Package 'wilson' reference manual

Title:	Web-Based Interactive Omics Visualization
Description:	Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data. This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.
Authors:	Hendrik Schultheis [aut, cre], Jens Preussner [aut], Looso Mario [aut]
Maintainer:	Hendrik Schultheis <[email protected]>
License:	MIT + file LICENSE
Version:	2.4.2
Built:	2025-03-09 06:14:28 UTC
Source:	https://github.com/loosolab/wilson

AND module server logic

Description

This function evaluates output from multiple OR modules by combining with a logical and.

Usage

and(
  input,
  output,
  session,
  data,
  show.elements = NULL,
  element.grouping = NULL,
  column.labels = NULL,
  delimiter = NULL,
  multiple = TRUE,
  contains = FALSE,
  ranged = FALSE,
  step = 100,
  reset = NULL
)
and(
  input,
  output,
  session,
  data,
  show.elements = NULL,
  element.grouping = NULL,
  column.labels = NULL,
  delimiter = NULL,
  multiple = TRUE,
  contains = FALSE,
  ranged = FALSE,
  step = 100,
  reset = NULL
)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`data`	The input data.frame for which selection should be provided. Evaluates an OR module for each column (Supports reactive).
`show.elements`	A Vector of column names determining which OR modules are shown. Defaults to names(data). (Supports reactive)
`element.grouping`	Group features in boxes. (Data.table: first column = columnnames, second column = groupnames) (Supports reactive)
`column.labels`	Additional labels for the columns, defaults to `names(data)`.
`delimiter`	A single character, or a vector indicating how column values are delimited. (Fills vector sequentially if needed)(Supports reactive)
`multiple`	Whether or not textual ORs should allow multiple selections. (Fills vector sequentially if needed)(Supports reactive)
`contains`	Whether or not textual ORs are initialized as textInput checking entries for given string. (Fills vector sequentially if needed)(Supports reactive)
`ranged`	Whether or not numeric ORs are ranged. (Fills vector sequentially if needed)(Supports reactive)
`step`	Set numeric ORs slider steps. (Fills vector sequentially if needed)(Supports reactive)
`reset`	Reactive which will cause a UI reset on change.

Value

A reactive containing named list with a boolean vector of length nrow(data) (bool), indicating whether an observation is selected or not and a vector of Strings showing the used filter (text).

The AND module connects filtering and selection across multiple columns of a data.frame. Columns of class boolean, character or factor will be represented as textual ORs, numeric columns as numerical OR.

Usage

andUI(id)
andUI(id)

Arguments

`id`	The ID of the modules namespace.

Value

A list with HTML tags from tag.

Function to generate categorical (qualitative) color palettes

Description

Function to generate categorical (qualitative) color palettes

Usage

categoricalPalettes(n)
categoricalPalettes(n)

Arguments

`n`	Number of colors to generate

Value

A data.table with (named) color palettes of length n

Clarion R6-class definition

Description

Use this to create a clarion object. This object is used by all top-level wilson modules.

Constructor

Clarion$new(header = NULL, metadata, data, validate = TRUE)

Constructor Arguments

Variable	Return
`header`	A named list. Defaults to NULL.
`metadata`	Clarion metadata in form of a data.table.
`data`	Data.table according to metadata.
`validate`	Logical value to validate on initialization. Defaults to TRUE.

Public fields

header: List of global information regarding the whole experiment.
metadata: Data.table with additional information for each column.
data: Data.table containing experiment result data.

Methods

Method `get_id()`

Returns name of unique identifier column. Assumes first feature to be unique if not specified.

Usage

Clarion$get_id()

Returns

Name of the id column.

Method `get_name()`

Returns name of name column. If not specified return unique Id.

Usage

Clarion$get_name()

Returns

Name of the name column.

Method `get_delimiter()`

Return delimiter used within multi-value fields (no delimiter = NULL).

Usage

Clarion$get_delimiter()

Method `is_delimited()`

Logical whether the given column name is delimited.

Usage

Clarion$is_delimited(x)

Arguments

x: Name of the column.

Returns

boolean

Method `get_factors()`

Get factors to all columns.

Usage

Clarion$get_factors()

Details

Named factors (e.g. factor1="name") will be cropped to their name.

Returns

Returns a data.table columns: key and factor(s) if any.

Method `get_level()`

Get level(s) to given column name(s).

Usage

Clarion$get_level(column)

Arguments

column: One or more column name(s).

Returns

Provide a vector of levels to the given columnnames in column. Returns NA for missing columns and character(0) if column = NULL.

Method `get_label()`

Get label(s) to given column name(s).

Usage

Clarion$get_label(column = NULL, sub_label = TRUE, sep = " ")

Arguments

column: One or more column name(s).
sub_label: Whether the sub_label should be included.
sep: Separator between label and sub_label.

Details

If a column does not have a label the key is returned.

Returns

Provides a vector of labels (+ sub_label) to the given columnnames in column. Returns NA for missing columns and all labels if column = NULL.

Method `validate()`

Check the object for inconsistencies.

Usage

Clarion$validate(solve = TRUE)

Arguments

solve: For solve = TRUE try to resolve some warnings.

Method `new()`

Initialize a new clarion object.

Usage

Clarion$new(header = NULL, metadata, data, validate = TRUE)

Arguments

header: A named list. Defaults to NULL.
metadata: Clarion metadata in form of a data.table.
data: Data.table according to metadata.
validate: Logical value to validate on initialization. Defaults to TRUE.

Returns

Clarion object.

Method `write()`

Save the object as a clarion file.

Usage

Clarion$write(file)

Arguments

file: Filename for the file to be written.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Clarion$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

## Not run: 
# initializing a new object
object <- Clarion$new(header, metadata, data, validate = TRUE)

# create a deep copy
object_copy <- object$clone(deep = TRUE)

## End(Not run)

## Not run: 
# initializing a new object
object <- Clarion$new(header, metadata, data, validate = TRUE)

# create a deep copy
object_copy <- object$clone(deep = TRUE)

## End(Not run)

colorPicker module server logic

Description

Provides server logic for the colorPicker2 module.

Usage

colorPicker(
  input,
  output,
  session,
  num.colors = 256,
  distribution = "all",
  winsorize = NULL,
  selected = NULL
)
colorPicker(
  input,
  output,
  session,
  num.colors = 256,
  distribution = "all",
  winsorize = NULL,
  selected = NULL
)

Arguments

`input`	Shiny's input object
`output`	Shiny's output object
`session`	Shiny's session object
`num.colors`	Define length of colorpalette vector (Default = 256).
`distribution`	Decide which palettes are selectable. One or more of list("sequential", "diverging", "categorical"). Defaults to "all" (Supports reactive).
`winsorize`	Numeric vector of two. Dynamically change lower and upper limit (supports reactive). Defaults to NULL.
`selected`	Set the default selected palette.

Details

A custom colorpalette's return will be NULL if there is something wrong with it.

equalize will be returned as FALSE if not selected.

Value

Reactive containing list(palette = c(colors), name = palette_name, transparency = Integer, reverse = Boolean, winsorize = NULL or a two-component vector containing lower and upper limits).

colorPicker module UI representation

Description

The functions creates HTML tag definitions of its representation based on the parameters supplied. Currently, two UI can be created for the user to choose either (a) colors from a given color scheme, or (b) choose one or more single colors.

Usage

colorPickerUI(
  id,
  label = "Color scheme",
  custom = FALSE,
  multiple = FALSE,
  show.reverse = TRUE,
  show.scaleoptions = TRUE,
  show.transparency = TRUE
)
colorPickerUI(
  id,
  label = "Color scheme",
  custom = FALSE,
  multiple = FALSE,
  show.reverse = TRUE,
  show.scaleoptions = TRUE,
  show.transparency = TRUE
)

Arguments

`id`	The ID of the modules namespace.
`label`	Either a character vector of length one with the label for the color scheme dropdown, or a character vector containing labels of the single colors.
`custom`	Boolean if TRUE custom colors can be selected (Default = FALSE).
`multiple`	Boolean value, if set to TRUE custom colorpalettes can be made. Only if custom = TRUE (Default = FALSE).
`show.reverse`	Logical, whether or not to show the reverse switch (Default = TRUE).
`show.scaleoptions`	Logical, whether or not to show color scaling option winsorize (Default = TRUE).
`show.transparency`	Logical, whether or not to show the transparency slider (Default = TRUE).

Value

A list with HTML tags from tag.

columnSelector module server logic

Description

columnSelector module server logic

Usage

columnSelector(
  input,
  output,
  session,
  type.columns,
  type = NULL,
  column.type.label = "Type of Column",
  label.label = "Label",
  multiple = TRUE,
  none = FALSE,
  sep = ", ",
  suffix = NULL
)
columnSelector(
  input,
  output,
  session,
  type.columns,
  type = NULL,
  column.type.label = "Type of Column",
  label.label = "Label",
  multiple = TRUE,
  none = FALSE,
  sep = ", ",
  suffix = NULL
)

Arguments

`input`	Shiny's input object
`output`	Shiny's output object
`session`	Shiny's session object
`type.columns`	data.table: (Supports reactive) key = columnnames (id) level = datalevel/ type of column label = optional, used instead of id sub_label = optional, added to id/ label
`type`	The type (contrast/group/sample of the type dropdown menu, selected in step 1 (upper dropdown). Defaults to unique(type.columns[,2]) (Supports reactive)
`column.type.label`	Changes the label of the first UI element
`label.label`	Change label above label text input.
`multiple`	Boolean value whether multiple values can be selected in second selector. (Default = TRUE)
`none`	If TRUE adds "None to secondSelector and select is. (Default = FALSE)
`sep`	Used to separate labels on multi value selection.
`suffix`	Added to label only on multiple = FALSE (supports reactive). Also uses sep as separator.

Value

Returns the input. As named list: names("type", "selected_columns", "label")

columnSelector module UI representation

Description

columnSelector module UI representation

Usage

columnSelectorUI(id, label = FALSE, title = NULL)
columnSelectorUI(id, label = FALSE, title = NULL)

Arguments

`id`	The ID of the modules namespace.
`label`	Boolean value; if true include a text input field with the desired axis label (this should be preset with the headline of the column)
`title`	String which is displayed as module title. (Default = NULL)

Value

A list from tag with the UI elements.

Method for geneView creation

Description

Method for geneView creation

Usage

create_geneview(
  data,
  grouping,
  plot.type = "line",
  facet.target = "gene",
  facet.cols = 2,
  colors = NULL,
  ylabel = NULL,
  ylimits = NULL,
  gene.label = NULL,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)
create_geneview(
  data,
  grouping,
  plot.type = "line",
  facet.target = "gene",
  facet.cols = 2,
  colors = NULL,
  ylabel = NULL,
  ylimits = NULL,
  gene.label = NULL,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)

Arguments

`data`	data.table containing plot data
`grouping`	data.table metadata containing: column1 : key column2 : factor1
`plot.type`	String specifying which plot type is used c("box", "line", "violin", "bar").
`facet.target`	Target to plot on x-Axis c("gene", "condition").
`facet.cols`	Number of plots per row.
`colors`	Vector of colors used for color palette
`ylabel`	Label of the y-axis (default = NULL).
`ylimits`	Vector defining scale of y-axis (default = NULL).
`gene.label`	Vector of labels used instead of gene names (default = NULL).
`plot.method`	Choose which method used for plotting. Either "static" or "interactive" (Default = "static").
`width`	Set the width of the plot in cm (default = "auto").
`height`	Set the height of the plot in cm (default = "auto").
`ppi`	Pixel per inch (default = 72).
`scale`	Modify plot size while preserving aspect ratio (Default = 1).

Details

Width/ height limit = 500. If exceeded default to 500 and issue exceed_size = TRUE.

Value

Returns depending on plot.method list(plot = ggplot/ plotly object, width = width in cm, height = height in cm, ppi = pixel per inch, exceed_size = Boolean).

Method for heatmap creation

Description

Method for heatmap creation

Usage

create_heatmap(
  data,
  unitlabel = "auto",
  row.label = TRUE,
  row.custom.label = NULL,
  column.label = TRUE,
  column.custom.label = NULL,
  clustering = "none",
  clustdist = "auto",
  clustmethod = "auto",
  colors = NULL,
  winsorize.colors = NULL,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)
create_heatmap(
  data,
  unitlabel = "auto",
  row.label = TRUE,
  row.custom.label = NULL,
  column.label = TRUE,
  column.custom.label = NULL,
  clustering = "none",
  clustdist = "auto",
  clustmethod = "auto",
  colors = NULL,
  winsorize.colors = NULL,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)

Arguments

`data`	data.table containing plot data. First column contains row labels.
`unitlabel`	label of the colorbar
`row.label`	Logical whether or not to show row labels.
`row.custom.label`	Vector of custom row labels.
`column.label`	Logical whether or not to show column labels.
`column.custom.label`	Vector of custom column labels.
`clustering`	How to apply clustering on data. c("none", "both", "column", "row")
`clustdist`	Which cluster distance to use. See `heatmapr`.
`clustmethod`	Which cluster method to use. See `heatmapr`.
`colors`	Vector of colors used for color palette.
`winsorize.colors`	NULL or a vector of length two, giving the values of colorbar ends (default = NULL).
`plot.method`	Choose which method is used for plotting. Either "plotly" or "complexHeatmap" (Default = "complexHeatmap").
`width`	Set width of plot in cm (Default = "auto").
`height`	Set height of plot in cm (Default = "auto").
`ppi`	Pixel per inch (default = 72).
`scale`	Modify plot size while preserving aspect ratio (Default = 1).

Details

Width/ height limit = 500. If exceeded default to 500 and issue exceed_size = TRUE.

Value

Returns list(plot = complexHeatmap/ plotly object, width = width in cm, height = height in cm, ppi = pixel per inch, exceed_size = Boolean whether width/ height exceeded max) depending on plot.method.

Method for pca creation.

Description

Method for pca creation.

Usage

create_pca(
  data,
  color.group = NULL,
  color.title = NULL,
  palette = NULL,
  shape.group = NULL,
  shape.title = NULL,
  shapes = c(15:25),
  dimension.a = 1,
  dimension.b = 2,
  dimensions = 6,
  on.columns = TRUE,
  labels = FALSE,
  custom.labels = NULL,
  pointsize = 2,
  labelsize = 3,
  width = 28,
  height = 28,
  ppi = 72,
  scale = 1
)
create_pca(
  data,
  color.group = NULL,
  color.title = NULL,
  palette = NULL,
  shape.group = NULL,
  shape.title = NULL,
  shapes = c(15:25),
  dimension.a = 1,
  dimension.b = 2,
  dimensions = 6,
  on.columns = TRUE,
  labels = FALSE,
  custom.labels = NULL,
  pointsize = 2,
  labelsize = 3,
  width = 28,
  height = 28,
  ppi = 72,
  scale = 1
)

Arguments

`data`	data.table from which the plot is created (First column will be handled as rownames if not numeric).
`color.group`	Vector of groups according to samples (= column names).
`color.title`	Title of the color legend.
`palette`	Vector of colors used for color palette.
`shape.group`	Vector of groups according to samples (= column names).
`shape.title`	Title of the shape legend.
`shapes`	Vector of shapes see `points`. Will recycle/ cut off shapes if needed. Default = c(15:25)
`dimension.a`	Number of dimension displayed on X-Axis.
`dimension.b`	Number of dimension displayed on Y-Axis.
`dimensions`	Number of dimensions to create.
`on.columns`	Boolean perform pca on columns or rows.
`labels`	Boolean show labels.
`custom.labels`	Vector of custom labels. Will replace columnnames.
`pointsize`	Size of the data points.
`labelsize`	Size of texts inside plot (default = 3).
`width`	Set the width of the plot in cm (default = 28).
`height`	Set the height of the plot in cm (default = 28).
`ppi`	Pixel per inch (default = 72).
`scale`	Modify plot size while preserving aspect ratio (Default = 1).

Details

If width and height are the same axis ratio will be set to one (quadratic plot).

Width/ height limit = 500. If exceeded default to 500 and issue exceed_size = TRUE.

Value

A named list(plot = ggplot object, data = pca.data, width = width of plot (cm), height = height of plot (cm), ppi = pixel per inch, exceed_size = Boolean whether width/ height exceeded max).

Method for scatter plot creation

Description

Method for scatter plot creation

Usage

create_scatterplot(
  data,
  data.labels = NULL,
  data.hovertext = NULL,
  transparency = 1,
  pointsize = 1,
  labelsize = 3,
  color = NULL,
  x_label = "",
  y_label = "",
  z_label = "",
  density = TRUE,
  line = TRUE,
  categorized = FALSE,
  highlight.data = NULL,
  highlight.labels = NULL,
  highlight.hovertext = NULL,
  highlight.color = "#FF0000",
  xlim = NULL,
  ylim = NULL,
  colorbar.limits = NULL,
  width = "auto",
  height = "auto",
  ppi = 72,
  plot.method = "static",
  scale = 1
)
create_scatterplot(
  data,
  data.labels = NULL,
  data.hovertext = NULL,
  transparency = 1,
  pointsize = 1,
  labelsize = 3,
  color = NULL,
  x_label = "",
  y_label = "",
  z_label = "",
  density = TRUE,
  line = TRUE,
  categorized = FALSE,
  highlight.data = NULL,
  highlight.labels = NULL,
  highlight.hovertext = NULL,
  highlight.color = "#FF0000",
  xlim = NULL,
  ylim = NULL,
  colorbar.limits = NULL,
  width = "auto",
  height = "auto",
  ppi = 72,
  plot.method = "static",
  scale = 1
)

Arguments

`data`	data.table containing plot data column 1: id column 2, 3(, 4): x, y(, z)
`data.labels`	Vector of labels used for data. Length has to be equal to nrow(data).
`data.hovertext`	Character vector with additional hovertext. Length has to be equal to nrow(data).
`transparency`	Set point transparency. See `geom_point`.
`pointsize`	Set point size. See `geom_point`.
`labelsize`	Set label size. See `geom_text`.
`color`	Vector of colors used for color palette.
`x_label`	Label x-Axis
`y_label`	Label Y-Axis
`z_label`	Label Z-Axis
`density`	Boolean value, perform 2d density estimate.
`line`	Boolean value, add reference line.
`categorized`	Z-Axis (if exists) as categories.
`highlight.data`	data.table containing data to highlight. Same structure as data.
`highlight.labels`	Vector of labels used for highlighted data. Length has to be equal to nrow(highlight.data).
`highlight.hovertext`	Character vector with additional hovertext. Length has to be equal to nrow(highlight.data).
`highlight.color`	String with hexadecimal color-code.
`xlim`	Numeric vector of two setting min and max limit of x-axis. See `lims`.
`ylim`	Numeric vector of two setting min and max limit of y-axis. See `lims`.
`colorbar.limits`	Vector with min, max values for colorbar (Default = NULL).
`width`	Set plot width in cm (Default = "auto").
`height`	Set plot height in cm (Default = "auto").
`ppi`	Pixel per inch (default = 72).
`plot.method`	Whether the plot should be 'interactive' or 'static' (Default = 'static').
`scale`	Modify plot size while preserving aspect ratio (Default = 1).

Details

Width/ height limit = 500. If exceeded default to 500 and issue exceed_size = TRUE.

Value

Returns list(plot = ggplotly/ ggplot, width, height, ppi, exceed_size).

Function to generate diverging (two-sided) color palettes (e.g. for log2fc, zscore)

Description

Function to generate diverging (two-sided) color palettes (e.g. for log2fc, zscore)

Usage

divergingPalettes(n)
divergingPalettes(n)

Arguments

`n`	Number of colors to generate

Value

A data.table with (named) color palettes of length n

Function used for downloading. Creates a zip container containing plot in png, pdf and user input in json format. Use inside `downloadHandler` content function.

Description

Function used for downloading. Creates a zip container containing plot in png, pdf and user input in json format. Use inside downloadHandler content function.

Usage

download(
  file,
  filename,
  plot,
  width,
  height,
  ppi = 72,
  save_plot = TRUE,
  ui = NULL
)
download(
  file,
  filename,
  plot,
  width,
  height,
  ppi = 72,
  save_plot = TRUE,
  ui = NULL
)

Arguments

`file`	See `downloadHandler` content parameter.
`filename`	See `downloadHandler`.
`plot`	Plot to download.
`width`	in centimeter.
`height`	in centimeter.
`ppi`	pixel per inch. Defaults to 72.
`save_plot`	Logical if plot object should be saved as .RData.
`ui`	List of user inputs. Will be converted to JavaScript Object Notation. See `toJSON`

Value

Path to zip archive invisibly. See zipr.

Method to get equalized min/max values from vector

Description

Method to get equalized min/max values from vector

Usage

equalize(values)
equalize(values)

Arguments

values

Numeric vector or table

Value

Vector with c(min, max).

featureSelector module server logic

Description

featureSelector module server logic

Usage

featureSelector(
  input,
  output,
  session,
  clarion,
  multiple = TRUE,
  contains = FALSE,
  ranged = TRUE,
  step = 100,
  truncate = 30,
  selection.default = "all"
)
featureSelector(
  input,
  output,
  session,
  clarion,
  multiple = TRUE,
  contains = FALSE,
  ranged = TRUE,
  step = 100,
  truncate = 30,
  selection.default = "all"
)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`clarion`	A clarion object. See `Clarion`. (Supports reactive)
`multiple`	Whether or not textual ORs should allow multiple selections. (Fills vector sequentially if needed)(Supports reactive)
`contains`	Whether or not textual ORs are initialized as textInput checking entries for given string. (Fills vector sequentially if needed)(Supports reactive)
`ranged`	Whether or not numeric ORs are ranged. (Fills vector sequentially if needed)(Supports reactive)
`step`	Set numeric ORs number of slider steps. (Fills vector sequentially if needed)(Supports reactive)
`truncate`	Truncate datatable entries at x characters (Default = 30).
`selection.default`	Decide whether everything or nothing is selected on default (no filters applied). Either "all" or "none" (Default = "all").

Details

Keep in mind that the order of features (columns in clarion$data) is the order in which multiple, contains, ranged and step are evaluated.

Value

Reactive containing names list: Selected data as reactive containing clarion object (object). Used filter to select data (filter).

featureSelector module guide

Description

featureSelector module guide

Usage

featureSelectorGuide(session)
featureSelectorGuide(session)

Arguments

session

The shiny session

Value

A shiny reactive that contains the texts for the guide steps.

featureSelector module UI representation

Description

featureSelector module UI representation

Usage

featureSelectorUI(id)
featureSelectorUI(id)

Arguments

`id`	The ID of the modules namespace

Value

A list with HTML tags from tag

Force evaluation of the parent function's arguments.

Description

Force evaluation of the parent function's arguments.

Usage

forceArgs(args)
forceArgs(args)

Arguments

args

List of Argument names to force evaluation. Defaults to all named arguments see match.call.

Details

Similar to forceAndCall but used from within the respective function.

This method is not using force as it is restricted to it's calling environment. Instead get is used.

geneView's module server logic

Description

Provides server logic for the geneView module.

Usage

geneView(
  input,
  output,
  session,
  clarion,
  plot.method = "static",
  label.sep = ", ",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)
geneView(
  input,
  output,
  session,
  clarion,
  plot.method = "static",
  label.sep = ", ",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`clarion`	A clarion object. See `Clarion`. (Supports reactive)
`plot.method`	Choose which method is used for plotting. Either "static" or "interactive" (Default = "static").
`label.sep`	Separator used for label merging (Default = ", ").
`width`	Width of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`height`	Height of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`ppi`	Pixel per inch. Defaults to 72 and supports reactive.
`scale`	Scale plot size. Defaults to 1, supports reactive.

Details

Width/ height/ ppi less or equal to default will use default value.

Ppi less or equal to zero will use default.

Value

Reactive containing data.table used for plotting.

geneView module guide

Description

geneView module guide

Usage

geneViewGuide(session)
geneViewGuide(session)

Arguments

session

The shiny session

Value

A shiny reactive that contains the texts for the Guide steps.

geneView's module UI representation

Description

geneView's module UI representation

Usage

geneViewUI(id, plot.columns = 3)
geneViewUI(id, plot.columns = 3)

Arguments

`id`	The ID of the modules namespace.
`plot.columns`	Initial value of plot column slider. Integer value between 1 and 7 (Default = 3).

Value

A list with HTML tags from tag.

global correlation heatmap module server logic

Description

global correlation heatmap module server logic

Usage

global_cor_heatmap(
  input,
  output,
  session,
  clarion,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)
global_cor_heatmap(
  input,
  output,
  session,
  clarion,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)

Arguments

`input`	Shiny's input object
`output`	Shiny's output object
`session`	Shiny's session object
`clarion`	A clarion object. See `Clarion`. (Supports reactive)
`plot.method`	Choose which method is used for plotting. Either "static" or "interactive" (Default = "static").
`width`	Width of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`height`	Height of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`ppi`	Pixel per inch. Defaults to 72 and supports reactive.
`scale`	Scale plot size. Defaults to 1, supports reactive.

Value

Reactive containing data used for plotting.

global correlation heatmap module guide

Description

global correlation heatmap module guide

Usage

global_cor_heatmap_guide(session)
global_cor_heatmap_guide(session)

Arguments

session

The shiny session

Value

A shiny reactive that contains the texts for the Guide steps.

global correlation heatmap module UI representation

Description

global correlation heatmap module UI representation

Usage

global_cor_heatmapUI(id)
global_cor_heatmapUI(id)

Arguments

`id`	The ID of the modules namespace.

Value

A list with HTML tags from tag

heatmap module server logic

Description

heatmap module server logic

Usage

heatmap(
  input,
  output,
  session,
  clarion,
  plot.method = "static",
  label.sep = ", ",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)
heatmap(
  input,
  output,
  session,
  clarion,
  plot.method = "static",
  label.sep = ", ",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)

Arguments

`input`	Shiny's input object
`output`	Shiny's output object
`session`	Shiny's session object
`clarion`	A clarion object. See `Clarion`. (Supports reactive)
`plot.method`	Choose which method is used for plotting. Either "static" or "interactive" (Default = "static").
`label.sep`	Separator used for label merging (Default = ", ").
`width`	Width of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`height`	Height of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`ppi`	Pixel per inch. Defaults to 72 and supports reactive.
`scale`	Scale plot size. Defaults to 1, supports reactive.

Value

Reactive containing data used for plotting.

heatmap module guide

Description

heatmap module guide

Usage

heatmapGuide(session)
heatmapGuide(session)

Arguments

session

The shiny session

Value

A shiny reactive that contains the texts for the Guide steps.

heatmap module UI representation

Description

heatmap module UI representation

Usage

heatmapUI(id, row.label = TRUE)
heatmapUI(id, row.label = TRUE)

Arguments

`id`	The ID of the modules namespace.
`row.label`	Boolean Value set initial Value for rowlabel checkbox (Default = TRUE).

Value

A list with HTML tags from tag.

Download and install Wilson Apps

Description

Download and install Wilson Apps

Usage

install_app(
  location = ".",
  remove_data = FALSE,
  start_after_install = FALSE,
  app_name = "wilson-basic",
  repository = "https://github.molgen.mpg.de/loosolab/wilson-apps"
)
install_app(
  location = ".",
  remove_data = FALSE,
  start_after_install = FALSE,
  app_name = "wilson-basic",
  repository = "https://github.molgen.mpg.de/loosolab/wilson-apps"
)

Arguments

`location`	Where the app should be installed. Default is current location.
`remove_data`	If TRUE demo data will be deleted.
`start_after_install`	Start the app when done installing.
`app_name`	Select app to install.
`repository`	Link to the repository that holds the apps.

Details

Will create a folder named after parameter app_name.

label module server logic

Description

label module server logic

Usage

label(
  input,
  output,
  session,
  data,
  label = "Select label columns",
  multiple = TRUE,
  sep = ", ",
  unique = TRUE,
  unique_sep = "_",
  disable = NULL
)
label(
  input,
  output,
  session,
  data,
  label = "Select label columns",
  multiple = TRUE,
  sep = ", ",
  unique = TRUE,
  unique_sep = "_",
  disable = NULL
)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`data`	Data.table used for label creation. Column names will be used for selection. (supports reactive)
`label`	Set label of selectizeInput.
`multiple`	Allow multiple selection which will be merged with sep (default = TRUE).
`sep`	Separator used to collapse selection (default = ", ").
`unique`	Make labels unique. Defaults to TRUE. See `make.unique`.
`unique_sep`	Separator used for unique (default = "_"). Should differ from sep.
`disable`	Reactive containing boolean. To disable/ enable module.

Value

Reactive containing list(label = vector of strings or NULL on empty selection, selected = user input).

label module UI representation

Description

label module UI representation

Usage

labelUI(id)
labelUI(id)

Arguments

`id`	The ID of the modules namespace

Value

A list with HTML tags from tag

limit module server logic

Description

limit module server logic

Usage

limit(input, output, session, lower = NULL, upper = NULL)
limit(input, output, session, lower = NULL, upper = NULL)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`lower`	Set lower limit (supports reactive).
`upper`	Set upper limit (supports reactive).

Value

Reactive containing: list(lower, upper).

limit module UI representation

Description

limit module UI representation

Usage

limitUI(id, label = "Limit")
limitUI(id, label = "Limit")

Arguments

`id`	The ID of the modules namespace
`label`	Set the modules label.

Value

A list with HTML tags from tag

logger message convenience function

Description

logger message convenience function

Usage

log_message(
  message,
  level = c("DEBUG", "INFO", "WARN", "ERROR", "FATAL"),
  token = NULL
)
log_message(
  message,
  level = c("DEBUG", "INFO", "WARN", "ERROR", "FATAL"),
  token = NULL
)

Arguments

`message`	String of message to be written in log. See `levellog`.
`level`	Set priority level of the message (number or character). See `levellog`.
`token`	Use token bound to this identifier.

Details

Does nothing if logger doesn't exist.

marker module server logic

Description

marker module server logic

Usage

marker(input, output, session, clarion)
marker(input, output, session, clarion)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`clarion`	A clarion object. See `Clarion`. (Supports reactive)

Value

A named list containing reactives (highlight, color, labelColumn, label, clarion).

marker module UI representation

Description

marker module UI representation

Usage

markerUI(id, label = "Highlight/ Label Selected Features")
markerUI(id, label = "Highlight/ Label Selected Features")

Arguments

`id`	The ID of the modules namespace
`label`	Set label of first element.

Value

A list with HTML tags from tag

orNumeric module server logic

Description

Provides server logic for the orNumeric module.

Usage

orNumeric(
  input,
  output,
  session,
  choices,
  value,
  label = "Column",
  step = 100,
  stepsize = NULL,
  min. = shiny::reactive(min(choices_r(), na.rm = TRUE)),
  max. = shiny::reactive(max(choices_r(), na.rm = TRUE)),
  label.slider = NULL,
  zoomable = TRUE,
  reset = NULL
)
orNumeric(
  input,
  output,
  session,
  choices,
  value,
  label = "Column",
  step = 100,
  stepsize = NULL,
  min. = shiny::reactive(min(choices_r(), na.rm = TRUE)),
  max. = shiny::reactive(max(choices_r(), na.rm = TRUE)),
  label.slider = NULL,
  zoomable = TRUE,
  reset = NULL
)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`choices`	A list or a numeric vector with the possible choices offered in the UI. See `sliderInput` (Supports reactive).
`value`	Initial value of the slider. Creates a ranged slider if numeric vector of two given (Supports reactive).
`label`	Label of the entire module.
`step`	Number of steps on interval (Default = 100).
`stepsize`	Value defining interval size of the slider. Will be used instead of step (Default = NULL).
`min.`	Minimum value that can be selected on slider (defaults to min(choices)) (Supports reactive).
`max.`	Maximum value that can be selected on slider (defaults to max(choices)) (Supports reactive).
`label.slider`	A character vector of length one with the label for the `sliderInput`.
`zoomable`	Boolean to enable zooming. Redefine the sliders range. Defaults to TRUE.
`reset`	A reactive which will trigger a module reset on change.

Value

Returns a reactive containing a named list with the label, the selected choices as a character vector (text), a boolean vector of length length(choices) (bool), and a vector of the selected value(s) (value), indicating whether a item has been chosen. If no item has been chosen, the return is TRUE for items.

orNumeric module UI representation

Description

This module allows to select value/range inputs from a sliderInput element. The functions creates HTML tag definitions of its representation based on the parameters supplied.

Usage

orNumericUI(id)
orNumericUI(id)

Arguments

`id`	The ID of the modules namespace.

Value

A list with HTML tags from tag.

orTextual module server logic

Description

Provides server logic for the orTextual module.

Usage

orTextual(
  input,
  output,
  session,
  choices,
  selected = NULL,
  label = "Column",
  delimiter = NULL,
  multiple = TRUE,
  contains = FALSE,
  reset = NULL,
  parse_mode = TRUE
)
orTextual(
  input,
  output,
  session,
  choices,
  selected = NULL,
  label = "Column",
  delimiter = NULL,
  multiple = TRUE,
  contains = FALSE,
  reset = NULL,
  parse_mode = TRUE
)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`choices`	A list or a character vector with the possible choices offered in the UI. See `selectInput`.
`selected`	The initially selected value. See `selectInput`.
`label`	A character vector of length one with the label for the `selectInput`.
`delimiter`	A single character indicating if and how items are delimited (default: `NULL` indicates no delimitation). Only if contains = FALSE.
`multiple`	Whether or not selection of multiple items is allowed.
`contains`	Logical variable. If TRUE shows module as a textsearch input.
`reset`	A reactive which will trigger a module reset on change.
`parse_mode`	Boolean to enable text to selection parsing. Ignored if multiple = FALSE or contains = TRUE.

Value

Returns a reactive containing a named list with the label, the selected choices as a character vector (text) and a boolean vector of length length(choices) (bool), indicating whether a item has been chosen. If no item has been chosen, the return is TRUE for items.

orTextual module UI representation

Description

This module allows to select (multiple) inputs from a selectInput element. The functions creates HTML tag definitions of its representation based on the parameters supplied.

Usage

orTextualUI(id)
orTextualUI(id)

Arguments

`id`	The ID of the modules namespace.

Value

A list with HTML tags from tag.

Converting MaxQuant Output file proteinGroups.txt to CLARION format by creating a headline of metadata for each column

Description

List with columns of reduced version (see config.json file) If you only want the samples of a specific keyword write: column;exp For example: You got: Intensity Intensity 'experiment_name' Do you want both add "Intensity" to the list. Do you only want the sample add "Intensity;exp" to the list Anything else like 'Intensity;ex' or 'Intensity;' results in writing both. Only works if there are samples of that type. If not, column does not show up in file

Usage

parse_MaxQuant(
  proteinGroups_in,
  summary_in,
  outfile,
  outfile_reduced,
  config = system.file("extdata", "parser_MaxQuant_config.json", package = "wilson"),
  delimiter = ";",
  format = NULL,
  version = NULL,
  experiment_id = NULL
)
parse_MaxQuant(
  proteinGroups_in,
  summary_in,
  outfile,
  outfile_reduced,
  config = system.file("extdata", "parser_MaxQuant_config.json", package = "wilson"),
  delimiter = ";",
  format = NULL,
  version = NULL,
  experiment_id = NULL
)

Arguments

`proteinGroups_in`	path of proteinGroup.txt file
`summary_in`	path of belonging summary.txt file
`outfile`	path of full CLARION output file
`outfile_reduced`	path of reduced CLARION output file
`config`	path of config file (containing information about metadata)
`delimiter`	delimiter (Default = ;)
`format`	pre-header information about format (optional)
`version`	pre-header information about version (optional)
`experiment_id`	pre-header information about experiment id (optional)

Value

TRUE on success

Author(s)

Rene Wiegandt

Method to parse input file.

Description

Method to parse input file.

Usage

parser(file, dec = ".")
parser(file, dec = ".")

Arguments

`file`	Path to file that needs parsing.
`dec`	The decimal separator. See `fread`.

Value

Clarion object. See Clarion

pca module server logic

Description

pca module server logic

Usage

pca(
  input,
  output,
  session,
  clarion,
  width = 28,
  height = 28,
  ppi = 72,
  scale = 1
)
pca(
  input,
  output,
  session,
  clarion,
  width = 28,
  height = 28,
  ppi = 72,
  scale = 1
)

Arguments

`input`	Shiny's input object
`output`	Shiny's output object
`session`	Shiny's session object
`clarion`	A clarion object. See `Clarion`. (Supports reactive)
`width`	Width of the plot in cm. Defaults to 28 and supports reactive.
`height`	Height of the plot in cm. Defaults to 28 and supports reactive.
`ppi`	Pixel per inch. Defaults to 72 and supports reactive.
`scale`	Scale plot size. Defaults to 1, supports reactive.

Details

Width/ height/ ppi less or equal to zero will use default value.

Value

A reactive containing list with dimensions.

pca module guide

Description

pca module guide

Usage

pcaGuide(session)
pcaGuide(session)

Arguments

session

The shiny session

Value

A shiny reactive that contains the texts for the Guide steps.

pca module UI representation

Description

pca module UI representation

Usage

pcaUI(id, show.label = TRUE)
pcaUI(id, show.label = TRUE)

Arguments

`id`	The ID of the modules namespace.
`show.label`	Set initial value of show label checkbox (Default = TRUE).

Value

A list with HTML tags from tag.

Defines additional questions asked before CRAN submission. DO NOT EXPORT!

Description

Defines additional questions asked before CRAN submission. DO NOT EXPORT!

Usage

release_questions()
release_questions()

scatterPlot module server logic

Description

scatterPlot module server logic

Usage

scatterPlot(
  input,
  output,
  session,
  clarion,
  marker.output = NULL,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)
scatterPlot(
  input,
  output,
  session,
  clarion,
  marker.output = NULL,
  plot.method = "static",
  width = "auto",
  height = "auto",
  ppi = 72,
  scale = 1
)

Arguments

`input`	Shiny's input object
`output`	Shiny's output object
`session`	Shiny's session object
`clarion`	A clarion object. See `Clarion`. (Supports reactive)
`marker.output`	Marker module output. See `marker`.
`plot.method`	Choose to rather render a 'interactive' or 'static' plot. Defaults to 'static'.
`width`	Width of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`height`	Height of the plot in cm. Defaults to minimal size for readable labels and supports reactive.
`ppi`	Pixel per inch. Defaults to 72 and supports reactive.
`scale`	Scale plot size. Defaults to 1, supports reactive.

Details

As markerOutput provides a second dataset used for highlighting it is crucial for it to have the same columnnames as the dataset provided by clarion.

Intersections between marker and clarion will be removed from clarion in favor of highlighting them.

Value

Returns reactive containing data used for plot.

scatterPlot module guide

Description

scatterPlot module guide

Usage

scatterPlotGuide(session, marker = FALSE)
scatterPlotGuide(session, marker = FALSE)

Arguments

`session`	The shiny session
`marker`	Logical if marker step should be enabled (Default = FALSE).

Value

A shiny reactive that contains the texts for the Guide steps.

scatterPlot module UI representation

Description

scatterPlot module UI representation

Usage

scatterPlotUI(id)
scatterPlotUI(id)

Arguments

`id`	The ID of the modules namespace.

Value

A list with HTML tags from tag.

Function to search data for selection

Description

Function to search data for selection

Usage

searchData(
  input,
  choices,
  options = c("=", "<", ">"),
  min. = min(choices, na.rm = TRUE),
  max. = max(choices, na.rm = TRUE)
)
searchData(
  input,
  choices,
  options = c("=", "<", ">"),
  min. = min(choices, na.rm = TRUE),
  max. = max(choices, na.rm = TRUE)
)

Arguments

`input`	Vector length one (single) or two (ranged) containing numeric values for selection.
`choices`	Vector on which input values are applied.
`options`	Vector on how the input and choices should be compared. It can contain: single = c("=", "<", ">") or ranged = c("inner", "outer").
`min.`	Minimum value that can be selected on slider (defaults to min(choices)).
`max.`	Maximum value that can be selected on slider (defaults to max(choices)).

Value

Returns a logical vector with the length of choices, where every matched position is TRUE.

Function to generate sequential (one-sided) color palettes (e.g. for expression, enrichment)

Description

Function to generate sequential (one-sided) color palettes (e.g. for expression, enrichment)

Usage

sequentialPalettes(n)
sequentialPalettes(n)

Arguments

`n`	Number of colors to generate

Value

A data.table with (named) color palettes of length n

set a log4r logger used within the package

Description

set a log4r logger used within the package

Usage

set_logger(logger, token = NULL)
set_logger(logger, token = NULL)

Arguments

`logger`	A logger object see `create.logger`. NULL to disable logging.
`token`	Set a unique identifier for this logger.

Details

This function will save each logger in the wilson.globals environment. Each logger is stored by the name 'logger'[token] (e.g. 'logger6b821824b0b53b1a3e8f531a34d0d6e6').

Use onSessionEnded to clean up after logging. See onFlush.

TOBIAS TFBS table to clarion parser

Description

Click here for more information about TOBIAS.

Usage

tobias_parser(
  input,
  output,
  filter_columns = NULL,
  filter_pattern = NULL,
  config = system.file("extdata", "tobias_config.json", package = "wilson"),
  omit_NA = FALSE,
  condition_names = NULL,
  condition_pattern = "_bound$",
  in_field_delimiter = ",",
  dec = ".",
  ...
)
tobias_parser(
  input,
  output,
  filter_columns = NULL,
  filter_pattern = NULL,
  config = system.file("extdata", "tobias_config.json", package = "wilson"),
  omit_NA = FALSE,
  condition_names = NULL,
  condition_pattern = "_bound$",
  in_field_delimiter = ",",
  dec = ".",
  ...
)

Arguments

`input`	Path to input table
`output`	Output path.
`filter_columns`	Either a vector of columnnames or a file containing one columnname per row.
`filter_pattern`	Keep columns matching the given pattern. Uses parameter filter_columns for matching if set. In the case of no matches a warning will be issued and all columns will be used.
`config`	Json file containing metadata information for all columns. Will use first occurence for duplicate column names.
`omit_NA`	Logical whether all rows containing NA should be removed.
`condition_names`	Vector of condition names. Default = NULL. Used to classify columns not provided in config.
`condition_pattern`	Used to identify condition names by matching and removing given pattern with `grep`. Ignored when condition_names is set.
`in_field_delimiter`	Delimiter for multi value fields. Default = ','.
`dec`	Decimal separator. Used in file reading and writing.
`...`	Used as header information.

Details

During conversion the parser will try to use the given config (if provided) to create the Clarion metadata. In the case of insufficient config information it will try to approximate by referencing condition names issuing warnings in the process.

As the format requires an unique id the parser will create one if necessary.

Factor grouping (metadata factor columns) is currently not implemented!

transformation module server logic

Description

The module provides several transformations on a numeric data matrix for the user.

Usage

transformation(
  input,
  output,
  session,
  data,
  transpose = FALSE,
  pseudocount = 1,
  replaceInf = TRUE,
  replaceNA = TRUE
)
transformation(
  input,
  output,
  session,
  data,
  transpose = FALSE,
  pseudocount = 1,
  replaceInf = TRUE,
  replaceNA = TRUE
)

Arguments

`input`	Shiny's input object.
`output`	Shiny's output object.
`session`	Shiny's session object.
`data`	Numeric matrix on which transformation is performed (column-wise). (Supports reactive)
`transpose`	Whether the matrix should be transposed to enable row-wise transformation. (Supports reactive)
`pseudocount`	Numeric Variable to add a pseudocount to log-based transformations. (Supports reactive)
`replaceInf`	Change Infinite to NA, applied after transformation. (Supports reactive)
`replaceNA`	Change NA to 0, applied after transformation. (Supports reactive)

Value

Namedlist of two containing data and name of the used method. data: Reactive containing the transformed matrix. Infinite values are replaced by NA and NA values are replaced by 0. method: Reactive containing String. transpose: Reactive containing String.

transformation module UI representation

Description

This function provides an input to select a transformation method.

Usage

transformationUI(
  id,
  label = "Transformation",
  selected = "raw",
  choices = list(None = "raw", log2 = "log2", `-log2` = "-log2", log10 = "log10",
    `-log10` = "-log10", `Z score` = "zscore", `regularized log` = "rlog"),
  transposeOptions = FALSE
)
transformationUI(
  id,
  label = "Transformation",
  selected = "raw",
  choices = list(None = "raw", log2 = "log2", `-log2` = "-log2", log10 = "log10",
    `-log10` = "-log10", `Z score` = "zscore", `regularized log` = "rlog"),
  transposeOptions = FALSE
)

Arguments

`id`	The ID of the modules namespace.
`label`	A character vector of length one with the label for the `selectInput`.
`selected`	The initially selected value. See `selectInput`.
`choices`	Named list of available transformations. Possible transformations are list('None' = "raw", 'log2' = "log2", '-log2' = "-log2", 'log10' = "log10", '-log10' = "-log10", 'Z score' = "zscore", 'regularized log' = "rlog") which is also the default.
`transposeOptions`	Boolean value if transpose radioButtons are shown (Default = FALSE).

Value

A list with HTML tags from tag.

Package 'wilson'

Help Index

AND module server logic

Description

Usage

Arguments

Value

AND module UI representation

Description

Usage

Arguments

Value

Function to generate categorical (qualitative) color palettes

Description

Usage

Arguments

Value

Clarion R6-class definition

Description

Constructor

Constructor Arguments

Public fields

Methods

Public methods

Method get_id()

Usage

Returns

Method get_name()

Usage

Returns

Method get_delimiter()

Usage

Method is_delimited()

Usage

Arguments

Returns

Method get_factors()

Usage

Details

Returns

Method get_level()

Usage

Arguments

Returns

Method get_label()

Usage

Arguments

Details

Returns

Method validate()

Usage

Arguments

Method new()

Usage

Arguments

Returns

Method write()

Usage

Arguments

Method clone()

Usage

Arguments

Examples

colorPicker module server logic

Description

Usage

Arguments

Details

Value

colorPicker module UI representation

Description

Usage

Arguments

Value

columnSelector module server logic

Description

Usage

Arguments

Value

columnSelector module UI representation

Method `get_id()`

Method `get_name()`

Method `get_delimiter()`

Method `is_delimited()`

Method `get_factors()`

Method `get_level()`

Method `get_label()`

Method `validate()`

Method `new()`

Method `write()`

Method `clone()`

Function used for downloading. Creates a zip container containing plot in png, pdf and user input in json format. Use inside `downloadHandler` content function.