Package 'iSEEfier'

Constant values used throughout iSEEfier

Description

Constant values used throughout iSEEfier

Usage

iSEE_panel_colors

Format

An object of class character of length 17.

Panel colors

• color values (as string character or hex value) for the panels included by default in iSEE and iSEEu

---

Glue together initial objects into one

Description

Glue a set of initial configuration objects, combining them into a single valid initial set.

Usage

glue_initials(
  ...,
  remove_duplicate_panels = TRUE,
  verbose = TRUE,
  custom_panels_allowed = NULL
)

Arguments

...	A set of initial list objects (in the format that is required to be passed as a parameter in the call to iSEE::iSEE()) - just as in the behavior of the c()/paste() function
remove_duplicate_panels	Logical, defaults to TRUE. Defines the behavior to remove panels detected as duplicated. Can be relevant upon concatenating mid to large sets of panels.
verbose	Logical, defaults to TRUE. If on, prints out a series of informative messages to describe the actions undertaken upon running.
custom_panels_allowed	Character vector, defaults to NULL. Can be used to specify additional panels to be allowed in the concatenation.

Details

The usage of custom_panels_allowed can be especially relevant when one creates one or more custom panels, with a specific name that needs to be indicated in this parameter. For example, if using a panel of class FancyPlotPanel and one called FancyTablePanel, the value for custom_panels_allowed should be set to c("FancyPlotPanel", "FancyTablePanel").

It is worth mentioning that iSEE::iSEE() is actually able to handle the automatic renaming of panels that could be detected as duplicated. This can basically relax the requirement on the "uniqueness" of the configured panels, with the only caveat of having to think of how the transmissions between panels will be handled; nevertheless, most users might not even need to face this situation.

Value

A single initial list object, in the format that is required to be passed as a parameter in the call to iSEE::iSEE(), concatenating the values provided as input.

Examples

## Load a dataset and preprocess this quickly
sce <- scRNAseq::RichardTCellData()
sce <- scuttle::logNormCounts(sce)
sce <- scater::runPCA(sce)
sce <- scater::runTSNE(sce)
## Select some features and aspects to focus on
gene_list_1 <- c("ENSMUSG00000026581")
gene_list_2 <- c("ENSMUSG00000005087", "ENSMUSG00000015437")
cluster <- "stimulus"
group <- "single cell quality"
initial1 <- iSEEinit(sce = sce,
                     features = gene_list_1,
                     clusters = cluster,
                     groups = group)
initial2 <- iSEEinit(sce = sce,
                     features = gene_list_2,
                     clusters = cluster,
                     groups = group)
initials_merged <- glue_initials(initial1,
                                 initial2)
view_initial_tiles(initial1)
view_initial_tiles(initial2)
view_initial_tiles(initials_merged)

## Continue your exploration directly within iSEE!
if (interactive())
  iSEE(sce, initial = initial_merged)

---

iSEEinit: Create an initial state of an iSEE instance for gene expression visualization

Description

iSEEinit() defines the initial setup of an iSEE instance, recommending tailored visual elements to effortlessly illustrate the expression of a gene list in a single view.

Usage

iSEEinit(
  sce,
  features,
  reddim_type = "TSNE",
  clusters = colnames(colData(sce))[1],
  groups = colnames(colData(sce))[1],
  gene_id = "id",
  add_markdown_panel = FALSE
)

Arguments

sce	SingleCellExperiment object
features	A character vector or a data.frame containing a list of genes. If features is a data.frame, the column containing the gene names must be named "id"
reddim_type	A string vector containing the dimensionality reduction type
clusters	A character string containing the name of the clusters/cell-type/state...(as listed in the colData of the sce)
groups	A character string of the groups/conditions...(as it appears in the colData of the sce)
gene_id	A character string containing the name of the column name containing gene names/ids, when 'features' is a data.frame
add_markdown_panel	A logical indicating whether or not to include the MarkdownBoard panel in the initial configuration

Value

A list of "Panel" objects specifying the initial state of iSEE instance

Examples

sce <- scRNAseq::RichardTCellData()
sce <- scuttle::logNormCounts(sce)
sce <- scater::runPCA(sce)
sce <- scater::runTSNE(sce)
gene_list <- c("ENSMUSG00000026581",
               "ENSMUSG00000005087",
               "ENSMUSG00000015437")
cluster <- "stimulus"
group <- "single cell quality"
initial <- iSEEinit(sce = sce, features = gene_list, clusters = cluster, groups = group)

---

iSEEmarker

Description

iSEEmarker() creates an initial state of an iSEE instance for interactive exploration of marker genes through the DynamicMarkerTable panel, synchronizing selections with a ReducedDimensionPlot and a FeatureAssayPlot to visualize the expression of selected marker genes

Usage

iSEEmarker(
  sce,
  reddim_type = "TSNE",
  clusters = colnames(colData(sce))[1],
  groups = colnames(colData(sce))[1],
  selection_plot_format = c("ColumnDataPlot", "ReducedDimensionPlot")
)

Arguments

sce	SingleCellExperiment object
reddim_type	A string vector containing the dimensionality reduction
clusters	A character string containing the name of the clusters/cell-type/state...(as listed in the colData of the sce)
groups	A character string of the groups/conditions...(as it appears in the colData of the sce)
selection_plot_format	A string character containing the class of the panel. It can be either ColumnDataPlot or ReducedDimensionPlot

Value

A list of "Panel" objects specifying the initial state of iSEE instance

Examples

sce <- scRNAseq::RichardTCellData()
sce <- scuttle::logNormCounts(sce)
sce <- scater::runPCA(sce)
sce <- scater::runTSNE(sce)
cluster <- "stimulus"
group <- "single cell quality"
initial <- iSEEmarker(sce = sce, clusters = cluster, groups = group,
selection_plot_format = "ColumnDataPlot")

---

iSEEnrich

Description

iSEEnrich() creates an initial state of an iSEE instance for interactive exploration of feature sets extracted from GO and KEGG database, displaying all associated genes in a RowDataTable panel.

Usage

iSEEnrich(
  sce,
  collection = c("GO", "KEGG"),
  organism = "org.Hs.eg.db",
  gene_identifier = "ENTREZID",
  clusters = colnames(colData(sce))[1],
  groups = colnames(colData(sce))[1],
  reddim_type = "PCA"
)

Arguments

sce	SingleCellExperiment object
collection	A character vector specifying the gene set collections of interest (GO,KEGG)
organism	A character string of the org.*.eg.db package to use to extract mappings of gene sets to gene IDs.
gene_identifier	A character string specifying the identifier to use to extract gene IDs for the organism package
clusters	A character string containing the name of the clusters/cell-type/state...(as listed in the colData of the sce)
groups	A character string of the groups/conditions...(as it appears in the colData of the sce)
reddim_type	A string vector containing the dimensionality reduction type

Value

A list of "Panel" objects specifying the initial state of iSEE instance

Examples

sce <- scRNAseq::RichardTCellData()
sce <- scuttle::logNormCounts(sce)
sce <- scater::runPCA(sce)
GO_collection <- "GO"
Mm_organism <- "org.Mm.eg.db"
gene_id <- "SYMBOL"
clusters <- "stimulus"
groups <- "single cell quality"
reddim_type <- "PCA"
results <- iSEEnrich(sce = sce,
                     collection = GO_collection,
                     organism = Mm_organism,
                     gene_identifier = gene_id,
                     clusters = clusters,
                     groups = groups,
                     reddim_type = reddim_type)

---

View an initial object as a network

Description

Translates the layout of the initial configuration object as a networks, representing panels as nodes and links between them as edges.

Usage

view_initial_network(initial, plot_format = c("igraph", "visNetwork", "none"))

Arguments

initial	An initial list object, in the format that is required to be passed as a parameter in the call to iSEE::iSEE().
plot_format	Character string, one of igraph, visNetwork, or none. Defaults to igraph. Determines the format of the visual representation generated as a side effect of this function - it can be the output of the plot() function for igraph objects, or an interactive widget created via visNetwork::visNetwork().

Details

Panels are the nodes, with color and names to identify them easily. The connections among panels are represented through directed edges. This can be a compact visualization to obtain an overview for the configuration, without the need of fully launching the app and loading the content of all panels

This function is particularly useful with mid-to-large initial objects, as they can be quickly generated in a programmatic manner via the iSEEinit() provided in this package.

Value

An igraph object, underlying the visual representation provided.

See Also

view_initial_tiles()

Examples

## Load a dataset and preprocess this quickly
sce <- scRNAseq::RichardTCellData()
sce <- scuttle::logNormCounts(sce)
sce <- scater::runPCA(sce)
sce <- scater::runTSNE(sce)
## Select some features and aspects to focus on
gene_list <- c("ENSMUSG00000026581", "ENSMUSG00000005087", "ENSMUSG00000015437")
cluster <- "stimulus"
group <- "single cell quality"
initial <- iSEEinit(sce = sce,
                    features = gene_list,
                    clusters = cluster,
                    groups = group)

g_init <- view_initial_network(initial)
g_init

view_initial_network(initial, plot_format = "visNetwork")

## Continue your exploration directly within iSEE!
if (interactive())
  iSEE(sce, initial = initial)

---

View an initial object as a set of tiles

Description

Previews the layout of the initial configuration object in a graphical form.

Usage

view_initial_tiles(initial)

Arguments

initial

An initial list object, in the format that is required to be passed as a parameter in the call to iSEE::iSEE().

Details

Tiles are used to represent the panel types, and reflect the values of their width. This can be a compact visualization to obtain an overview for the configuration, without the need of fully launching the app and loading the content of all panels

This function is particularly useful with mid-to-large initial objects, as they can be quickly generated in a programmatic manner via the iSEEinit() provided in this package.

Value

A ggplot object, representing a schematic view for the initial object.

See Also

view_initial_network()

Examples

## Load a dataset and preprocess this quickly
sce <- scRNAseq::RichardTCellData()
sce <- scuttle::logNormCounts(sce)
sce <- scater::runPCA(sce)
sce <- scater::runTSNE(sce)
## Select some features and aspects to focus on
gene_list <- c("ENSMUSG00000026581",
               "ENSMUSG00000005087",
               "ENSMUSG00000015437")
cluster <- "stimulus"
group <- "single cell quality"
initial <- iSEEinit(sce = sce,
                    features = gene_list,
                    clusters = cluster,
                    groups = group)

view_initial_tiles (initial)

## Continue your exploration directly within iSEE!
if (interactive())
  iSEE(sce, initial = initial)

---