Package 'spatialFDA' reference manual

Title:	A Tool for Spatial Multi-sample Comparisons
Description:	spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund.
Authors:	Martin Emons [aut, cre] , Samuel Gunz [aut] , Mark Robinson [aut, fnd]
Maintainer:	Martin Emons <[email protected]>
License:	GPL (>= 3) + file LICENSE
Version:	0.99.12
Built:	2025-02-20 08:29:36 UTC
Source:	https://github.com/bioc/spatialFDA

Convert SpatialExperiment object to ppp object

Description

Convert SpatialExperiment object to ppp object

Usage

.dfToppp(df, marks = NULL, continuous = FALSE, window = NULL)
.dfToppp(df, marks = NULL, continuous = FALSE, window = NULL)

Arguments

`df`	A dataframe with the x and y coordinates from the corresponding SpatialExperiment and the ColData
`marks`	A vector of marks to be associated with the points, has to be either named 'cell_type' if you want to compare discrete celltypes or else continous gene expression measurements are assumed as marks.
`continuous`	A boolean indicating whether the marks are continuous defaults to FALSE
`window`	An observation window of the point pattern of class `owin`.

Value

A ppp object for use with spatstat functions

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
speSub <- subset(spe, , image_number == "138")
dfSub <- .speToDf(speSub)
pp <- .dfToppp(dfSub, marks = "cell_type")

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
speSub <- subset(spe, , image_number == "138")
dfSub <- .speToDf(speSub)
pp <- .dfToppp(dfSub, marks = "cell_type")

Compute a spatial metric on a SpatialExperiment object

Description

A function that takes a SpatialExperiment object and computes a spatial statistics function as implemented in spatstat. The output is a spatstat object.

Usage

.extractMetric(
  df,
  selection,
  fun,
  marks = NULL,
  rSeq = NULL,
  by = NULL,
  continuous = FALSE,
  window = NULL,
  ...
)
.extractMetric(
  df,
  selection,
  fun,
  marks = NULL,
  rSeq = NULL,
  by = NULL,
  continuous = FALSE,
  window = NULL,
  ...
)

Arguments

`df`	A `dataframe` with the x and y coordinates from the corresponding `SpatialExperiment` and the `colData`
`selection`	the mark(s) you want to compare
`fun`	the `spatstat` function to compute on the point pattern object
`marks`	the marks to consider e.g. cell types
`rSeq`	the range of r values to compute the function over
`by`	the spe `colData` variable(s) to add to the meta data
`continuous`	A boolean indicating whether the marks are continuous defaults to FALSE
`window`	a observation window for the point pattern of class `owin`.
`...`	Other parameters passed to `spatstat.explore` functions

Value

a spatstat metric object with the fov number, the number of points and the centroid of the image

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
speSub <- subset(spe, , image_number == "138")
dfSub <- .speToDf(speSub)
metricRes <- .extractMetric(dfSub, c("alpha", "beta"),
    fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 1000, length.out = 100),
    by = c("patient_stage", "patient_id", "image_number")
)
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
speSub <- subset(spe, , image_number == "138")
dfSub <- .speToDf(speSub)
metricRes <- .extractMetric(dfSub, c("alpha", "beta"),
    fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 1000, length.out = 100),
    by = c("patient_stage", "patient_id", "image_number")
)

load Example dataset from Damond et al. (2019)

Description

load Example dataset from Damond et al. (2019)

Usage

.loadExample(full = FALSE)
.loadExample(full = FALSE)

Arguments

full

a boolean indicating whether to load the entire Damond et al. (2019) or only a subset

Value

A SpatialExperiment object as uploaded to ExperimentHub()

Examples

# retrieve the Damond et al. (2019) dataset
spe <- .loadExample()

# retrieve the Damond et al. (2019) dataset
spe <- .loadExample()

Transform a SpatialExperiment into a dataframe

Description

Transform a SpatialExperiment into a dataframe

Usage

.speToDf(spe)
.speToDf(spe)

Arguments

spe

A SpatialExperiment object subset to a single image

Value

A dataframe with the x and y coordinates from the corresponding SpatialExperiment and the colData

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
speSub <- subset(spe, , image_number == "138")
dfSub <- .speToDf(speSub)
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
speSub <- subset(spe, , image_number == "138")
dfSub <- .speToDf(speSub)

Calculate cross spatial metrics for all combinations per FOV

Description

A function that takes a SpatialExperiment object as input and calculates a cross spatial metric as implemented by spatstat per field of view for all combinations provided by the user.

Usage

calcCrossMetricPerFov(
  spe,
  selection,
  subsetby = NULL,
  fun,
  marks = NULL,
  rSeq = NULL,
  by = NULL,
  ncores = 1,
  continuous = FALSE,
  assay = "exprs",
  ...
)
calcCrossMetricPerFov(
  spe,
  selection,
  subsetby = NULL,
  fun,
  marks = NULL,
  rSeq = NULL,
  by = NULL,
  ncores = 1,
  continuous = FALSE,
  assay = "exprs",
  ...
)

Arguments

`spe`	a `SpatialExperiment` object
`selection`	the mark(s) you want to compare
`subsetby`	the spe `colData` variable to subset the data by
`fun`	the `spatstat` function to compute on the point pattern object
`marks`	the marks to consider e.g. cell types
`rSeq`	the range of r values to compute the function over
`by`	the spe `colData` variable(s) to add to the meta data
`ncores`	the number of cores to use for parallel processing, default = 1
`continuous`	A boolean indicating whether the marks are continuous defaults to FALSE
`assay`	the assay which is used if `continuous = TRUE`
`...`	Other parameters passed to spatstat.explore functions

Value

a dataframe of the spatstat metric objects with the radius r, the theoretical value of a Poisson process, the different border corrections the fov number, the number of points and the centroid of the image

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcCrossMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcCrossMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)

Calculate a spatial metric on a `SpatialExperiment` object per field of view

Description

A function that takes a SpatialExperiment object as input and calculates a spatial metric as implemented by spatstat per field of view.

Usage

calcMetricPerFov(
  spe,
  selection,
  subsetby,
  fun,
  marks = NULL,
  rSeq = NULL,
  by = NULL,
  continuous = FALSE,
  assay = "exprs",
  ncores = 1,
  ...
)
calcMetricPerFov(
  spe,
  selection,
  subsetby,
  fun,
  marks = NULL,
  rSeq = NULL,
  by = NULL,
  continuous = FALSE,
  assay = "exprs",
  ncores = 1,
  ...
)

Arguments

`spe`	a `SpatialExperiment` object
`selection`	the mark(s) you want to compare. NOTE: This is directional. c(A,B) is not the same result as c(B,A).
`subsetby`	the spe `colData` variable to subset the data by. This variable has to be provided, even if there is only one sample.
`fun`	the `spatstat` function to compute on the point pattern object
`marks`	the marks to consider e.g. cell types
`rSeq`	the range of r values to compute the function over
`by`	the spe `colData` variable(s) to add to the meta data
`continuous`	A boolean indicating whether the marks are continuous defaults to FALSE
`assay`	the assay which is used if `continuous = TRUE`
`ncores`	the number of cores to use for parallel processing, default = 1
`...`	Other parameters passed to `spatstat.explore` functions

Value

a dataframe of the spatstat metric objects with the radius r, the theoretical value of a Poisson process, the different border corrections the fov number, the number of points and the centroid of the image

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)

General additive model with functional response

Description

A function that takes the output of a metric calculation as done by calcMetricPerFov. The data has to be prepared into the correct format for the functional analysis by the prepData function. The output is a pffr object as implemented by refund.

Usage

functionalGam(data, x, designmat, weights, formula, family = "gaussian", ...)
functionalGam(data, x, designmat, weights, formula, family = "gaussian", ...)

Arguments

`data`	a dataframe with the following columns: Y = functional response; sample_id = sample ID; image_id = image ID;
`x`	the x-axis values of the functional response
`designmat`	a design matrix as defined by model.matrix()
`weights`	weights as the number of points per image. These weights are normalised by the mean of the weights in the fitting process
`formula`	the formula for the model. The colnames of the designmatrix have to correspond to the variables in the formula.
`family`	the distributional family as implemented in `family.mgcv`. For fast computation the default is set to `gaussian`. If the covariance scales e.g. as a function of the domain, this estimation can be improved with `gaulss` - for more information see `family.mgcv`.
`...`	Other parameters passed to `pffr`

Value

a fitted pffr object which inherits from gam

Examples

# load the pancreas dataset
library("tidyr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)
# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()

# create meta info of the IDs
splitData <- strsplit(dat$ID, "|", fixed = TRUE)
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# create a designmatrix
condition <- dat$condition
# relevel the condition - can set explicit contrasts here
condition <- relevel(condition, "Non-diabetic")
designmat <- model.matrix(~condition)
# colnames don't work with the '-' sign
colnames(designmat) <- c(
    "Intercept", "conditionLong_duration",
    "conditionOnset"
)
# fit the model
mdl <- functionalGam(
    data = dat, x = metricRes$r |> unique(),
    designmat = designmat, weights = dat$npoints,
    formula = formula(Y ~ conditionLong_duration +
        conditionOnset + s(patient_id, bs = "re"))
)
summary(mdl)
# load the pancreas dataset
library("tidyr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)
# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()

# create meta info of the IDs
splitData <- strsplit(dat$ID, "|", fixed = TRUE)
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# create a designmatrix
condition <- dat$condition
# relevel the condition - can set explicit contrasts here
condition <- relevel(condition, "Non-diabetic")
designmat <- model.matrix(~condition)
# colnames don't work with the '-' sign
colnames(designmat) <- c(
    "Intercept", "conditionLong_duration",
    "conditionOnset"
)
# fit the model
mdl <- functionalGam(
    data = dat, x = metricRes$r |> unique(),
    designmat = designmat, weights = dat$npoints,
    formula = formula(Y ~ conditionLong_duration +
        conditionOnset + s(patient_id, bs = "re"))
)
summary(mdl)

Functional Principal Component Analysis

Description

A function that takes as input the output of calcMetricPerFov which has to be converted into the correct format by prepData. The output is a list with the fpca.face output from refund.

Usage

functionalPCA(data, r, ...)
functionalPCA(data, r, ...)

Arguments

`data`	a data object for functional data analysis containing at least the functional response $Y$.
`r`	the functional domain
`...`	Other parameters passed to `fpca.sc` functions

Value

a list with components of fpca.sc

Examples

# load the pancreas dataset
library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)
# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()
# create meta info of the IDs
splitData <- str_split(dat$ID, "x")
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# calculate fPCA
mdl <- functionalPCA(
    data = dat, r = metricRes$r |> unique()
)
# load the pancreas dataset
library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)
# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()
# create meta info of the IDs
splitData <- str_split(dat$ID, "x")
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# calculate fPCA
mdl <- functionalPCA(
    data = dat, r = metricRes$r |> unique()
)

Creates a nXn plot of the cross metrics per sample

Description

Helper function for plotCrossMetricPerFov. It applies plotMetricPerFov to all n marks defined in the variable selection. This gives an nxn plot of all marks.

Usage

plotCrossFOV(
  subFov,
  theo,
  correction,
  x,
  imageId,
  ID = NULL,
  ncol = NULL,
  nrow = NULL,
  legend.position = "none",
  ...
)
plotCrossFOV(
  subFov,
  theo,
  correction,
  x,
  imageId,
  ID = NULL,
  ncol = NULL,
  nrow = NULL,
  legend.position = "none",
  ...
)

Arguments

`subFov`	a subset of the `dataframe` to the respective fov
`theo`	logical; if the theoretical line should be plotted
`correction`	the border correction to plot
`x`	the x-axis variable to plot
`imageId`	the ID of the image/fov
`ID`	the (optional) ID for plotting combinations
`ncol`	the number of columns for the facet wrap
`nrow`	the number of rows for the facet wrap
`legend.position`	the position of the legend of the plot
`...`	Other parameters passed to `ggplot2` functions

Value

a ggplot object

Plot a cross type spatial metric per field of view

Description

This function plots the cross function between two marks output from calcMetricPerFov. It wraps around helper function and applies this function to all samples.

Usage

plotCrossMetricPerFov(
  metricDf,
  theo = NULL,
  correction = NULL,
  x = NULL,
  imageId = NULL,
  ID = NULL,
  nrow = NULL,
  ncol = NULL,
  legend.position = "none",
  ...
)
plotCrossMetricPerFov(
  metricDf,
  theo = NULL,
  correction = NULL,
  x = NULL,
  imageId = NULL,
  ID = NULL,
  nrow = NULL,
  ncol = NULL,
  legend.position = "none",
  ...
)

Arguments

`metricDf`	the metric dataframe as calculated by `calcMetricPerFov`
`theo`	logical; if the theoretical line should be plotted
`correction`	the border correction to plot
`x`	the x-axis variable to plot
`imageId`	the ID of the image/fov
`ID`	the (optional) ID for plotting combinations
`nrow`	the number of rows for the facet wrap
`ncol`	the number of columns for the facet wrap
`legend.position`	the position of the legend of the plot
`...`	Other parameters passed to `ggplot2` functions

Value

a ggplot object

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcCrossMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)

metricRes$ID <- paste0(
 metricRes$patient_stage, "|", metricRes$patient_id
)

metricRes <- subset(metricRes, image_number %in% c(138, 139, 140))
p <- plotCrossMetricPerFov(metricRes,
    theo = TRUE, correction = "rs",
    x = "r", imageId = "image_number", ID = "ID"
)
print(p)
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcCrossMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)

metricRes$ID <- paste0(
 metricRes$patient_stage, "|", metricRes$patient_id
)

metricRes <- subset(metricRes, image_number %in% c(138, 139, 140))
p <- plotCrossMetricPerFov(metricRes,
    theo = TRUE, correction = "rs",
    x = "r", imageId = "image_number", ID = "ID"
)
print(p)

Functional boxplot of spatstat curves

Description

This function creates a functional boxplot of the spatial statistics curves. It creates one functional boxplot per aggregation category, e.g. condition.

Usage

plotFbPlot(metricDf, x, y, aggregateBy)
plotFbPlot(metricDf, x, y, aggregateBy)

Arguments

`metricDf`	the metric dataframe as calculated by `calcMetricPerFov`
`x`	the name of the x-axis of the spatial metric
`y`	the name of the y-axis of the spatial metric
`aggregateBy`	the criterion by which to aggregate the curves into a functional boxplot. Can be e.g. the condition of the different samples.

Value

a list of base R plots

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)
# create a unique ID for the data preparation
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)

plotFbPlot(metricRes, 'r', 'rs', 'patient_stage')
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)
# create a unique ID for the data preparation
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)

plotFbPlot(metricRes, 'r', 'rs', 'patient_stage')

Plot a biplot from an fPCA analysis

Description

A function that takes the output from the functionalPCA function and returns a ggplot object of the first two dimensions of the PCA as biplot.

Usage

plotFpca(data, res, colourby = NULL, labelby = NULL)
plotFpca(data, res, colourby = NULL, labelby = NULL)

Arguments

`data`	a data object for functional data analysis containing at least the functional response $Y$.
`res`	the output from the fPCA calculation
`colourby`	the variable by which to colour the PCA plot by
`labelby`	the variable by which to label the PCA plot by

Value

a list with components of fpca.face

Examples

# load the pancreas dataset
library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)

# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()
# create meta info of the IDs
splitData <- str_split(dat$ID, "|")
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# calculate fPCA
mdl <- functionalPCA(
    data = dat, r = metricRes$r |> unique()
)
p <- plotFpca(
    data = dat, res = mdl, colourby = "condition",
    labelby = "patient_id"
)
print(p)
# load the pancreas dataset
library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)

# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()
# create meta info of the IDs
splitData <- str_split(dat$ID, "|")
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# calculate fPCA
mdl <- functionalPCA(
    data = dat, r = metricRes$r |> unique()
)
p <- plotFpca(
    data = dat, res = mdl, colourby = "condition",
    labelby = "patient_id"
)
print(p)

Plot a pffr model object

Description

A function that takes a pffr object as calculated in functionalGam and plots the functional coefficients. The functions are centered such that their expected value is zero. Therefore, the scalar intercept has to be added to the output with the argument shift in order to plot the coefficients in their original range.

Usage

plotMdl(mdl, predictor, shift = NULL)
plotMdl(mdl, predictor, shift = NULL)

Arguments

`mdl`	a `pffr` model object
`predictor`	predictor to plot
`shift`	the value by which to shift the centered functional intercept. this will most often be the constant intercept

Value

ggplot object of the functional estimate

Examples

library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)
# create a unique ID for each row
metricRes$ID <- paste0(
    metricRes$patient_stage, "x", metricRes$patient_id,
    "x", metricRes$image_number
)

dat <- prepData(metricRes, "r", "rs")

# create meta info of the IDs
splitData <- str_split(dat$ID, "x")
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# create a designmatrix
condition <- dat$condition
# relevel the condition - can set explicit contrasts here
condition <- relevel(condition, "Non-diabetic")
designmat <- model.matrix(~condition)
# colnames don't work with the '-' sign
colnames(designmat) <- c(
    "Intercept", "conditionLong_duration",
    "conditionOnset"
)
# fit the model
mdl <- functionalGam(
    data = dat, x = metricRes$r |> unique(),
    designmat = designmat, weights = dat$npoints,
    formula = formula(Y ~ conditionLong_duration +
        conditionOnset + s(patient_id, bs = "re"))
)
summary(mdl)
plotLs <- lapply(colnames(designmat), plotMdl,
    mdl = mdl,
    shift = mdl$coefficients[["(Intercept)"]]
)
library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)
# create a unique ID for each row
metricRes$ID <- paste0(
    metricRes$patient_stage, "x", metricRes$patient_id,
    "x", metricRes$image_number
)

dat <- prepData(metricRes, "r", "rs")

# create meta info of the IDs
splitData <- str_split(dat$ID, "x")
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# create a designmatrix
condition <- dat$condition
# relevel the condition - can set explicit contrasts here
condition <- relevel(condition, "Non-diabetic")
designmat <- model.matrix(~condition)
# colnames don't work with the '-' sign
colnames(designmat) <- c(
    "Intercept", "conditionLong_duration",
    "conditionOnset"
)
# fit the model
mdl <- functionalGam(
    data = dat, x = metricRes$r |> unique(),
    designmat = designmat, weights = dat$npoints,
    formula = formula(Y ~ conditionLong_duration +
        conditionOnset + s(patient_id, bs = "re"))
)
summary(mdl)
plotLs <- lapply(colnames(designmat), plotMdl,
    mdl = mdl,
    shift = mdl$coefficients[["(Intercept)"]]
)

Plot a spatial metric per field of view

Description

A function that plots the output of the function calcMetricPerFov. The plot contains one curve per FOV and makes subplots by samples.

Usage

plotMetricPerFov(
  metricDf,
  theo = FALSE,
  correction = NULL,
  x = NULL,
  imageId = NULL,
  ID = NULL,
  nrow = NULL,
  ncol = NULL,
  legend.position = "none",
  ...
)
plotMetricPerFov(
  metricDf,
  theo = FALSE,
  correction = NULL,
  x = NULL,
  imageId = NULL,
  ID = NULL,
  nrow = NULL,
  ncol = NULL,
  legend.position = "none",
  ...
)

Arguments

`metricDf`	the metric `dataframe` as calculated by `calcMetricPerFov`
`theo`	logical; if the theoretical line should be plotted
`correction`	the border correction to plot
`x`	the x-axis variable to plot
`imageId`	the ID of the image/fov
`ID`	the (optional) ID for plotting combinations
`nrow`	the number of rows for the facet wrap
`ncol`	the number of columns for the facet wrap
`legend.position`	the position of the legend of the plot
`...`	Other parameters passed to `ggplot2` functions

Value

a ggplot object

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)
# ceate a unique plotting ID
metricRes$ID <- paste0(
 metricRes$patient_stage, "|", metricRes$patient_id
)

p <- plotMetricPerFov(metricRes,
    correction = "rs", x = "r",
    imageId = "image_number", ID = "ID"
)
print(p)
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)
# ceate a unique plotting ID
metricRes$ID <- paste0(
 metricRes$patient_stage, "|", metricRes$patient_id
)

p <- plotMetricPerFov(metricRes,
    correction = "rs", x = "r",
    imageId = "image_number", ID = "ID"
)
print(p)

Prepare data from calcMetricRes to be in the right format for FDA

Description

Prepare data from calcMetricRes to be in the right format for FDA

Usage

prepData(metricRes, x, y)
prepData(metricRes, x, y)

Arguments

`metricRes`	a dataframe as calculated by calcMetricRes - requires the columns ID (unique identifier of each row)
`x`	the name of the x-axis of the spatial metric
`y`	the name of the y-axis of the spatial metric

Value

returns a list with three entries, the unique ID, the functional response Y and the weights

Examples

# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)

# create a unique ID for each row
metricRes$ID <- paste0(
    metricRes$patient_stage, "|", metricRes$patient_id,
    "|", metricRes$image_number
)
dat <- prepData(metricRes, "r", "rs")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross", marks = "cell_type",
    rSeq = seq(0, 50, length.out = 50), by = c(
        "patient_stage", "patient_id",
        "image_number"
    ),
    ncores = 1
)

# create a unique ID for each row
metricRes$ID <- paste0(
    metricRes$patient_stage, "|", metricRes$patient_id,
    "|", metricRes$image_number
)
dat <- prepData(metricRes, "r", "rs")

print the fPCA results

Description

this is a function that prints a summary of the fPCA result of class fpca

Usage

## S3 method for class 'fpca'
print(x, ...)
## S3 method for class 'fpca'
print(x, ...)

Arguments

`x`	the result of function `functionalPCA`
`...`	other parameters passed to base generic function `print`

Value

a formatted overview of the fPCA result

Examples

# load the pancreas dataset
library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)
# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()

# create meta info of the IDs
splitData <- strsplit(dat$ID, "|", fixed = TRUE)
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# calculate fPCA
mdl <- functionalPCA(
    data = dat, r = metricRes$r |> unique()
)
mdl
# load the pancreas dataset
library("tidyr")
library("stringr")
library("dplyr")
# retrieve example data from Damond et al. (2019)
spe <- .loadExample()
# calculate the Gcross metric for alpha and beta cells
metricRes <- calcMetricPerFov(spe, c("alpha", "beta"),
    subsetby = "image_number", fun = "Gcross",
    marks = "cell_type", rSeq = seq(0, 50, length.out = 50),
    c("patient_stage", "patient_id", "image_number"), ncores = 1
)
metricRes$ID <- paste0(
  metricRes$patient_stage, "|", metricRes$patient_id,
  "|", metricRes$image_number
)
# prepare data for FDA
dat <- prepData(metricRes, "r", "rs")

# drop rows with NA
dat <- dat |> drop_na()

# create meta info of the IDs
splitData <- strsplit(dat$ID, "|", fixed = TRUE)
dat$condition <- factor(sapply(splitData, function(x) x[1]))
dat$patient_id <- factor(sapply(splitData, function(x) x[2]))
dat$image_id <- factor(sapply(splitData, function(x) x[3]))
# calculate fPCA
mdl <- functionalPCA(
    data = dat, r = metricRes$r |> unique()
)
mdl

Package 'spatialFDA'

Help Index

Convert SpatialExperiment object to ppp object

Description

Usage

Arguments

Value

Examples

Compute a spatial metric on a SpatialExperiment object

Description

Usage

Arguments

Value

Examples

load Example dataset from Damond et al. (2019)

Description

Usage

Arguments

Value

Examples

Transform a SpatialExperiment into a dataframe

Description

Usage

Arguments

Value

Examples

Calculate cross spatial metrics for all combinations per FOV

Description

Usage

Arguments

Value

Examples

Calculate a spatial metric on a SpatialExperiment object per field of view

Description

Usage

Arguments

Value

Examples

General additive model with functional response

Description

Usage

Arguments

Value

Examples

Functional Principal Component Analysis

Description

Usage

Arguments

Value

Examples

Creates a nXn plot of the cross metrics per sample

Description

Usage

Arguments

Value

Plot a cross type spatial metric per field of view

Description

Usage

Arguments

Value

Examples

Functional boxplot of spatstat curves

Description

Usage

Arguments

Value

Examples

Plot a biplot from an fPCA analysis

Description

Usage

Arguments

Value

Examples

Plot a pffr model object

Description

Usage

Arguments

Value

Examples

Plot a spatial metric per field of view

Calculate a spatial metric on a `SpatialExperiment` object per field of view