Package 'CytoMDS' reference manual

Title:	Low Dimensions projection of cytometry samples
Description:	This package implements a low dimensional visualization of a set of cytometry samples, in order to visually assess the 'distances' between them. This, in turn, can greatly help the user to identify quality issues like batch effects or outlier samples, and/or check the presence of potential sample clusters that might align with the exeprimental design. The CytoMDS algorithm combines, on the one hand, the concept of Earth Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the other hand, the Multi Dimensional Scaling (MDS) algorithm for the low dimensional projection. Also, the package provides some diagnostic tools for both checking the quality of the MDS projection, as well as tools to help with the interpretation of the axes of the projection.
Authors:	Philippe Hauchamps [aut, cre] , Laurent Gatto [aut] , Dan Lin [ctb]
Maintainer:	Philippe Hauchamps <[email protected]>
License:	GPL-3
Version:	1.3.1
Built:	2024-11-17 03:10:47 UTC
Source:	https://github.com/bioc/CytoMDS

Summary statistics per channel computation

Description

Computation of summary statistic for selected channels, for all flowFrames of a flowSet, or for all expression matrices of a list. This method provides three different input modes:

the user provides directly a flowCore::flowSet loaded in memory (RAM)
the user provides directly a list of expression matrices of which the column names are the channel/marker names
the user provides (1.) a number of samples nSamples; (2.) an ad-hoc function that takes as input an index between 1 and nSamples, and codes the method to load the corresponding expression matrix in memory;

Usage

channelSummaryStats(
  x,
  loadExprMatrixFUN = NULL,
  loadExprMatrixFUNArgs = NULL,
  channels = NULL,
  statFUNs = stats::median,
  verbose = FALSE,
  BPPARAM = BiocParallel::SerialParam(),
  BPOPTIONS = BiocParallel::bpoptions(packages = c("flowCore"))
)
channelSummaryStats(
  x,
  loadExprMatrixFUN = NULL,
  loadExprMatrixFUNArgs = NULL,
  channels = NULL,
  statFUNs = stats::median,
  verbose = FALSE,
  BPPARAM = BiocParallel::SerialParam(),
  BPOPTIONS = BiocParallel::bpoptions(packages = c("flowCore"))
)

Arguments

`x`	can be: a flowCore::flowSet a list of expression matrices (Double matrix with named columns) the number of samples (integer >=1)
`loadExprMatrixFUN`	the function used to translate an integer index into an expression matrix. In other words, the function should code how to load the `index`th expression matrix into memory. IMPORTANT: the expression matrix index should be the first function argument and should be named `exprMatrixIndex`.
`loadExprMatrixFUNArgs`	(optional) a named list containing additional input parameters of `loadExprMatrixFUN()`
`channels`	which channels needs to be included: if it is a character vector, it can refer to either the channel names, or the marker names if it is a numeric vector, it refers to the indices of channels in `fs` if NULL, all scatter and fluorescent channels of `fs` #' will be selected.
`statFUNs`	a list (possibly of length one) of functions to call to calculate the statistics, or a simple function. This list can be named, in that case, these names will be transfered to the returned list.
`verbose`	if `TRUE`, output a message after each single statistics calculation
`BPPARAM`	sets the `BPPARAM` back-end to be used for the computation. If not provided, will use `BiocParallel::SerialParam()` (no task parallelization)
`BPOPTIONS`	sets the BPOPTIONS to be passed to `bplapply()` function. Note that if you use a `SnowParams` back-end, you need to specify all the packages that need to be loaded for the different CytoProcessingStep to work properly (visibility of functions). As a minimum, the `flowCore` package needs to be loaded. (hence the default `BPOPTIONS = bpoptions(packages = c("flowCore"))` )

Value

a list of named statistic matrices. In each stat matrix, the columns are the channel statistics for all flowFrames of the flowSet. Exception: if only one stat function (and not a list) is passed in statFUNs, the return value is simplified to the stat matrix itself.

Examples


library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)

channelsOrMarkers <- c("FSC-A", "SSC-A", "BV785 - CD3")

# calculate mean for each 4 selected channels, for each 2 samples

channelMeans <- channelSummaryStats(
    OMIP021Trans,
    channels = channelsOrMarkers,
    statFUNs = mean)
    
# calculate median AND std deviation
# for each 4 selected channels, for each 2 samples

channelMedians <- channelSummaryStats(
    OMIP021Trans,
    channels = channelsOrMarkers,
    statFUNs = list("median" = stats::median, 
                    "std.dev" = stats::sd))
 
library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)

channelsOrMarkers <- c("FSC-A", "SSC-A", "BV785 - CD3")

# calculate mean for each 4 selected channels, for each 2 samples

channelMeans <- channelSummaryStats(
    OMIP021Trans,
    channels = channelsOrMarkers,
    statFUNs = mean)
    
# calculate median AND std deviation
# for each 4 selected channels, for each 2 samples

channelMedians <- channelSummaryStats(
    OMIP021Trans,
    channels = channelsOrMarkers,
    statFUNs = list("median" = stats::median, 
                    "std.dev" = stats::sd))

metric MDS projection of sample

Description

Multi-dimensional scaling projection of samples, using a distance matrix as an input. The MDS algorithm is not the classical MDS (cmdscale alike, aka Torgerson's algorithm), but is the SMACOF algorithm for metric distances that are not necessarily euclidean. After having obtained the projections on the nDim dimensions, we always apply svd decomposition to visualize as first axes the ones that contain the most variance of the projected dataset in nDim dimensions. Instead of being provided directly by the user, the nDim parameter can otherwise be found iteratively by finding the minimum nDim parameter that allows the projection to reach a target pseudo RSquare. If this is the case, the maxDim parameter is used to avoid looking for too big projection spaces.

Usage

computeMetricMDS(
  pwDist,
  nDim = NULL,
  seed = NULL,
  targetPseudoRSq = 0.95,
  maxDim = 128,
  ...
)
computeMetricMDS(
  pwDist,
  nDim = NULL,
  seed = NULL,
  targetPseudoRSq = 0.95,
  maxDim = 128,
  ...
)

Arguments

`pwDist`	(`nSamples` rows, `nSamples` columns), previously calculated pairwise distances between samples, must be provided as a full symmetric square matrix, with 0. diagonal
`nDim`	number of dimensions of projection, as input to SMACOF algorithm if not provided, will be found iteratively using `targetPseudoRSq`
`seed`	seed to be set when launching SMACOF algorithm (e.g. when `init` is set to `"random"` but not only)
`targetPseudoRSq`	target pseudo RSquare to be reached (only used when `nDim` is set to NULL)
`maxDim`	in case `nDim` is found iteratively, maximum number of dimensions the search procedure is allowed to explore
`...`	additional parameters passed to SMACOF algorithm

Value

an object of S4 class MDS

Examples


library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    
# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)


library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    
# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)

Calculate Earth Mover's distance between two samples

Description

Calculate Earth Mover's distance between two samples

Usage

EMDDist(
  x1,
  x2,
  channels = NULL,
  binSize = 0.05,
  minRange = -10,
  maxRange = 10,
  returnAll = FALSE
)
EMDDist(
  x1,
  x2,
  channels = NULL,
  binSize = 0.05,
  minRange = -10,
  maxRange = 10,
  returnAll = FALSE
)

Arguments

`x1`	can be either a flowCore::flowFrame, or an expression matrix
`x2`	can be either a flowCore::flowFrame, or an expression matrix
`channels`	which channels (integer index(ices) or character(s)): if it is a character vector, it can refer to either the channel names, or the marker names if x1 and x2 have been provided as flowCore::flowFrame if it is a numeric vector, it refers to the indexes of channels in `x1` if NULL : if `x1` and `x2` are provided as flowCore::flowFrames, all scatter and fluorescent channels of `x1` will be selected; if `x1` and `x2` are provided as expression matrices, all colnames of `x1` will be selected.
`binSize`	size of equal bins to approximate the marginal distributions.
`minRange`	minimum value taken when approximating the marginal distributions
`maxRange`	maximum value taken when approximating the marginal distributions
`returnAll`	If `TRUE`, distributions and marginal distribution distances are returned as well. Default = `FALSE`.

Value

the Earth Mover's distance between x1 and x2, which is calculated by summing up all EMD approximates for the marginal distributions of each channel

Examples


library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)

# distance with itself (all channels at once)
# => should return 0
dist0 <- EMDDist(
    x1 = OMIP021Trans[[1]],
    x2 = OMIP021Trans[[1]])

# returning only distance, 2 channels
dist1 <- EMDDist(
    x1 = OMIP021Trans[[1]], 
    x2 = OMIP021Trans[[2]], 
    channels = c("FSC-A", "SSC-A"))

# using only one channel, passed by marker name
dist2 <- EMDDist(x1 = OMIP021Trans[[1]], 
                 x2 = OMIP021Trans[[2]], 
                 channels = c("BV785 - CD3"))

# using only one channel, passed by index
dist3 <- EMDDist(x1 = OMIP021Trans[[1]], 
                 x2 = OMIP021Trans[[2]], 
                 channels = 10)

dist2 == dist3

library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)

# distance with itself (all channels at once)
# => should return 0
dist0 <- EMDDist(
    x1 = OMIP021Trans[[1]],
    x2 = OMIP021Trans[[1]])

# returning only distance, 2 channels
dist1 <- EMDDist(
    x1 = OMIP021Trans[[1]], 
    x2 = OMIP021Trans[[2]], 
    channels = c("FSC-A", "SSC-A"))

# using only one channel, passed by marker name
dist2 <- EMDDist(x1 = OMIP021Trans[[1]], 
                 x2 = OMIP021Trans[[2]], 
                 channels = c("BV785 - CD3"))

# using only one channel, passed by index
dist3 <- EMDDist(x1 = OMIP021Trans[[1]], 
                 x2 = OMIP021Trans[[2]], 
                 channels = 10)

dist2 == dist3

Plot of channel intensity marginal densities

Description

ggplotMarginalDensities uses ggplot2 to draw plots of marginal densities of selected channels of a flowSet. If the flowSet contains several flowFrames, all events are concatenated together. By default, a pseudo Rsquare projection quality indicator, and the number of dimensions of the MDS projection are provided in sub-title

Usage

ggplotMarginalDensities(
  x,
  sampleSubset,
  channels,
  pDataForColour,
  pDataForGroup,
  nEventInSubsample = Inf,
  seed = NULL,
  transList
)
ggplotMarginalDensities(
  x,
  sampleSubset,
  channels,
  pDataForColour,
  pDataForGroup,
  nEventInSubsample = Inf,
  seed = NULL,
  transList
)

Arguments

`x`	a `flowCore::flowSet` (or a single `flowCore::flowFrame`)
`sampleSubset`	(optional) a logical vector, of size `nrow(pData)`, which is by construction the nb of samples, indicating which samples to keep in the plot. Typically it is obtained through the evaluation of a logical condition on `pData` rows.
`channels`	(optional)
`pDataForColour`	(optional) which `phenoData(fs)` variable will be used as colour aesthetic. Should be a character.
`pDataForGroup`	(optional) which `phenoData(fs)` variable will be used as group aesthetic. Should be a character.
`nEventInSubsample`	how many event to take (per flowFrame of the flowSet).
`seed`	if not null, used in subsampling.
`transList`	a `flowCore::transformList` that will be applied before plotting.

Value

a ggplot object

Examples


library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    

# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))
flowCore::pData(fsAll)$lbl <- paste0("S", 1:5)

# plot densities, all samples together
p <- ggplotMarginalDensities(fsAll)

# plot densities, per sample
p <- ggplotMarginalDensities(fsAll, pDataForGroup = "lbl")

# plot densities, per sample and coloured by group
p <- ggplotMarginalDensities(
    fsAll, 
    pDataForGroup = "lbl",
    pDataForColour = "grpId")

library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    

# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))
flowCore::pData(fsAll)$lbl <- paste0("S", 1:5)

# plot densities, all samples together
p <- ggplotMarginalDensities(fsAll)

# plot densities, per sample
p <- ggplotMarginalDensities(fsAll, pDataForGroup = "lbl")

# plot densities, per sample and coloured by group
p <- ggplotMarginalDensities(
    fsAll, 
    pDataForGroup = "lbl",
    pDataForColour = "grpId")

Plot of Metric MDS object

Description

ggplotSampleMDS uses ggplot2 to provide plots of Metric MDS results. By default, a pseudo Rsquare projection quality indicator, and the number of dimensions of the MDS projection are provided in sub-title

Usage

ggplotSampleMDS(
  mdsObj,
  pData,
  sampleSubset,
  projectionAxes = c(1, 2),
  biplot = FALSE,
  biplotType = c("correlation", "regression"),
  extVariables,
  pDataForColour,
  pDataForShape,
  pDataForLabel,
  pDataForAdditionalLabelling,
  sizeReflectingStress = FALSE,
  title = "Multi Dimensional Scaling",
  displayPointLabels = TRUE,
  pointLabelSize = 3.88,
  repelPointLabels = TRUE,
  displayArrowLabels = TRUE,
  arrowLabelSize = 3.88,
  repelArrowLabels = FALSE,
  arrowThreshold = 0.8,
  flipXAxis = FALSE,
  flipYAxis = FALSE,
  displayPseudoRSq = TRUE,
  ...
)
ggplotSampleMDS(
  mdsObj,
  pData,
  sampleSubset,
  projectionAxes = c(1, 2),
  biplot = FALSE,
  biplotType = c("correlation", "regression"),
  extVariables,
  pDataForColour,
  pDataForShape,
  pDataForLabel,
  pDataForAdditionalLabelling,
  sizeReflectingStress = FALSE,
  title = "Multi Dimensional Scaling",
  displayPointLabels = TRUE,
  pointLabelSize = 3.88,
  repelPointLabels = TRUE,
  displayArrowLabels = TRUE,
  arrowLabelSize = 3.88,
  repelArrowLabels = FALSE,
  arrowThreshold = 0.8,
  flipXAxis = FALSE,
  flipYAxis = FALSE,
  displayPseudoRSq = TRUE,
  ...
)

Arguments

`mdsObj`	a MDS object, output of the `computeMetricMDS()` method.
`pData`	(optional) a data.frame providing user input sample data. These can be design of experiment variables, phenotype data per sample,... and will be used to highlight sample categories in the plot and/or for subsetting.
`sampleSubset`	(optional) a logical vector, of size `nrow(pData)`, which is by construction the nb of samples, indicating which samples to keep in the plot. Typically it is obtained through the evaluation of a logical condition on `pData` rows.
`projectionAxes`	which two axes should be plotted (should be a numeric vector of length 2)
`biplot`	if TRUE, adds projection of external variables
`biplotType`	type of biplot used: if "correlation", projection of external variables will be according to Pearson correlations w.r.t. projection axes (arrow x & y coordinates) if "regression", a linear regression of external variables using the 2 projection axes as explanatory variables is performed, and the projection of external variables will be according to regression coefficients (arrow direction) and R square of regression (arrow size)
`extVariables`	are used to generate a biplot these are the external variables that will be used in the biplot. They should be provided as a matrix with named columns corresponding to the variables. The number of rows should be the same as the number of samples. The matrix might contain some NA's, in that case only complete rows will be used to calculate biplot arrows.
`pDataForColour`	(optional) which `pData` variable will be used as colour aesthetic. Should be a character.
`pDataForShape`	(optional) which `pData` variable will be used as shape aesthetic. Should be a character.
`pDataForLabel`	(optional) which `pData` variable will be used as point labels in the plot. Should be a character. If missing, point labels will be set equal to point names defined in MDS object (if not NULL, otherwise no labels will be set).
`pDataForAdditionalLabelling`	(optional) which `pData` variable(s) will be add to the ggplot mapping, as to make them available for plotly tooltipping. Should be an array of character of maximum length 3. Note this works only if biplot=FALSE, as biplots contain circle and arrows that are currently not supported under `ggplotly`.
`sizeReflectingStress`	if TRUE, size of points will appear proportional to stress by point, i.e. the bigger the sample point appears, the less accurate its representation is (in terms of distances w.r.t. other points)
`title`	title to give to the plot
`displayPointLabels`	if TRUE, displays labels attached to points (see `pDataForLabels` for the setting of the label values)
`pointLabelSize`	size of point labels (default: 3.88 as in `geom_text()`)
`repelPointLabels`	if TRUE, uses `ggrepel::geom_text_repel()` instead of `ggplot2::geom_text()` (try to split the labels such that they do not overlap) for the points
`displayArrowLabels`	if TRUE, displays arrows labels (only with biplot)
`arrowLabelSize`	size of arrow labels (default: 3.88 as in `geom_text()`)
`repelArrowLabels`	if TRUE, uses `ggrepel::geom_text_repel()` instead of `ggplot2::geom_text()` for the arrows (only with biplot)
`arrowThreshold`	(only with biplot), arrows will be made barely visible if their length is (in absolute value) less than this threshold.
`flipXAxis`	if TRUE, take the opposite of x values (provided as it might ease low dimensional projection comparisons)
`flipYAxis`	if TRUE, take the opposite of y values (provided as it might ease low dimensional projection comparisons)
`displayPseudoRSq`	if TRUE, display pseudo RSquare in subtitle, on top of nb of dimensions
`...`	additional parameters passed to `ggrepel::geom_text_repel()` (if used)

Value

a ggplot object

Examples


library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)

# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)



# plot mds projection on axes 1 and 2,
# use 'grpId' for colour, 'type' for shape, and no label 

p_12 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "grpId",
    pDataForShape = "type")

# plot mds projection on axes 3 and 4,
# use 'grpId' for colour, and 'name' as point label

p_34 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(3,4),
    pDataForColour = "grpId",
    pDataForLabel = "name")

# plot mds projection on axes 1 and 2,
# use 'group' for colour, 'type' for shape, and 'name' as point label
# have sample point size reflecting 'stress'
# i.e. quality of projection w.r.t. distances to other points

p12_Stress <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "grpId",
    pDataForLabel = "name",
    pDataForShape = "type",
    sizeReflectingStress = TRUE)

# try to associate axes with median of each channel
# => use bi-plot

extVars <- channelSummaryStats(
    fsAll,
    channels = c("FSC-A", "SSC-A"),
    statFUNs = stats::median)


bp_12 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    biplot = TRUE,
    extVariables = extVars,
    pDataForColour = "grpId",
    pDataForShape = "type",
    seed = 0)

bp_34 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(3,4),
    biplot = TRUE,
    extVariables = extVars,
    pDataForColour = "grpId",
    pDataForLabel = "name",
    seed = 0)

library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)

# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)



# plot mds projection on axes 1 and 2,
# use 'grpId' for colour, 'type' for shape, and no label 

p_12 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "grpId",
    pDataForShape = "type")

# plot mds projection on axes 3 and 4,
# use 'grpId' for colour, and 'name' as point label

p_34 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(3,4),
    pDataForColour = "grpId",
    pDataForLabel = "name")

# plot mds projection on axes 1 and 2,
# use 'group' for colour, 'type' for shape, and 'name' as point label
# have sample point size reflecting 'stress'
# i.e. quality of projection w.r.t. distances to other points

p12_Stress <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "grpId",
    pDataForLabel = "name",
    pDataForShape = "type",
    sizeReflectingStress = TRUE)

# try to associate axes with median of each channel
# => use bi-plot

extVars <- channelSummaryStats(
    fsAll,
    channels = c("FSC-A", "SSC-A"),
    statFUNs = stats::median)


bp_12 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    biplot = TRUE,
    extVariables = extVars,
    pDataForColour = "grpId",
    pDataForShape = "type",
    seed = 0)

bp_34 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(3,4),
    biplot = TRUE,
    extVariables = extVars,
    pDataForColour = "grpId",
    pDataForLabel = "name",
    seed = 0)

Plot of Metric MDS object - Shepard diagram

Description

ggplotSampleMDSShepard uses ggplot2 to provide plot of Metric MDS results. Shepard diagram provides a scatter plot of :

on the x axis, the high dimensional pairwise distances between each sample pairs
on the y axis, the corresponding pairwise distances in the obtained low dimensional projection

Usage

ggplotSampleMDSShepard(
  mdsObj,
  nDim,
  title = "Multi Dimensional Scaling - Shepard's diagram",
  pointSize = 0.5,
  lineWidth = 0.5,
  displayPseudoRSq = TRUE
)
ggplotSampleMDSShepard(
  mdsObj,
  nDim,
  title = "Multi Dimensional Scaling - Shepard's diagram",
  pointSize = 0.5,
  lineWidth = 0.5,
  displayPseudoRSq = TRUE
)

Arguments

`mdsObj`	a MDS object, output of the `computeMetricMDS()` method.
`nDim`	(optional) number of dimensions to use when calculating Shepard's diagram and pseudoRSquare. If missing, it will be set equal to the number of projection dimensions as calculated in `mdsObj`
`title`	title to give to the plot
`pointSize`	point size in plot
`lineWidth`	line width in plot
`displayPseudoRSq`	if TRUE, display pseudo RSquare in subtitle, on top of nb of dimensions

Value

a ggplot object

Examples



library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    
ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)

# Shepard diagrams 

p2D <- ggplotSampleMDSShepard(
    mdsObj,
    nDim = 2,
    pointSize = 1,
    title = "Shepard with 2 dimensions")

p3D <- ggplotSampleMDSShepard(
    mdsObj,
    nDim = 3,
    title = "Shepard with 3 dimensions") 
    #' 
pDefD <- ggplotSampleMDSShepard(
    mdsObj,
    title = "Shepard with default nb of dimensions") 

library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    
ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)

# Shepard diagrams 

p2D <- ggplotSampleMDSShepard(
    mdsObj,
    nDim = 2,
    pointSize = 1,
    title = "Shepard with 2 dimensions")

p3D <- ggplotSampleMDSShepard(
    mdsObj,
    nDim = 3,
    title = "Shepard with 3 dimensions") 
    #' 
pDefD <- ggplotSampleMDSShepard(
    mdsObj,
    title = "Shepard with default nb of dimensions")

SampleMDS biplot wrapping

Description

ggplotSampleMDSWrapBiplots calls ggplotSampleMDS repeatly to generate biplots with different sets of external variables and align them in a grid using the patchwork package, in a similar fashion as ggplot2::facet_wrap() does.

Usage

ggplotSampleMDSWrapBiplots(
  mdsObj,
  extVariableList,
  ncol = NULL,
  nrow = NULL,
  byrow = NULL,
  displayLegend = TRUE,
  ...
)
ggplotSampleMDSWrapBiplots(
  mdsObj,
  extVariableList,
  ncol = NULL,
  nrow = NULL,
  byrow = NULL,
  displayLegend = TRUE,
  ...
)

Arguments

`mdsObj`	a MDS object, output of the `computeMetricMDS()` method
`extVariableList`	should be a named list of external variable matrices Each element of the list should be a matrix with named columns corresponding to the variables. The number of rows should be the same as the number of samples.
`ncol`	passed to `patchwork::wrap_plots()`
`nrow`	passed to `patchwork::wrap_plots()`
`byrow`	passed to `patchwork::wrap_plots()`
`displayLegend`	if FALSE, will de-active the legend display
`...`	additional parameters passed to `ggplotSampleMDS()` (if used)

Value

a ggplot object

Examples



library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    
# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)

# plot mds projection on axes 1 and 2,
# use 'group' for colour, 'type' for shape, and no label 

p_12 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "grpId",
    pDataForShape = "type")

# try to associate axes with median or std deviation of each channel
# => use bi-plots

extVarList <- channelSummaryStats(
    fsAll,
    channels = c("FSC-A", "SSC-A"),
    statFUNs = c("median" = stats::median, 
                 "std.dev" = stats::sd))

bpFull <- ggplotSampleMDSWrapBiplots(
    mdsObj = mdsObj,
    extVariableList = extVarList,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "group",
    pDataForShape = "type",
    seed = 0)

library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    
# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$type <- factor(c("real", "real", rep("synthetic", 3)))
flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))

# calculate all pairwise distances

pwDist <- pairwiseEMDDist(fsAll, 
                             channels = c("FSC-A", "SSC-A"),
                             verbose = FALSE)

# compute Metric MDS object with explicit number of dimensions
mdsObj <- computeMetricMDS(pwDist, nDim = 4, seed = 0)

dim <- nDim(mdsObj) # should be 4

#' # compute Metric MDS object by reaching a target pseudo RSquare
mdsObj2 <- computeMetricMDS(pwDist, seed = 0, targetPseudoRSq = 0.999)

# plot mds projection on axes 1 and 2,
# use 'group' for colour, 'type' for shape, and no label 

p_12 <- ggplotSampleMDS(
    mdsObj = mdsObj,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "grpId",
    pDataForShape = "type")

# try to associate axes with median or std deviation of each channel
# => use bi-plots

extVarList <- channelSummaryStats(
    fsAll,
    channels = c("FSC-A", "SSC-A"),
    statFUNs = c("median" = stats::median, 
                 "std.dev" = stats::sd))

bpFull <- ggplotSampleMDSWrapBiplots(
    mdsObj = mdsObj,
    extVariableList = extVarList,
    pData = flowCore::pData(fsAll),
    projectionAxes = c(1,2),
    pDataForColour = "group",
    pDataForShape = "type",
    seed = 0)

MDS class

Description

Class representing Multi Dimensional Scaling (MDS) projection.

returns the value of the stress criterion, minimized by the SMACOF algorithm.

returns a vector of nPoints dimension, containing the stress indicator per point. The stress minimization criterion can indeed be allocated per represented point. The more the stress of a particular point, the less accurate its distances w.r.t. the other points.

Usage

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

nDim(x)

nPoints(x)

pwDist(x)

projections(x)

projDist(x)

stress(x)

spp(x)

eigenVals(x)

pctvar(x)

RSq(x)

RSqVec(x)

GoF(x)

smacofRes(x)
## S4 method for signature 'MDS'
show(object)

nDim(x)

nPoints(x)

pwDist(x)

projections(x)

projDist(x)

stress(x)

spp(x)

eigenVals(x)

pctvar(x)

RSq(x)

RSqVec(x)

GoF(x)

smacofRes(x)

Arguments

`object`	a `MDS` object
`x`	a `MDS` object

Value

nothing

Slots

nDim

numeric, nb of dimensions of the projection

pwDist

An object of class dist storing the triangular relevant part of the symmetric, zero diagonal pairwise distance matrix (nPoints * nPoints), BEFORE projection.

proj

The projection matrix, resulting from MDS

projDist

An object of class dist storing the triangular relevant part of the symmetric, zero diagonal pairwise distance matrix (nPoints * nPoints), AFTER projection.

eigen

numeric, vector of nDim length, containing the eigen values of the PCA that is applied after the Smacof algorithm.

pctvar

numeric, vector of nDim length, containing the percentage of explained variance per axis.

RSq

numeric, vector of pseudo R square indicators, as a function of number of dimensions. RSq[nDim] is the global pseudo R square, as displayed on plots.

GoF

numeric, vector of goodness of fit indicators, as a function of number of dimensions. GoF[nDim] is the global goodness of fit.

Note pseudo R square and goodness of fit indicators are essentially the same indicator, only the definition of total sum of squares differ:

for pseudo RSq: TSS is calculated using the mean pairwise distance as minimum
for goodness of fit: TSS is calculated using 0 as minimum

smacofRes

an object of class 'smacofB' containing the algorithmic optimization results, for example stress and stress per point, as returned by smacof::smacofSym() method.

Examples



nHD <- 10
nLD <- 2
nPoints <- 20 

# generate uniformly distributed points in 10 dimensions
points <- matrix(
    data = runif(n = nPoints * nHD),
    nrow = nPoints)
    
# calculate euclidian distances     
pwDist  <- dist(points)

# compute Metric MDS object by reaching a target pseudo RSquare
mdsObj <- computeMetricMDS(pwDist, targetPseudoRSq = 0.95)

show(mdsObj)


nHD <- 10
nLD <- 2
nPoints <- 20 

# generate uniformly distributed points in 10 dimensions
points <- matrix(
    data = runif(n = nPoints * nHD),
    nrow = nPoints)
    
# calculate euclidian distances     
pwDist  <- dist(points)

# compute Metric MDS object by reaching a target pseudo RSquare
mdsObj <- computeMetricMDS(pwDist, targetPseudoRSq = 0.95)

show(mdsObj)

Pairwise Earth Mover's Distance calculation

Description

Computation of all EMD between pairs of flowFrames belonging to a flowSet. This method provides three different input modes:

the user provides directly a flowCore::flowSet loaded in memory (RAM).
the user provides directly a list of expression matrices loaded in RAM, of which the column names are the channel/marker names
the user provides (1.) a number of samples nSamples; (2.) an ad-hoc function that takes as input an index between 1 and nSamples, and codes the method to load the corresponding expression matrix in memory; Optional row and column ranges can be provided to limit the calculation to a specific rectangle of the matrix. These i.e. can be specified as a way to split heavy calculations of large distance matrices on several computation nodes.

Usage

pairwiseEMDDist(
  x,
  rowRange = c(1, nSamples),
  colRange = c(min(rowRange), nSamples),
  loadExprMatrixFUN = NULL,
  loadExprMatrixFUNArgs = NULL,
  channels = NULL,
  verbose = FALSE,
  BPPARAM = BiocParallel::SerialParam(),
  BPOPTIONS = BiocParallel::bpoptions(packages = c("flowCore")),
  binSize = 0.05,
  minRange = -10,
  maxRange = 10
)
pairwiseEMDDist(
  x,
  rowRange = c(1, nSamples),
  colRange = c(min(rowRange), nSamples),
  loadExprMatrixFUN = NULL,
  loadExprMatrixFUNArgs = NULL,
  channels = NULL,
  verbose = FALSE,
  BPPARAM = BiocParallel::SerialParam(),
  BPOPTIONS = BiocParallel::bpoptions(packages = c("flowCore")),
  binSize = 0.05,
  minRange = -10,
  maxRange = 10
)

Arguments

`x`	can be: a flowCore::flowSet a list of expression matrices (Double matrix with named columns) the number of samples (integer >=1)
`rowRange`	the range of rows of the distance matrix to be calculated
`colRange`	the range of columns of the distance matrix to be calculated
`loadExprMatrixFUN`	the function used to translate an integer index into an expression matrix. In other words, the function should code how to load the `index`th expression matrix into memory. IMPORTANT: the expression matrix index should be the first function argument and should be named `exprMatrixIndex`.
`loadExprMatrixFUNArgs`	(optional) a named list containing additional input parameters of `loadExprMatrixFUN()`
`channels`	which channels (integer index(ices) or character(s)): if it is a character vector, it can refer to either the channel names, or the marker names if it is a numeric vector, it refers to the indexes of channels in `fs` if NULL all scatter and fluorescent channels of `fs` #' will be selected
`verbose`	if `TRUE`, output a message after each single distance calculation
`BPPARAM`	sets the `BPPARAM` back-end to be used for the computation. If not provided, will use `BiocParallel::SerialParam()` (no task parallelization)
`BPOPTIONS`	sets the BPOPTIONS to be passed to `bplapply()` function. Note that if you use a `SnowParams` back-end, you need to specify all the packages that need to be loaded for the different CytoProcessingStep to work properly (visibility of functions). As a minimum, the `flowCore` package needs to be loaded. (hence the default `BPOPTIONS = bpoptions(packages = c("flowCore"))` )
`binSize`	size of equal bins to approximate the marginal distributions.
`minRange`	minimum value taken when approximating the marginal distributions
`maxRange`	maximum value taken when approximating the marginal distributions

Value

a distance matrix of pairwise distances (full symmetric with 0. diagonal)

Examples


library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples,
    transList)

# calculate pairwise distances using only FSC-A & SSC-A channels
pwDist <- pairwiseEMDDist(
    x = OMIP021Trans,
    channels = c("FSC-A", "SSC-A"))

library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples,
    transList)

# calculate pairwise distances using only FSC-A & SSC-A channels
pwDist <- pairwiseEMDDist(
    x = OMIP021Trans,
    channels = c("FSC-A", "SSC-A"))

Package 'CytoMDS'

Help Index

Summary statistics per channel computation

Description

Usage

Arguments

Value

Examples

metric MDS projection of sample

Description

Usage

Arguments

Value

Examples

Calculate Earth Mover's distance between two samples

Description

Usage

Arguments

Value

Examples

Plot of channel intensity marginal densities

Description

Usage

Arguments

Value

Examples

Plot of Metric MDS object

Description

Usage

Arguments

Value

See Also

Examples

Plot of Metric MDS object - Shepard diagram

Description

Usage

Arguments

Value

See Also

Examples

SampleMDS biplot wrapping

Description

Usage

Arguments

Value

See Also

Examples

MDS class

Description

Usage

Arguments

Value

Slots

Examples

Pairwise Earth Mover's Distance calculation

Description

Usage

Arguments

Value

Examples