbiome data, rowData is a tree hierarchy For single cell data, colData is a tree hierarchy

Usage

TreeViz(assays = SimpleList(), rowData = NULL, colData = NULL, ...)
TreeViz(assays = SimpleList(), rowData = NULL, colData = NULL, ...)

Arguments

`assays`	simple list of counts
`rowData`	rowData
`colData`	colData
`...`	other parameters for SummarizedExperiment

Value

a 'TreeViz' object

Examples

library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)
library(metagenomeSeq)
data(mouseData)
counts <- MRcounts(mouseData)
hierarchy <- fData(mouseData)
tree <- TreeIndex(hierarchy)
mbiome <- TreeViz(SimpleList(counts=counts), rowData=tree)

TreeViz class wrapper for SummarizedExperiment objects

Description

TreeViz class wrapper for SummarizedExperiment objects

Class managing connection to metaviz application.

Description

Class managing connection to metaviz application.

Methods

plotGene(gene = NULL, datasource_name = "SCRNA_1")

Plot a bar plot for a gene across cell types

gene: gene to extract expression values
datasource_name: object to extract from (automatically selected)

`hierarchy_tree`	hierarchy as a data.table
`node_ids_table`	node ids
`nodes_table`	nodes table
`feature_order`	order of the tree if different from colnames

Package 'scTreeViz'

Help Index

generate hierarchy tree

Description

Usage

Arguments

Value

generate leaf of table

Description

Usage

Arguments

Value

generate node ids in the tree

Description

Usage

Arguments

Value

generate nodes table tree

Description

Usage

Arguments

Value

replace if there are NA's in the hierarchy

Description

Usage

Arguments

Value

Subset TreeIndex

Description

Usage

Arguments

Value

Examples

Creates a new ClusterHierarchy object.

Description

Usage

Arguments

Value

Examples

ClusterHierarchy class to manage treeviz cluster data

Description

Creates a 'TreeViz“ object from 'SingleCellExperiment'. Generates clusters based on Walktrap algorithm if no default is provided

Description

Usage

Arguments

Value

Examples

Creates a 'TreeViz' object from 'Seurat'

Description

Usage

Arguments

Value

Examples

Creates 'TreeViz' object from hierarchy and count matrix

Description

Usage

Arguments

Value

Examples

Data container for MRexperiment objects

Description

Methods

Sets gene list for visualization

Description

Usage

Arguments

Value

show object

Description

Usage

Arguments

Value

Functions

Examples

Start treeviz app and create TreeVizApp object to manage connection.

Description

Usage

Arguments

Value

See Also

Start treeviz app and create `TreeVizApp` object to manage connection.