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Racmacs: Antigenic Cartography Macros

Description

The acmap data object

The fundamental unit of the Racmacs package is theacmapobject, short for Antigenic Cartography MAP. This object contains all theinformation about an antigenic map. You can read in a new acmap object from afile with the functionread.acmap and create a new acmap object within anR session using theacmap function.

Key information associated with each acmap object is summarized in thesections below.

The titer table

Eachacmap object is built upon a table of data of measurements thereactivity of a set of different sera against a set of different antigens.Typically these measurements are HI assay measurements, but it is alsopossible to use other similar assays as input data.

For the table of data it is always assumed that sera form thecolumns andantigens form therows, as below. You can get and set titer data withtiterTable.

Optimizations

Another key component of theacmap object is a list of optimizations.While acmap objects only have one table of data, they can have manyoptimizations or none at all.

Each optimization has the following main attributes (see the vignette onoptimizing antigenic maps for more details on minimum column bases andstress):

• Antigen coordinates, the coordinates of each antigen in thisoptimization.

• Sera coordinates, the coordinates of each serum in this optimization.

• Minimum column basis, the minimum column basis assumed when calculatingthis optimization.

• Stress, the stress of this optimization.

• Dimensions, the number of dimensions of this optimization.

Plotting styles

The final type of information that is contained in the acmap object isinformation on point styles when plotting. By altering these attributes youcan change the appearance of the antigen and serum points in any mapsplotted, the main ones include:

• Size

• Shape

• Fill color

• Outline color

Author(s)

Maintainer: Sam Wilkssw463@cam.ac.uk

See Also

Useful links:

• https://acorg.github.io/Racmacs/

• https://github.com/acorg/Racmacs/

• Report bugs athttps://github.com/acorg/Racmacs/issues

Set acmap merge options

Description

This function facilitates setting options for the acmap titer merging process byreturning a list of option settings.

Usage

RacMerge.options(sd_limit = NULL, dilution_stepsize = 1, method = NULL)

Arguments

Details

When merging measured titers, the general approach is to take the geometricmean and use that as the merged titer, however in particular when < valuesare present there are different options that can be employed. In olderversions of Racmacs, < values were converted to maximum possible numerictiter after accounting for the dilution_stepsize factor, then the geometricmean was taken. This approach can be used by specifying the method as"likelihood" since, this approach gives a very rough approximation of themost likely mean numeric value. In contrast, the "conservative" method andcurrent default returns the highest < value that satisfies all the valuesthat were measured. As an example merging <10 and 20, (assumingdilution_stepsize = 1) would return a value of 10 with the "likelihood"method and <40 with the "conservative" method.

Value

Returns a named list of merging options

See Also

Other map merging functions:htmlMergeReport(),mergeMaps(),mergeReport(),splitTiterLayers()

Set acmap optimization options

Description

This function facilitates setting options for the acmap optimizer process byreturning a list of option settings.

Usage

RacOptimizer.options(  dim_annealing = FALSE,  method = "L-BFGS",  maxit = 1000,  num_basis = 10,  armijo_constant = 1e-04,  wolfe = 0.9,  min_gradient_norm = 1e-06,  factr = 1e-15,  max_line_search_trials = 50,  min_step = 1e-20,  max_step = 1e+20,  num_cores = getOption("RacOptimizer.num_cores"),  report_progress = NULL,  ignore_disconnected = FALSE,  progress_bar_length = options()$width)

Arguments

Details

For more details, for example on "dimensional annealing" seevignette("intro-to-antigenic-cartography"). For details on optimizersettings likemaxit see the underlying optimizer documentation atensmallen.org.

Value

Returns a named list of optimizer options

See Also

Other map optimization functions:make.acmap(),moveTrappedPoints(),optimizeMap(),randomizeCoords(),relaxMapOneStep(),relaxMap()

Create a RacViewer widget

Description

This creates an html widget for viewing antigenic maps.

Usage

RacViewer(  map,  show_procrustes = FALSE,  show_group_legend = FALSE,  options = list(),  width = NULL,  height = NULL,  elementId = NULL)

Arguments

Value

An object of class htmlwidget that will intelligently print itselfinto HTML in a variety of contexts including the R console, within RMarkdown documents, and within Shiny output bindings.

See Also

Other functions to view maps:RacViewer.options(),export_viewer(),ggplot.acmap(),mapGadget(),plot.acmap(),setLegend(),view.acmap(),view.default(),view()

Shiny bindings for RacViewer

Description

Output and render functions for using RacViewer within Shinyapplications and interactive Rmd documents.

Usage

RacViewerOutput(outputId, width = "100%", height = "100%")renderRacViewer(expr, env = parent.frame(), quoted = FALSE)

Arguments

Value

An output or render function that enables the use of the widgetwithin Shiny applications.

See Also

Other shiny app functions:runGUI(),view.acmap()

Set viewer options

Description

This function facilitates setting racviewer options by returning a list ofoption settings.

Usage

RacViewer.options(  point.opacity = NA,  viewer.controls = "hidden",  grid.display = "static",  grid.col = "#cfcfcf",  background.col = "#ffffff",  show.names = FALSE,  show.errorlines = FALSE,  show.connectionlines = FALSE,  show.titers = FALSE,  xlim = NULL,  ylim = NULL,  translation = c(0, 0, 0),  rotation = c(0, 0, 0),  zoom = NULL)

Arguments

Value

Returns a named list of viewer options

See Also

Other functions to view maps:RacViewer(),export_viewer(),ggplot.acmap(),mapGadget(),plot.acmap(),setLegend(),view.acmap(),view.default(),view()

Generate a new acmap object

Description

This function generates a new acmap object, the base object for storing mapdata in the Racmacs package.

Usage

acmap(  ag_names = NULL,  sr_names = NULL,  titer_table = NULL,  ag_coords = NULL,  sr_coords = NULL,  check_duplicates = TRUE,  ...)

Arguments

Details

The fundamental unit of the Racmacs package is theacmap object,short for Antigenic Cartography MAP. This object contains all theinformation about an antigenic map. You can read in a new acmap object froma file with the functionread.acmap() and create a new acmap objectwithin an R session using theacmap() function.

Value

Returns the new acmap object

See Also

SeeoptimizeMap() for generating new optimizations estimatingantigen similarity from the acmap titer data.

Other functions for working with map data:addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Get acmap attributes

Description

Functions to get various attributes about an acmap object.

Usage

numAntigens(map)numSera(map)numSeraGroups(map)numPoints(map)numOptimizations(map)numLayers(map)

Arguments

Value

A number relating to the attribute

See Also

Other map attribute functions:adjustedLogTiterTable(),adjustedTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapDescription(),mapName(),titerTableFlat(),titerTableLayers(),titerTable()

Add a new optimization to an acmap object

Description

Function to add a new optimization to an acmap object, with specified values.

Usage

addOptimization(  map,  ag_coords = NULL,  sr_coords = NULL,  number_of_dimensions = NULL,  minimum_column_basis = "none",  fixed_column_bases = NULL,  ag_reactivity_adjustments = NULL)

Arguments

Value

Returns the acmap data object with new optimization added (but notselected).

See Also

Other functions for working with map data:acmap(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Get the reactivity adjusted log titer table

Description

Return the log titer table plus any antigen reactivity adjustments.

Usage

adjustedLogTiterTable(map, optimization_number = 1)

Arguments

Value

A numeric matrix of adjusted log titers.

See Also

Other map attribute functions:acmapAttributes,adjustedTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapDescription(),mapName(),titerTableFlat(),titerTableLayers(),titerTable()

Get the reactivity adjusted titer table

Description

Return the titer table plus any antigen reactivity adjustments.

Usage

adjustedTiterTable(map, optimization_number = 1)

Arguments

Value

A character matrix of titers.

See Also

htmlAdjustedTiterTable()

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapDescription(),mapName(),titerTableFlat(),titerTableLayers(),titerTable()

Getting and setting antigen attributes

Description

These functions get and set the antigen attributes for a map.

Usage

agIDs(map)agIDs(map) <- valueagDates(map)agDates(map) <- valueagReference(map)agReference(map) <- valueagNames(map)agNames(map) <- valueagExtra(map)agExtra(map) <- valueagPassage(map)agPassage(map) <- valueagLineage(map)agLineage(map) <- valueagReassortant(map)agReassortant(map) <- valueagStrings(map)agStrings(map) <- valueagContinent(map)agContinent(map) <- value

Arguments

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

srAttributes()

Other antigen and sera attribute functions:agGroups(),agHomologousSr(),agLabIDs(),agSequences(),ptAnnotations,ptClades,srAttributes,srGroups(),srHomologousAgs(),srSequences()

Check map cohesion

Description

Checks the vertex connectivity of points in a map (the minimum number ofpoints needed to remove from the map to eliminate all paths from one point toanother point). This is for checking for example if after merging maps youonly have a small number of points in common between separate groups ofpoints, leading to a situation where groups of points cannot be robustlypositioned relative to each other. If the vertex connectivity is smaller thanthe number of map dimensions + 1 then this will certainly be occurring andwill lead to an unstable map solution.mapCohesion() returns the minimumvertex connectivity found between any given points, whileagCohesion() andsrCohesion() return the vertex connectivity between each pair of antigensand sera as a table helping to diagnose which antigens and sera are formingseparate groups. Note that for these purposes only detectable titers countas connections and non-detectable titers are ignored.

Usage

agCohesion(map)srCohesion(map)mapCohesion(map)

Arguments

Value

A scalar real value.

See Also

Other map diagnostic functions:bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Getting and setting antigen groups

Description

These functions get and set the antigen groupings for a map.

Usage

agGroups(map)agGroups(map) <- value

Arguments

Value

A factor vector of groupings.

See Also

Other antigen and sera attribute functions:agAttributes,agHomologousSr(),agLabIDs(),agSequences(),ptAnnotations,ptClades,srAttributes,srGroups(),srHomologousAgs(),srSequences()

Get homologous sera for each antigen

Description

Gets the indices of homologous sera for each antigen in an antigenic map.See also the functionsrHomologousAgs() for getting and setting thehomologous antigens reciprocally.

Usage

agHomologousSr(map)

Arguments

Value

A list, where each entry is a vector of indices for homologoussera, or a length 0 vector where no homologous serum is present

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agLabIDs(),agSequences(),ptAnnotations,ptClades,srAttributes,srGroups(),srHomologousAgs(),srSequences()

Getting and setting antigen lab id information

Description

Getting and setting antigen lab id information

Usage

agLabIDs(map)agLabIDs(map) <- value

Arguments

Value

A character vector of antigen laboratory IDs

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agSequences(),ptAnnotations,ptClades,srAttributes,srGroups(),srHomologousAgs(),srSequences()

Get and set antigen reactivity adjustments

Description

Get and set antigen reactivity adjustments

Usage

agReactivityAdjustments(map)agReactivityAdjustments(map) <- value

Arguments

Value

A numeric vector of antigen reactivity adjustments

See Also

Other functions for working with map data:acmap(),addOptimization(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Getting and setting antigen sequence information

Description

Getting and setting antigen sequence information

Usage

agSequences(map, missing_value = ".")agSequences(map) <- valueagNucleotideSequences(map, missing_value = ".")agNucleotideSequences(map) <- value

Arguments

Value

A character matrix of sequences, where each row represents an antigen.

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agLabIDs(),ptAnnotations,ptClades,srAttributes,srGroups(),srHomologousAgs(),srSequences()

Apply the current map transformation

Description

Applies the map transformation associated with a selected optimization run toa set of coordinates.

Usage

applyMapTransform(coords, map, optimization_number = 1)

Arguments

Value

An acmap object with transformation applied

See Also

Other functions relating to map transformation:reflectMap(),rotateMap(),translateMap()

Apply a plotspec from another acmap

Description

Copy point style from matching antigens and sera in another acmap

Usage

applyPlotspec(map, source_map)

Arguments

Value

Returns the acmap object with updated point styles (unmatched pointstyles unchanged)

See Also

Other map point style functions:ptDrawingOrder(),ptOpacity,ptStyles

Convert map to json format

Description

Convert map to json format

Usage

as.json(map, pretty = FALSE, round_titers = FALSE)

Arguments

Value

Returns map data as .ace json format

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Plot a blob object

Description

Plot a blob object such as that return fromagBootstrapBlob() using thepolygon() function.

Usage

blob(x, col, border, lwd, alpha = 1, ...)

Arguments

Value

No return value, called for the side effect of plotting the blobs.

See Also

Other additional plotting functions:blobsize()

Calculate size of a blob object

Description

Returns either the area (for 2D blobs) or volume (for 3D blobs)

Usage

blobsize(blob)

Arguments

Value

A numeric vector

See Also

Other additional plotting functions:blob()

Calculate bootstrap blob data for an antigenic map

Description

This function takes a map for which the functionbootstrapMap() has alreadybeen applied and draws contour blobs for each point illustrating how pointposition varies in each bootstrap repeat. The blobs are calculated usingkernal density estimates according to these point distribution and drawnso as to encompass a given proportion of this variation according to theparameterconf.level. Aconf.level set at 0.95 for example will drawblobs that are calculated to encompass 95% of the positional variation seenin the bootstrap repeats. Note however that the accuracy of these estimateswill depend on the number of bootstrap repeats performed, for example whether100 or 1000 repeats were performed in the initial calculations usingbootstrapMap().

Usage

bootstrapBlobs(  map,  conf.level = 0.68,  smoothing = 6,  gridspacing = 0.25,  antigens = TRUE,  sera = TRUE,  method = "ks")

Arguments

Value

Returns an acmap object that will then show the corresponding bootstrapblobs when viewed or plotted.

See Also

Other map diagnostic functions:agCohesion(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Perform a bootstrap on a map

Description

This function takes the map and original titer table, and performs a versionofbootstrappingdefined by the method argument. For each bootstrap runthis process is performed and a record of the coordinates of points in thelowest stress solution is kept. See details for a description of the bootstrappingmethods you can apply.

Usage

bootstrapMap(  map,  method,  bootstrap_repeats = 1000,  bootstrap_ags = TRUE,  bootstrap_sr = TRUE,  reoptimize = TRUE,  optimizations_per_repeat = 100,  ag_noise_sd = 0.7,  titer_noise_sd = 0.7,  options = list())

Arguments

Details

Bootstrapping methods

"resample":Theresample bootstrapis the most standard bootstrap method, a random resample of the titer table data istakenwith replacement. Depending on your specification, resampling is applied acrosseither individual antigens, individual sera or both antigens and sera.In essence this method tries to let you see how robust the map is to inclusion ofparticular titer measurements or antigens or sera. Like most bootstrapping techniques itwill prove give more reliable results the more antigens and sera you have in your map. Itwon't work very well for a map of 5 sera and antigens for example, in this case a "noisy"bootstrap may be better.

"bayesian":Thebayesian bootstrapis akin to the resampling bootstrap, but rather than explicitly resampling data, weights areassigned to each part of the titer table data according to random draws from a dirichilet distribution.Under this scheme, every data point will play at least some role in making the map, even if onlyweighted slightly. Sometimes this is helpful, if you know for example that the points in your mapare highly dependent upon the presence of a few antigens / sera / titers to achieve reasonabletriangulation of point positions and you don't really want to risk removing them completely andending up with bootstrap runs that are under-constrained, you might want to consider this approach.On the other hand this might be exactly what you don't want and you want to know uncertainty thatcan be generated when certain subsets of the data are excluded completely, in that case you probablywant to stick with the "resample" method.

"noisy":The noisy bootstrap, sometimes termed asmooth bootstrapinvolved adding normally distributed noise to each observation. The distribution of this noise canbe parameterised through theag_noise_sd andtiter_noise_sd arguments.titer_noise_sd refers to thestandard deviation (on the log scale) of noise added to each individual titer measurement in the table,whileantigen_noise_sd refers to the standard deviation of noise applied to titers for each antigen.The reason for this distinction is that we have noticed with repeat measurements of influenza data thereis often both a random noise per titer and a random noise per antigen, i.e. in one repeat titers may allbe around one 2-fold higher on average, in addition to unbiased additional titer noise. If you wish to onlysimulate additional noise per titer and not a per antigen effect, simply setantigen_noise_sd to 0. Notethat in order to use this most effectively it is best to have an idea of the amount and type of measurementnoise you may expect in your data and set these parameters accordingly.

Value

Returns the map object updated with bootstrap information

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Check for hemisphering or trapped points

Description

Check for hemisphering or trapped points

Usage

checkHemisphering(  map,  optimization_number = 1,  grid_spacing = 0.25,  stress_lim = 0.1,  options = list())

Arguments

Value

Returns a data frame with information on any points that were foundto be hemisphering or trapped.

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Getting and setting column bases

Description

Functions to get and set column bases specified for an optimization run,either through the minimum column basis or through a vector of specifiedcolumn bases.

Usage

minColBasis(map, optimization_number = 1)minColBasis(map, optimization_number = 1) <- valuefixedColBases(map, optimization_number = 1)fixedColBases(map, optimization_number = 1) <- value

Arguments

Details

In general a map can have column bases that are specified eitherthrough a minimum column basis or a vector of fixed column bases for eachsera. When you callminColBasis(), it will return the minimum columnbasis if it has been set, or "fixed" if column bases have instead beenfixed directly. ThecolBases() function will return the column bases ascalculated for a given optimization run. Setting column bases through thisfunction with⁠colBases()<-⁠ will fix the column bases to the suppliedvector of values.

Note that although the output fromcolBases() might be the same in a casewhere a minimum column basis was set or a case where column bases were setexplicitly, when a minimum column basis is set, the column bases will stilldepend on the log titers recorded against a given sera, so changing thetiters may therefore change the actual column bases calculated. For fixedcolumn bases case, column bases will remain fixed at their valuesindependently of measured titers.

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

Other map optimization attribute functions:mapComment(),mapDimensions(),mapStress(),mapTransformation(),ptBaseCoords(),ptCoords()

Deprecated functions

Description

These functions still work but have been deprecated in favour of another function. Arguments will be passed onto the new function with a warning.

Usage

stressBlobs(...)

Arguments

Value

Values from the new function

Get or set the dilution stepsize associated with a map

Description

This defaults to 1 but can be changed using this function with knock-oneffects for how < values are treated when maps are optimized or relaxed andthe way stress is calculated, see details.

Usage

dilutionStepsize(map)dilutionStepsize(map) <- value

Arguments

Details

Antigenic cartography was originally developed for HI titers whichtypically follow a 2-fold dilution series starting from 1/10, then 1/20,1/40 etc. This represents a "dilution stepsize" of 1 when converted to thelog2 scale. When no inhibition was recorded at the highest dilution, thevalue is typically recorded as <10 but the optimization regime effectivelytreats this as a <=5, the rationale being that, had the dilution series beencontinued to higher concentrations, the next lowest titer would have been a5. Over time the method has also been applied to other neutralizationassays that sometimes have a continuous read out with a lower end, in thesecases a <10 really means a <10 since any other values like 9.8 or 7.62would also be possible. To indicate these continuous cases, you can specifythe dilution stepsize as 0. Equally, if the dilution regime followed adifferent pattern, you can also set that here.

Value

A number giving the current dilution stepsize setting for a map.

See Also

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),adjustedTiterTable(),logtiterTableLayers(),mapDescription(),mapName(),titerTableFlat(),titerTableLayers(),titerTable()

Perform dimension testing on a map object

Description

Take a map object and perform cross-validation, seeing how well titers arepredicted when they are excluded from the map.

Usage

dimensionTestMap(  map,  dimensions_to_test = 1:5,  test_proportion = 0.1,  minimum_column_basis = "none",  fixed_column_bases = rep(NA, numSera(map)),  number_of_optimizations = 1000,  replicates_per_dimension = 100,  options = list())

Arguments

Details

For each run, the ag-sr titers that were randomly excluded arepredicted according to their relative positions in the map trained withoutthem. An RMSE is then calculated by comparing predicted titers inferredfrom the map on the log scale to the actual log titers. This is doneseparately for detectable titers (e.g. 40) and non-detectable titers (e.g.<10). For non-detectable titers, if the predicted titer is the same orlower than the log-titer threshold, the error is set to 0.

Value

Returns a data frame with the following columns. "dimensions" : thedimension tested, "mean_rmse_detectable" : mean prediction rmse fordetectable titers across all runs. "var_rmse_detectable" the variance ofthe prediction rmse for detectable titers across all runs, useful forestimating confidence intervals. "mean_rmse_nondetectable" and"var_rmse_nondetectable" the equivalent for non-detectable titers

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Edit antigen names in an acmap

Description

Edit antigen names in an acmap

Usage

edit_agNames(map, old_names, new_names)

Arguments

Value

Returns the acmap object with antigen names updated.

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Edit sera names in an acmap

Description

Edit sera names in an acmap

Usage

edit_srNames(map, old_names, new_names)

Arguments

Value

Returns the acmap object with sera names updated.

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Export the map viewer

Description

Export a map in a standalone html viewer

Usage

export_viewer(map, file, selfcontained = TRUE, ...)

Arguments

Value

Called for the side effect of saving the viewer to an html file butinvisibly returns the map viewer htmlwidget.

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),ggplot.acmap(),mapGadget(),plot.acmap(),setLegend(),view.acmap(),view.default(),view()

Get optimization details from an acmap object

Description

Gets the details associated with the currently selected or specified acmapoptimization as a list.

Usage

getOptimization(map, optimization_number = 1)

Arguments

Value

Returns a list with information about the optimization

See Also

SeelistOptimizations() for getting information about alloptimizations.

Plot an antigenic map using ggplot

Description

Method for plotting an antigenic map as a ggplot object

Usage

## S3 method for class 'acmap'ggplot(  data = NULL,  mapping = NULL,  optimization_number = 1,  xlim = NULL,  ylim = NULL,  plot_ags = TRUE,  plot_sr = TRUE,  plot_blobs = TRUE,  plot_hemisphering = TRUE,  show_procrustes = TRUE,  show_error_lines = FALSE,  plot_stress = FALSE,  indicate_outliers = "arrowheads",  grid.col = "grey90",  grid.lwd = 0.5,  grid.margin.col = "grey50",  grid.margin.lwd = grid.lwd,  fill.alpha = 0.8,  outline.alpha = 0.8,  padding = 1,  arrow_angle = 25,  arrow_length = 0.2,  margins = rep(0.5, 4),  ...,  environment = NULL)

Arguments

Value

Returns the ggplot plot

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),export_viewer(),mapGadget(),plot.acmap(),setLegend(),view.acmap(),view.default(),view()

Return an html formatted titer table with antigen reactivity adjustments applied

Description

Prints an html formatted titer table, visualisingwith colors things like which titers are the maximum for each sera.

Usage

htmlAdjustedTiterTable(map, optimization_number = 1)

Arguments

Value

A list() with a Rac_html_merge_report and shiny.tag class that canbe converted into an HTML string via as.character() and saved to a filewith save_html().

Return an html formatted merge report

Description

Prints an html formatted table merge report of a set of merged maps, visualisingwith colors how different titers have been merged together.

Usage

htmlMergeReport(map)

Arguments

Value

A list() with a Rac_html_merge_report and shiny.tag class that canbe converted into an HTML string via as.character() and saved to a filewith save_html().

See Also

Other map merging functions:RacMerge.options(),mergeMaps(),mergeReport(),splitTiterLayers()

Return an html formatted titer table

Description

Prints an html formatted titer table, visualisingwith colors things like which titers are the maximum for each sera.

Usage

htmlTiterTable(map)

Arguments

Value

A list() with a Rac_html_merge_report and shiny.tag class that canbe converted into an HTML string via as.character() and saved to a filewith save_html().

See Also

htmlAdjustedTiterTable

Keep only the lowest stress map optimization

Description

Keep only the lowest stress map optimization

Usage

keepBestOptimization(map)

Arguments

Value

An acmap object with only the lowest stress optimization kept

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Keep specified optimization runs

Description

Keep only data from specified optimization runs.

Usage

keepOptimizations(map, optimization_numbers)

Arguments

Value

Returns the updated acmap object

See Also

Other functions to work with map optimizations:optimizationProperties,removeOptimizations(),sortOptimizations()

Keep only a single optimization run

Description

Keep only a single optimization run

Usage

keepSingleOptimization(map, optimization_number = 1)

Arguments

Value

An acmap object with only one optimization kept

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Get and set map layer names

Description

Get and set map layer names

Usage

layerNames(map)layerNames(map) <- value

Arguments

Value

A character vector of layer names

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Get all optimization details from an acmap object

Description

Gets the details associated with the all the optimizations of an acmap objectas a list.

Usage

listOptimizations(map)

Arguments

Value

Returns a list of lists with information about the optimizations

See Also

SeegetOptimization() for getting information about a singleoptimization.

Get the log titers from an acmap

Description

Converts titers to the log scale via via the transformation$log2(x/10)$, lessthan values are reduced by 1 on the log scale and greaterthan values are increased by 1, hence <10 => -1 and >1280 => 8

Usage

logtiterTable(map)

Arguments

Value

Returns a matrix of titers converted to the log scale

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:mapDistances(),mapResiduals(),pointStress,recalculateStress(),stressTable(),tableColbases(),tableDistances()

Return a list of logtiter table layers

Description

Return a list of logtiter table layers

Usage

logtiterTableLayers(map)

Arguments

Value

A list of numeric matrices with logtiter values

See Also

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),adjustedTiterTable(),dilutionStepsize(),mapDescription(),mapName(),titerTableFlat(),titerTableLayers(),titerTable()

Make an antigenic map from scratch

Description

This is a wrapper function for first making a map with table data then,running optimizations to make the map otherwise done withacmap()followed byoptimizeMap().

Usage

make.acmap(  titer_table = NULL,  ag_names = NULL,  sr_names = NULL,  number_of_dimensions = 2,  number_of_optimizations = 100,  minimum_column_basis = "none",  fixed_column_bases = NULL,  sort_optimizations = TRUE,  check_convergence = TRUE,  verbose = TRUE,  options = list(),  ...)

Arguments

Value

Returns an acmap object that has optimization run results.

See Also

Other map optimization functions:RacOptimizer.options(),moveTrappedPoints(),optimizeMap(),randomizeCoords(),relaxMapOneStep(),relaxMap()

Plot map vs table distances

Description

Plot map vs table distances

Usage

plot_map_table_distance(  map,  optimization_number = 1,  xlim,  ylim,  line_of_equality = TRUE)plotly_map_table_distance(  map,  optimization_number = 1,  xlim,  ylim,  line_of_equality = TRUE)

Arguments

Value

Returns the ggplot2 object

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Get bootstrap coordinates associated with a map

Description

This can be used to get information about the bootstrap run resultsafterbootstrapMap() has been run.

Usage

mapBootstrap_ptBaseCoords(map)mapBootstrap_agCoords(map)mapBootstrap_srCoords(map)

Arguments

Value

Returns a list of coordinate matrices for the points in each ofthe bootstrap runs

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Get or set an optimization run comment

Description

Get or set an optimization run comment

Usage

mapComment(map, optimization_number = 1)mapComment(map, optimization_number = 1) <- value

Arguments

Value

Gets or sets map comments for the optimization run.

See Also

Other map optimization attribute functions:colBases(),mapDimensions(),mapStress(),mapTransformation(),ptBaseCoords(),ptCoords()

Getting and setting the map description

Description

Getting and setting the map description

Usage

mapDescription(map)mapDescription(map) <- value

Arguments

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),adjustedTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapName(),titerTableFlat(),titerTableLayers(),titerTable()

Get the current map dimensions

Description

Get the current map dimensions

Usage

mapDimensions(map, optimization_number = 1)

Arguments

Value

Returns the number of dimensions for the optimization run.

See Also

Other map optimization attribute functions:colBases(),mapComment(),mapStress(),mapTransformation(),ptBaseCoords(),ptCoords()

Return calculated map distances for an acmap

Description

Takes the acmap object and calculates euclidean distances between antigensand sera for the currently selected or specified optimization.

Usage

mapDistances(map, optimization_number = 1)

Arguments

Value

Returns a matrix of map distances with antigens as rows and sera ascolumns.

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:logtiterTable(),mapResiduals(),pointStress,recalculateStress(),stressTable(),tableColbases(),tableDistances()

Open a shiny gadget to view the map

Description

This function is equivalent to runningrunGUI() and loading a map file, but thistakes the acmap object to open as an input argument.

Usage

mapGadget(map)

Arguments

Value

No value returned, called for the side effect of starting the gadget.

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),export_viewer(),ggplot.acmap(),plot.acmap(),setLegend(),view.acmap(),view.default(),view()

Getting and setting the map name

Description

Getting and setting the map name

Usage

mapName(map)mapName(map) <- value

Arguments

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),adjustedTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapDescription(),titerTableFlat(),titerTableLayers(),titerTable()

Check if a map has been fully relaxed

Description

Checks if the map optimization run can be relaxed further.

Usage

mapRelaxed(map, optimization_number = 1, options = list())

Arguments

Value

Returns TRUE or FALSE

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Get a table of residuals from an acmap

Description

This is the difference between the table distance and the map distance

Usage

mapResiduals(map, exclude_nd = FALSE, optimization_number = 1)

Arguments

Value

Returns a matrix of residuals, showing the residual error betweenmap distance and table distance for each antigen-sera pair.

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:logtiterTable(),mapDistances(),pointStress,recalculateStress(),stressTable(),tableColbases(),tableDistances()

Calculate the current map stress

Description

Calculate the current map stress

Usage

mapStress(map, optimization_number = 1)

Arguments

Value

A number giving the map stress

See Also

Other map optimization attribute functions:colBases(),mapComment(),mapDimensions(),mapTransformation(),ptBaseCoords(),ptCoords()

Reading map transformation data

Description

These functions can be used to query and if necessary set the maptransformation and map translation attributes for a given optimization run.

Usage

mapTransformation(map, optimization_number = 1)mapTransformation(map, optimization_number = 1) <- valuemapTranslation(map, optimization_number = 1)mapTranslation(map, optimization_number = 1) <- value

Arguments

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

Other map optimization attribute functions:colBases(),mapComment(),mapDimensions(),mapStress(),ptBaseCoords(),ptCoords()

Find matching antigens or sera between 2 maps

Description

Find matching antigens or sera between 2 maps

Usage

match_mapAntigens(map1, map2)match_mapSera(map1, map2)

Arguments

Value

Returns the indices of matching strains in map 2, or NA in theposition of strains not found.

See Also

Other functions to compare maps:procrustesData(),procrustesMap(),realignMap(),realignOptimizations()

Merging maps

Description

Functions to merge together two tables or maps.

Usage

mergeMaps(  ...,  method = "table",  number_of_dimensions,  number_of_optimizations,  minimum_column_basis = "none",  optimizer_options = list(),  merge_options = list(),  verbose = TRUE)

Arguments

Details

Maps can be merged in a number of ways depending upon the desiredresult.

Method 'table'

As you would expect, this merges the tables ofthe two maps but does not attempt to create any new optimizations and anyexisting optimizations are lost.

Method 'reoptimized-merge'

This merges the tables and thendoes a specified number of fresh optimizations from random startingcoordinates, ignoring any pre-existing optimization runs. It's exactly thesame as doing a 'table' merge and runningoptimizeMap() on the mergedtable.

Method 'incremental-merge'

This takes the currently selectedoptimization in the first map and then merges in the additional maps inturn. Each time any points not already found in the first map (or the lastmap in the incremental merge chain) are randomised and everything isrelaxed, this is repeated the specified number of times and the process isrepeated.

Method 'frozen-overlay'

This fixes the positions of points ineach map and tries to best match them simply through re-orientation. Oncethe best re-orientation is found, points that are in common between themaps are moved to the average position.

Method 'relaxed-overlay'

This is the same as thefrozen-overlay but points in the resulting map are then allowed to relax.

Method 'frozen-merge'

In this version, positions of allpoints in the first map are fixed and remain fixed, so the original mapdoes not change. The second map is then realigned to the first as closelyas possible and then all the new points appearing in the second map areallowed to relax into their new positions. This is a way to merge in newantigens and sera into a map without affecting the first one at all (andwas first implemented in lisp).

Value

Returns the merged map object

See Also

Other map merging functions:RacMerge.options(),htmlMergeReport(),mergeReport(),splitTiterLayers()

Return a merge report

Description

Prints a raw text merge report from merging two map tables.

Usage

mergeReport(map)

Arguments

Value

Returns a character matrix of information on merged titers.

See Also

Other map merging functions:RacMerge.options(),htmlMergeReport(),mergeMaps(),splitTiterLayers()

Move trapped points

Description

Sometimes points in a map optimization run get trapped in local optima, thisfunction tries to combat this by doing a grid search for each pointindividually moving points if a better optima is found. Note that this onlyperforms grid searches individually so won't find cases where a group ofpoints are trapped together in a local optima.

Usage

moveTrappedPoints(  map,  optimization_number = 1,  grid_spacing = 0.25,  max_iterations = 10,  options = list())

Arguments

Details

The search is iterative, searching for and moving points that are found to betrapped before relaxing the map and searching again, stopping either when nomore trapped points are found ormax_iterations is reached.

Value

Returns the acmap object with updated coordinates (if any trappedpoints found)

See Also

Other map optimization functions:RacOptimizer.options(),make.acmap(),optimizeMap(),randomizeCoords(),relaxMapOneStep(),relaxMap()

Get optimization properties

Description

Utility functions to get a vector of all the map optimizationproperties.

Usage

allMapStresses(map)allMapDimensions(map)

Arguments

Value

A numeric vector of values

See Also

Other functions to work with map optimizations:keepOptimizations(),removeOptimizations(),sortOptimizations()

Optimize antigen reactivity adjustments

Description

Usage

optimizeAgReactivity(  map,  optimization_number = 1,  reactivity_stress_weighting = 1,  fixed_ag_reactivities = rep(NA, numAntigens(map)),  start_pars = rep(0, numAntigens(map)),  reoptimize = FALSE,  number_of_optimizations = 100,  options = list())

Arguments

Value

The acmap object is returned with antigen reactivity adjustmentsset to the value calculated in the optimizer. This can be queried withagReactivityAdjustments().

Optimize an acmap

Description

Take an acmap object with a table of titer data and perform optimization runsto try and find the best arrangement of antigens and sera to represent theirantigenic similarity. Optimizations generated from each run with differentrandom starting conditions will be added to the acmap object.

Usage

optimizeMap(  map,  number_of_dimensions,  number_of_optimizations,  minimum_column_basis = "none",  fixed_column_bases = NULL,  titer_weights = NULL,  sort_optimizations = TRUE,  check_convergence = TRUE,  verbose = TRUE,  options = list())

Arguments

Details

This is the core function to run map optimizations. In essence, foreach optimization run, points are randomly distributed in n-dimensionalspace, the L-BFGS gradient-based optimization algorithm is applied to movepoints into an optimal position. Depending on the map, this may not be atrivial optimization process and results will depend upon the startingconditions so multiple optimization runs may be required. For a fullexplanation seevignette("intro-to-antigenic-cartography").

Minimum column basis and fixed column bases

Fixed column bases is a vector of fixed column bases for each sera, whereNA is specified (the default) column bases will be calculated according totheminimum_column_basis setting. Again for a full explanation of columnbases and what they mean seevignette("intro-to-antigenic-cartography").

Value

Returns the acmap object updated with new optimizations.

See Also

SeerelaxMap() for optimizing a given optimization starting fromits current coordinates.

Other map optimization functions:RacOptimizer.options(),make.acmap(),moveTrappedPoints(),randomizeCoords(),relaxMapOneStep(),relaxMap()

Order antigens and sera

Description

Functions to change the order of antigens and sera in a map

Usage

orderAntigens(map, order)orderSera(map, order)

Arguments

Value

An acmap object with points reordered

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Plot an antigenic map

Description

Method for plotting an antigenic map in two dimensions

Usage

## S3 method for class 'acmap'plot(  x,  optimization_number = 1,  xlim = NULL,  ylim = NULL,  plot_ags = TRUE,  plot_sr = TRUE,  plot_labels = FALSE,  plot_blobs = TRUE,  point_opacity = "automatic",  show_procrustes = TRUE,  show_error_lines = FALSE,  plot_stress = FALSE,  indicate_outliers = "arrowheads",  grid.col = "grey90",  grid.margin.col = "grey50",  outlier.arrow.col = grid.col,  fill.alpha = 0.8,  outline.alpha = 0.8,  procrustes.lwd = 2,  procrustes.col = "black",  procrustes.arr.type = "triangle",  procrustes.arr.length = 0.2,  procrustes.arr.width = 0.15,  label.offset = 0,  padding = 1,  cex = 1,  margins = rep(0.5, 4),  ...)

Arguments

Value

Called for the side effect of plotting the map but invisiblyreturns the map object.

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),export_viewer(),ggplot.acmap(),mapGadget(),setLegend(),view.acmap(),view.default(),view()

Get individual point stress

Description

Functions to get stress associated with individual points in a map.

Usage

agStress(map, antigens = TRUE, optimization_number = 1)srStress(map, sera = TRUE, optimization_number = 1)srStressPerTiter(map, sera = TRUE, optimization_number = 1)agStressPerTiter(map, antigens = TRUE, optimization_number = 1)

Arguments

Value

A numeric vector of point stresses

See Also

SeemapStress() for getting the total map stress directly.

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:logtiterTable(),mapDistances(),mapResiduals(),recalculateStress(),stressTable(),tableColbases(),tableDistances()

Return procrustes data on a map comparison

Description

Returns information about how similar point positions are in two maps,to get an idea of how similar antigenic positions are in for examplemaps made from two different datasets.

Usage

procrustesData(  map,  comparison_map,  optimization_number = 1,  comparison_optimization_number = 1,  antigens = TRUE,  sera = TRUE,  translation = TRUE,  scaling = FALSE)

Arguments

Value

Returns a list with information on antigenic distances between thealigned maps, and the rmsd of the point differences split by antigenpoints, serum points and total, or all points. The distances are a vectormatching the number of points in the main map, with NA in the position ofany points not found in the comparison map.

See Also

Other functions to compare maps:matchStrains,procrustesMap(),realignMap(),realignOptimizations()

Return procrustes information

Description

Returns information from one map procrusted to another.

Usage

procrustesMap(  map,  comparison_map,  optimization_number = 1,  comparison_optimization_number = 1,  antigens = TRUE,  sera = TRUE,  translation = TRUE,  scaling = FALSE,  keep_optimizations = FALSE)

Arguments

Value

Returns an acmap object with procrustes information added, which willbe shown when the map is plotted. To avoid ambiguity about whichoptimization run the procrustes was applied to, only the optimization runspecified byoptimization_number is kept in the map returned.

See Also

Other functions to compare maps:matchStrains,procrustesData(),realignMap(),realignOptimizations()

Getting and setting point annotation information

Description

Getting and setting point annotation information

Usage

agAnnotations(map)srAnnotations(map)agAnnotations(map) <- valuesrAnnotations(map) <- value

Arguments

Value

A character vector of point annotations.

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agLabIDs(),agSequences(),ptClades,srAttributes,srGroups(),srHomologousAgs(),srSequences()

Getting and setting base coordinates

Description

These functions get and set the base coordinates for a given optimizationrun.

Usage

ptBaseCoords(map, optimization_number = 1)agBaseCoords(map, optimization_number = 1)agBaseCoords(map, optimization_number = 1) <- valuesrBaseCoords(map, optimization_number = 1)srBaseCoords(map, optimization_number = 1) <- value

Arguments

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

agCoords()srCoords()

Other map optimization attribute functions:colBases(),mapComment(),mapDimensions(),mapStress(),mapTransformation(),ptCoords()

Get antigen or serum bootstrap blob information

Description

Get antigen or serum bootstrap blob information for plotting with theblob() function.

Usage

agBootstrapBlob(map, antigen, optimization_number = 1)srBootstrapBlob(map, serum, optimization_number = 1)agBootstrapBlobs(map, optimization_number = 1)srBootstrapBlobs(map, optimization_number = 1)ptBootstrapBlobs(map, optimization_number = 1)

Arguments

Value

Returns an object of class "blob" that can be plotted using theblob() function.

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Get antigen or serum bootstrap coordinates information

Description

Get antigen or serum bootstrap coordinates information

Usage

ptBootstrapCoords(map, point)agBootstrapCoords(map, antigen)srBootstrapCoords(map, serum)

Arguments

Value

Returns a matrix of coordinates for the point in each of thebootstrap runs

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Getting and setting point clade information

Description

Getting and setting point clade information

Usage

agClades(map)srClades(map)agClades(map) <- valuesrClades(map) <- value

Arguments

Value

A character vector of clade information.

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agLabIDs(),agSequences(),ptAnnotations,srAttributes,srGroups(),srHomologousAgs(),srSequences()

Getting and setting point coordinates

Description

Getting and setting of antigen and serum coordinates in a map optimizationrun (by default the currently selected one).

Usage

agCoords(map, optimization_number = 1)srCoords(map, optimization_number = 1)ptCoords(map, optimization_number = 1)ptCoords(map, optimization_number = 1) <- valueagCoords(map, optimization_number = 1) <- valuesrCoords(map, optimization_number = 1) <- value

Arguments

Details

These functions get and set point coordinates in a map. By defaultthese coordinates refer to the currently selected optimization run, unlessotherwise specified through theoptimization_number argument.

99\want to use but you should note that the outputs are actually the map basecoordinates after the transformation and translation associated with theoptimization run has been applied (seemapTransformation() andmapTranslation() for more details). When you set the antigen or serumcoordinates through these functions, the transformed coordinates are"baked" in and the map transformation and translation are reset.Consequently if you want to apply a transformation to all coordinatesgenerally, you are better off modifying the map translation andtransformation directly, as is done by functions likerotateMap() andtranslateMap().

Value

Returns a matrix of point coordinates.

See Also

agBaseCoords()srBaseCoords()mapTransformation()mapTranslation()

Other map optimization attribute functions:colBases(),mapComment(),mapDimensions(),mapStress(),mapTransformation(),ptBaseCoords()

Get and set point drawing order in map

Description

Point drawing order is a vector of indices defining the order inwhich points should be draw when plotting or viewing a map. Pointsare indexed in the same order as antigens then followed bysera.

Usage

ptDrawingOrder(map)ptDrawingOrder(map) <- value

Arguments

Value

A numeric vector of point drawing order information

See Also

Other map point style functions:applyPlotspec(),ptOpacity,ptStyles

Calculate point leverage

Description

These functions attempt to estimate leverage of each antigen, sera or titerby removing it from the data, relaxing the map, then calculating the rmsd ofthe procrustes comparison between the original and newly relaxed map. Columnbases will be recalculated unless you have specified them as fixed withfixedColBases().

Usage

agLeverage(map, antigens = TRUE, sera = TRUE)srLeverage(map, antigens = TRUE, sera = TRUE)titerLeverage(map, antigens = TRUE, sera = TRUE)

Arguments

Value

Returns a numeric vector of the leverage calculated for each of thepoints.

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Set point opacity in a map

Description

These are helper functions to quickly set the opacity of points in a map,they set both the fill and outline color opacity by modifying the filland outline colors to include an alpha channel for opacity. If you needmore control, for example different opacities for the fill and outlinecolors, you alter the fill and outline opacities yourself, for examplewith thegrDevices::adjustcolor() function.

Usage

agOpacity(map) <- valuesrOpacity(map) <- value

Arguments

Value

A numeric vector of point opacities.

See Also

Other map point style functions:applyPlotspec(),ptDrawingOrder(),ptStyles

Getting and setting point plotting styles

Description

These functions get and set the styles to use for each point when plotting.

Usage

agShown(map)srShown(map)agShown(map) <- valuesrShown(map) <- valueagSize(map)srSize(map)agSize(map) <- valuesrSize(map) <- valueagFill(map)srFill(map)agFill(map) <- valuesrFill(map) <- valueagOutline(map)srOutline(map)agOutline(map) <- valuesrOutline(map) <- valueagOutlineWidth(map)srOutlineWidth(map)agOutlineWidth(map) <- valuesrOutlineWidth(map) <- valueagRotation(map)srRotation(map)agRotation(map) <- valuesrRotation(map) <- valueagAspect(map)srAspect(map)agAspect(map) <- valuesrAspect(map) <- valueagShape(map)srShape(map)agShape(map) <- valuesrShape(map) <- value

Arguments

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

Other map point style functions:applyPlotspec(),ptDrawingOrder(),ptOpacity

Get antigen or serum triangulation blob information

Description

Get antigen or serum triangulation blob information for plotting with theblob() function.

Usage

agTriangulationBlob(map, antigen, optimization_number = 1)srTriangulationBlob(map, serum, optimization_number = 1)agTriangulationBlobs(map, optimization_number = 1)srTriangulationBlobs(map, optimization_number = 1)ptTriangulationBlobs(map, optimization_number = 1)

Arguments

Value

Returns an object of class "blob" that can be plotted using theblob() function.

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Randomize map coordinates

Description

Moves map coordinates back into random starting conditions, as performedbefore each optimization run. The maximum table distance is calculatedthen points are randomized in a box with side length equal to maximumtable distance multiplied bytable_dist_factor

Usage

randomizeCoords(map, optimization_number = 1, table_dist_factor = 2)

Arguments

Value

Returns an updated map object

See Also

Other map optimization functions:RacOptimizer.options(),make.acmap(),moveTrappedPoints(),optimizeMap(),relaxMapOneStep(),relaxMap()

Read in acmap data from a file

Description

Reads an antigenic map file and converts it into an acmap data object.

Usage

read.acmap(  filename,  optimization_number = NULL,  sort_optimizations = FALSE,  align_optimizations = FALSE)

Arguments

Value

Returns the acmap data object.

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Read in a table of titer data

Description

Reads in a table of titer data, converting it to a matrix of titers withlabelled column and row names. Missing titers should be represented by anasterisk character.

Usage

read.titerTable(filepath)

Arguments

Details

Currently supported file formats are .csv and .xls and .txt

Value

Returns a matrix of titers.

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Realign map to match another

Description

Realigns the coordinates of a map to match a target map as closely aspossible, based on aprocrustes analysis.Note that all optimization runs will be separately aligned to match asclosely as possible the first optimization run of the target map.

Usage

realignMap(map, target_map, translation = TRUE, scaling = FALSE)

Arguments

Value

Returns a map object aligned to the target map

See Also

Other functions to compare maps:matchStrains,procrustesData(),procrustesMap(),realignOptimizations()

Realigns optimizations in the map

Description

Realigns all map optimizations through rotation and translation to matchpoint positions as closely as possible to the first optimization run. Thisis done by default when optimizing a map and makes comparing point positionsin each optimization run much easier to do by eye.

Usage

realignOptimizations(map)

Arguments

Value

Returns the map with realigned optimizations

See Also

Other functions to compare maps:matchStrains,procrustesData(),procrustesMap(),realignMap()

Recalculate the stress associated with an acmap optimization

Description

Recalculates the stress associated with the currently selected oruser-specified optimization.

Usage

recalculateStress(map, optimization_number = 1)

Arguments

Value

Returns the recalculated map stress for a given optimization

See Also

SeepointStress() for getting the stress of individual points.

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,stressTable(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:logtiterTable(),mapDistances(),mapResiduals(),pointStress,stressTable(),tableColbases(),tableDistances()

Reflect a map

Description

Reflects map coordinates

Usage

reflectMap(map, axis = "x", optimization_number = NULL)

Arguments

Value

An acmap object with reflection applied

See Also

Other functions relating to map transformation:applyMapTransform(),rotateMap(),translateMap()

Relax a map

Description

Optimize antigen and serum positions starting from their current coordinatesin the selected or specified optimization.

Usage

relaxMap(  map,  optimization_number = 1,  fixed_antigens = FALSE,  fixed_sera = FALSE,  titer_weights = NULL,  options = list())

Arguments

Value

Returns an acmap object with the optimization relaxed.

See Also

SeeoptimizeMap() for performing new optimization runs from randomstarting coordinates.

Other map optimization functions:RacOptimizer.options(),make.acmap(),moveTrappedPoints(),optimizeMap(),randomizeCoords(),relaxMapOneStep()

Relax a map one step in the optimiser

Description

Relax a map one step in the optimiser

Usage

relaxMapOneStep(  map,  optimization_number = 1,  fixed_antigens = FALSE,  fixed_sera = FALSE,  options = list())

Arguments

Value

Returns an updated map object

See Also

Other map optimization functions:RacOptimizer.options(),make.acmap(),moveTrappedPoints(),optimizeMap(),randomizeCoords(),relaxMap()

Remove map optimizations

Description

Remove all optimization run data from a map object

Usage

removeOptimizations(map)

Arguments

Value

An acmap object with all optimizations removed

See Also

Other functions to work with map optimizations:keepOptimizations(),optimizationProperties,sortOptimizations()

Remove antigens and sera

Description

Functions to remove antigens and sera from a map

Usage

removeAntigens(map, antigens)removeSera(map, sera)

Arguments

Value

An acmap object with points removed

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Rotate a map

Description

Apply a rotation to an antigenic map

Usage

rotateMap(map, degrees, axis = NULL, optimization_number = NULL)

Arguments

Value

An acmap object with rotation applied

See Also

Other functions relating to map transformation:applyMapTransform(),reflectMap(),translateMap()

Open the Racmacs GUI

Description

This function opens the Racmacs GUI in a new window

Usage

runGUI()

Value

Nothing returned, called only for the side effect of starting the viewer.

See Also

Other shiny app functions:RacViewer-shiny,view.acmap()

Save acmap data to a file

Description

Save acmap data to a file. The preferred extension is ".ace", althoughthe format of the file will be a json file of map data compressed using'xz' compression.

Usage

save.acmap(  map,  filename,  compress = FALSE,  pretty = !compress,  round_titers = FALSE)

Arguments

Value

No return value, called for the side effect of saving the map datato the file.

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.coords(),save.titerTable(),subsetCommonPoints,subsetMap()

Save acmap coordinate data to a file

Description

Saves acmap coordinate data of all or specified antigens and sera to a .csvfile.

Usage

save.coords(  map,  filename,  optimization_number = 1,  antigens = TRUE,  sera = TRUE)

Arguments

Value

No return value, called for the side effect of saving thecoordinate data.

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.titerTable(),subsetCommonPoints,subsetMap()

Save titer data to a file

Description

Saves titer data of all or specified antigens and sera to a .csv file.

Usage

save.titerTable(map, filename, antigens = TRUE, sera = TRUE)

Arguments

Value

No return value, called for the side effect of saving the titerdata to the file.

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),subsetCommonPoints,subsetMap()

Set acmap legend

Description

This sets the acmap legend used when viewing a map for example.

Usage

setLegend(map, legend, fill, style.bottom = "8px", style.right = "8px")

Arguments

Value

Returns the updated acmap object

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),export_viewer(),ggplot.acmap(),mapGadget(),plot.acmap(),view.acmap(),view.default(),view()

Sort optimizations by stress

Description

Sorts all the optimization runs for a given map object by stress(lowest to highest). Note that this is done by default when runningoptimizeMap().

Usage

sortOptimizations(map)

Arguments

Value

An acmap object with optimizations sorted by stress.

See Also

Other functions to work with map optimizations:keepOptimizations(),optimizationProperties,removeOptimizations()

Split a map made up from titer layers into a list of separate maps each with a titer tablecorresponding to one of the layers

Description

Split a map made up from titer layers into a list of separate maps each with a titer tablecorresponding to one of the layers

Usage

splitTiterLayers(map)

Arguments

Value

A list of acmap objects

See Also

Other map merging functions:RacMerge.options(),htmlMergeReport(),mergeMaps(),mergeReport()

Getting and setting sera attributes

Description

These functions get and set the sera attributes for a map.

Usage

srIDs(map)srIDs(map) <- valuesrDates(map)srDates(map) <- valuesrReference(map)srReference(map) <- valuesrNames(map)srNames(map) <- valuesrExtra(map)srExtra(map) <- valuesrPassage(map)srPassage(map) <- valuesrLineage(map)srLineage(map) <- valuesrReassortant(map)srReassortant(map) <- valuesrStrings(map)srStrings(map) <- valuesrSpecies(map)srSpecies(map) <- value

Arguments

Value

Returns either the requested attribute when using a getter function or

the updated acmap object when using the setter function.

See Also

agAttributes()

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agLabIDs(),agSequences(),ptAnnotations,ptClades,srGroups(),srHomologousAgs(),srSequences()

Getting and setting sera groups

Description

These functions get and set the sera groupings for a map.

Usage

srGroups(map)srGroups(map) <- value

Arguments

Value

A factor vector of serum groups

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agLabIDs(),agSequences(),ptAnnotations,ptClades,srAttributes,srHomologousAgs(),srSequences()

Get and set homologous antigens for sera

Description

Get and set indices of homologous antigens to sera in an antigenic map

Usage

srHomologousAgs(map)srHomologousAgs(map) <- value

Arguments

Value

A list, where each entry is a vector of indices for homologousantigens, or a length 0 vector where no homologous antigen is present.

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agLabIDs(),agSequences(),ptAnnotations,ptClades,srAttributes,srGroups(),srSequences()

Getting and setting sera sequence information

Description

Getting and setting sera sequence information

Usage

srSequences(map, missing_value = ".")srSequences(map) <- valuesrNucleotideSequences(map, missing_value = ".")srNucleotideSequences(map) <- value

Arguments

Value

A character matrix of sequences with rows equal to the number ofsera.

See Also

Other antigen and sera attribute functions:agAttributes,agGroups(),agHomologousSr(),agLabIDs(),agSequences(),ptAnnotations,ptClades,srAttributes,srGroups(),srHomologousAgs()

Standardize strain names

Description

This is a utility function to help standardise antigen names into a moreconsistent format, also attempting to break apart different componentsof the name.

Usage

standardizeStrainNames(  names,  default_species = NA,  default_virus_type = "A",  default_virus_subtype = "HXNX")

Arguments

Value

Returns a tibble of standardised names and extracted information

Get a stress table from an acmap

Description

Get a stress table from an acmap

Usage

stressTable(map, optimization_number = 1)

Arguments

Value

Returns a matrix of stresses, showing how much each antigen and serameasurement contributes to stress in the selected or specifiedoptimization.

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),tableColbases(),tableDistances(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:logtiterTable(),mapDistances(),mapResiduals(),pointStress,recalculateStress(),tableColbases(),tableDistances()

Remove antigens and sera

Description

Functions to subset a list of maps to include only antigens, antigen groups, seraor serum groups that are in common between them.

Usage

subsetCommonAgs(maps)subsetCommonSrGroups(maps)

Arguments

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetMap()

Subset an antigenic map

Description

Subset an antigenic map to contain only specified antigens and sera

Usage

subsetMap(map, antigens = TRUE, sera = TRUE)

Arguments

Value

Returns a new antigenic map containing only match antigens and sera

See Also

Other functions for working with map data:acmap(),addOptimization(),agReactivityAdjustments(),as.json(),edit_agNames(),edit_srNames(),keepBestOptimization(),keepSingleOptimization(),layerNames(),orderPoints,read.acmap(),read.titerTable(),removePoints,save.acmap(),save.coords(),save.titerTable(),subsetCommonPoints

Calculate column bases for a titer table

Description

For more information on column bases, what they mean and how they arecalculated seevignette("intro-to-antigenic-cartography")

Usage

tableColbases(  titer_table,  minimum_column_basis = "none",  fixed_column_bases = rep(NA, ncol(titer_table)),  ag_reactivity_adjustments = rep(0, nrow(titer_table)))

Arguments

Value

Returns a numeric vector of the log-converted column bases for thetable

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableDistances(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:logtiterTable(),mapDistances(),mapResiduals(),pointStress,recalculateStress(),stressTable(),tableDistances()

Return calculated table distances for an acmap

Description

Takes the acmap object and, assuming the column bases associated with thecurrently selected or specified optimization, returns the table distancescalculated from the titer data. For more information on column bases andtheir role in antigenic cartography seevignette("intro-to-antigenic-cartography")

Usage

tableDistances(map, optimization_number = 1)

Arguments

Value

Returns a matrix of numeric table distances

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),triangulationBlobs(),unstableMaps

Other functions relating to map stress calculation:logtiterTable(),mapDistances(),mapResiduals(),pointStress,recalculateStress(),stressTable(),tableColbases()

Getting and setting map titers

Description

Functions to get and set the map titer table. Note that when setting thetiter table like this any titer table layer information is lost, this isnormally not a problem unless the map is a result of merging two titer tablestogether previously and you then go on the merge the titers again.

Usage

titerTable(map)titerTable(map) <- value

Arguments

Value

Returns a character matrix of titers.

See Also

adjustedTiterTable(),htmlTiterTable()

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),adjustedTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapDescription(),mapName(),titerTableFlat(),titerTableLayers()

Getting and setting the flat titer table

Description

These are underlying functions to get and set the "flat" version of the titertable only. When a map is merged, the titer tables are merged but a record ofthe original titers associated with each map are kept as titer table layersso that information on the original set of titers that made up the merge isnot lost. At the same time, the merged titer version of the titer table iscreated and saved as the titer_table_flat attribute. When you access titersthrough thetiterTable() function, the flat version of the titer table isretrieved (only really a relevant distinction for merged maps). When you settiters through⁠titerTable<-()⁠ titer table layers are lost. These functionsallow you to manipulate the flat version without affecting the titer tablelayers information.

Usage

titerTableFlat(map)titerTableFlat(map) <- value

Arguments

Value

Returns a character matrix of titers.

See Also

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),adjustedTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapDescription(),mapName(),titerTableLayers(),titerTable()

Getting and setting titer table layers

Description

Functions to get and set the underlying titer table layers of a map (seedetails).

Usage

titerTableLayers(map)titerTableLayers(map) <- value

Arguments

Details

When you merge maps withmergeMaps() repeated antigen - serumtiters are merged to create a new titer table but information on theoriginal titers is not lost. The original titer tables, aligned to theirnew positions in the merged table, are kept as separate layers that can beaccessed with these functions. If you have merged a whole bunch ofdifferent maps, these functions can be useful to check for example,variation in titer seen between a single antigen and serum pair.

Value

A list of character matrices of titers.

See Also

Other map attribute functions:acmapAttributes,adjustedLogTiterTable(),adjustedTiterTable(),dilutionStepsize(),logtiterTableLayers(),mapDescription(),mapName(),titerTableFlat(),titerTable()

Translate a map

Description

Translates map coordinates

Usage

translateMap(map, translation, optimization_number = NULL)

Arguments

Value

An acmap object with transformation applied

See Also

Other functions relating to map transformation:applyMapTransform(),reflectMap(),rotateMap()

Calculate triangulation blobs data for an antigenic map

Description

This function is to help give an idea of how well coordinated each point isin a map, and to give some idea of uncertainty in it's position. It worksby moving each point in a grid search and seeing how the total map stresschanges, see details.

Usage

triangulationBlobs(  map,  optimization_number = 1,  stress_lim = 1,  grid_spacing = 0.25,  antigens = TRUE,  sera = TRUE,  .check_relaxation = TRUE,  .options = list())

Arguments

Details

The region or regions of the plot where total map stress is notincreased above a certain threshold (stress_lim) are shown when the mapis plotted. This function is really to check whether point positions areclearly very uncertain, for example the underlying titers may support anantigen being a certain distance away from a group of other points but dueto the positions of the sera against which it was titrated the directionwould be unclear, and you might see a blob that forms an arc or "banana"that represents this. Note that it is not really a confidence intervalsince a point may be well coordinated in terms of the optimization butit's position may still be defined by perhaps only one particular titerwhich is itself uncertain. For something more akin to confidence intervalsyou can use other diagnostic functions likebootstrapMap().

Value

Returns the acmap data object with triangulation blob information added,which will be shown when the map is plotted

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),unstableMaps

Notes on unstable maps

Description

Tips for exploring maps that are difficult to find a consistent optimal solution for.

Details

Maps may be difficult to optimize or unstable for a variety of reasons, a commonone with larger maps being simply that it is difficult to find a global optimaand so many different local optima are found each time.

One approach that can sometimeshelp is to consider running the optimizer withoptions = list(dim_annealing = TRUE)(see seevignette("intro-to-antigenic-cartography") for an explanation of thedimensional annealing approach). However be wary that in our experience, while applyingdimensional annealing can sometimes significantly speed up finding a better minima, itcan also sometimes be more prone to getting stuck in worse local optima.

If there are many missing or non-detectable titers it is alsopossible that points in map are too poorly connected to find a robustsolution, to check this seemapCohesion().

See Also

Other map diagnostic functions:agCohesion(),bootstrapBlobs(),bootstrapMap(),checkHemisphering(),dimensionTestMap(),logtiterTable(),map-table-distances,mapBootstrapCoords,mapDistances(),mapRelaxed(),mapResiduals(),pointStress,ptBootstrapBlob,ptBootstrapCoords(),ptLeverage,ptTriangulationBlob,recalculateStress(),stressTable(),tableColbases(),tableDistances(),triangulationBlobs()

Update the ferret serum names to match antigens

Description

Update the ferret serum names to match antigens

Usage

update_ferret_seraNames(map, dictionary_file = NULL)

Arguments

Value

Returns the updated map data object

S3 method for viewing objects

Description

S3 method for viewing objects

Usage

view(x, ...)

Arguments

Value

When called on an acmap object, returns an htmlwidget object thatcan be used to interactively view the map. Otherwise by default itsimply calls the print method of the respective object with no returnvalue.

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),export_viewer(),ggplot.acmap(),mapGadget(),plot.acmap(),setLegend(),view.acmap(),view.default()

Viewing racmap objects

Description

View a racmap object in the interactive viewer.

Usage

## S3 method for class 'acmap'view(  x,  optimization_number = 1,  ...,  .jsCode = NULL,  .jsData = NULL,  select_ags = NULL,  select_sr = NULL,  show_procrustes = NULL,  show_diagnostics = NULL,  num_optimizations = 1,  options = list())

Arguments

Value

Returns an htmlwidget object

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),export_viewer(),ggplot.acmap(),mapGadget(),plot.acmap(),setLegend(),view.default(),view()

Other shiny app functions:RacViewer-shiny,runGUI()

Default method for viewing objects

Description

Default method for viewing objects

Usage

## Default S3 method:view(x, ...)

Arguments

Value

No value returned, simply calls the print method on the object

See Also

Other functions to view maps:RacViewer.options(),RacViewer(),export_viewer(),ggplot.acmap(),mapGadget(),plot.acmap(),setLegend(),view.acmap(),view()