Package 'beadplexr'

Description

Approximates the adjust argument to stats::density() needed to find the required number of clusters.

Usage

approx_adjust(.x, .k, .lower = 0.4, .upper = 2, .step = 0.2)

Arguments

Details

This function finds the first value of the stats::density() adjust argument which gives the .k number of clusters. It it quite crude in that every value of adjust from .lower to .upper is tested until the desired number of clusters is found. A cluster is defined by a peak, and should no suitable adjust value be found, NA is returned.

Value

A numeric.

Examples

set.seed(1234)
.x <- c(rnorm(100, 2, 1), rnorm(100, 9, 1))
approx_adjust(.x, 2)

Description

A well structured list, such at those loaded by load_panel(), is cast to a data.frame.

Usage

as_data_frame_analyte(
  .analytes,
  .id_bead = "Bead group",
  .id_analyte = "Analyte ID"
)

Arguments

Details

Each analyte in the list passed to the function is expected to be a named list with named elements name and concentration. The name of the list with the analyte specific information is the analyte ID.

Because of the particular setup of the LEGENDplex assay with two bead groups, the analytes are expected to be wrapped in another list.

Value

A data-frame

Examples

.analytes <-  list(A = list(
                A1 = list(name = "name_a1", concentration = 500),
                A2 = list(name = "name_a2", concentration = 50000)))

as_data_frame_analyte(.analytes)

Description

The mean fluorescence intensity (MFI) of the analyte

Usage

calc_analyte_mfi(
  df,
  .parameter,
  .column_name = "analyte",
  .mean_fun = c("geometric", "harmonic", "arithmetic"),
  .data = NULL
)

Arguments

Value

A summarized data.frame

Mean calculation

The calculation of the harmonic mean is as follows:

$n\frac{1}{\sum_{i=1}^n \frac{1}{x_i}}$

NAs are removed before calculation

The geometric mean is given by:

$\left(\prod_{i=1}^n x_i \right)^\frac{1}{n}$

but implemented as:

$\frac{1}{n}\exp{\sum_{i=1}^n\log(x_i)}$

NAs are removed before calculation

Examples

library(beadplexr)
library(dplyr)

data("lplex")

df <- lplex[[1]] |>
  filter(`FSC-A` > 4e5L, `FSC-A` < 6.3e5L) |>
  identify_analyte(.parameter = "FL6-H",
                   .analyte_id = as.character(c(1:7)))

df |>
  calc_analyte_mfi(.parameter = "FL2-H")

df |>
  calc_analyte_mfi(.parameter = "FL2-H",
              .mean_fun = "harmonic")

Description

Given a start concentration and dilution factor, the concentration of the given standard samples is calculated

Usage

calc_std_conc(.standard_sample, .start_concentration, .dilution_factor = 4L)

Arguments

Details

In the manuals to the LEGENDplex system, standard are labeled 0 to 8, where 8 indicate the highest concentration and 0 the background (no analyte). The standard is diluted at 1:4 so that

[s7] = [start]
[s6] = [s7]/4
[s5] = [s6]/4
[s4] = [s5]/4
[s3] = [s4]/4
[s2] = [s3]/4
[s1] = [s2]/4
[s0] = 0

It might happen, that a dilution step is missing in which case the dilution is corrected to accommodate the missing step. However, since it is inspired guess work and out of the ordinary, a warning is thrown, see Examples.

Value

A numeric vector

Standard sample order

If the vector is numeric, the values are ordered numerically from high to low.

If the vector is not numeric, things become a little more difficult, because sorting a vector like c("a", "c", "0", "b") by default results in c("0", "a", "b", "c"), which means that '0' is the highest value and will be assigned the start concentration and the sample 'a' is then the first dilution.

To avoid this problem, the vector is split into two: one containing numerical values and one containing alphabetical. Each vector is then sorted appropriately and combined, see Examples.

Examples

calc_std_conc(.standard_sample = c(7:0),
                                 .start_concentration = 5000)

suppressWarnings(
# Sample 5 is missing - raises a warning
calc_std_conc(.standard_sample = c(7, 6, 4, 3, 2, 1, 0),
                                 .start_concentration = 5000)
)

calc_std_conc(.standard_sample = rep(c(7:0), 2),
                                 .start_concentration = 5000)
calc_std_conc(.standard_sample = c(9:0),
                                .start_concentration = 5000)

calc_std_conc(.standard_sample = c(letters[1:7], 0),
                                 .start_concentration = 5000)
calc_std_conc(.standard_sample = c(letters[1:7], 0, 1),
                                 .start_concentration = 5000)

calc_std_conc(.standard_sample = c(7:1, 0),
                                 .start_concentration = 5000,
                                 .dilution_factor = c(1, 2, 2, 2, 4, 6, 6, 0))

# If 0 exists it is always set to 0
calc_std_conc(.standard_sample = c(7:1, 0),
                                 .start_concentration = 5000,
                                 .dilution_factor = c(1, 2, 2, 2, 4, 6, 6, 100000))
calc_std_conc(.standard_sample = c(8:1),
                                 .start_concentration = 5000,
                                 .dilution_factor = c(1, 2, 2, 2, 4, 6, 6, 100000))

Description

Calculate the concentration in a sample

Usage

calculate_concentration(
  df,
  .model,
  .parameter = "FL2.H",
  .value = "Calc.conc",
  .data = NULL
)

Arguments

Value

The df with the calculated concentration and error added in two columns.

Examples

library(beadplexr)
library(drc)
data(ryegrass)

ryegrass_m <-
  fit_standard_curve(df = ryegrass,
                     .parameter = "rootl",
                     .concentration = "conc")

sample_data <-
  calculate_concentration(df = ryegrass[sample(1:nrow(ryegrass), 5),],
                          .model = ryegrass_m,
                          .parameter = "rootl")

Description

Cluster identification with various algorithms and subsequent trimming of each cluster

Usage

bp_kmeans(df, .parameter, .column_name, .k, .trim = 0, .data = NULL, ...)

bp_clara(df, .parameter, .column_name, .k, .trim = 0, .data = NULL, ...)

bp_dbscan(
  df,
  .parameter,
  .column_name,
  .eps = 0.2,
  .MinPts = 50,
  .data = NULL,
  ...
)

bp_mclust(
  df,
  .parameter,
  .column_name,
  .k,
  .trim = 0,
  .sample_frac = 0.05,
  .max_subset = 500,
  .data = NULL,
  ...
)

bp_density_cut(df, .parameter, .column_name, .k, .trim = 0, .data = NULL, ...)

Arguments

Value

The data.frame in df with the cluster classification added in the column given by .column_name.

Additional parameters

Information on additional arguments passed, can be found here:

clara

cluster::clara()

kmeans

kmeans()

dbscan

fpc::dbscan()

mclust

mclust::Mclust()

density_cut

approx_adjust()

Default parameters to clara()

cluster::clara() is by default called with the following parameters:

samples

100

pamLike

TRUE

Parameters to dbscan

It requires some trial and error to get the right parameters for the density based clustering, but the parameters usually stay stable throughout an entire experiment and over time (assuming that there is only little drift in the flow cytometer). There is no guarantee that the correct number of clusters are returned, and it might be better to use this on the forward - side scatter discrimination.

Scaling of the parameters seems to be appropriate in most cases for the forward - side scatter discrimination and is automatically performed.

Parameters to mclust

Mclust is is slow and memory hungry on large datasets. Using a subset of the data to initialise the clustering greatly improves the speed. I have found that a subset sample of 500 even works well and gives no markedly better clustering than a subset of 5000 events, but initialisation with 500 makes the clustering complete about 12 times faster than with 5000 events.

Parameters to density_cut

This simple function works by smoothing a density function until the desired number of clusters are found. The segregation of the clusters follows at the lowest point between two clusters.

See Also

trim_population(), identify_analyte().

Mclust and dbscan seems to do an excellent job at separating on the forward and side scatter parameters. Mclust and clara both perform well separating beads in the APC channel, but clara is about 3 times faster than Mclust.

Examples

library(beadplexr)
library(dplyr)
library(ggplot2)

data("lplex")

lplex[[1]] |>
  # Speed things up a bit by selecting one fourth of the events.
  # Probably not something you'd usually do
  dplyr::sample_frac(0.25) |>
  bp_kmeans(.parameter = c("FSC-A", "SSC-A"),
            .column_name = "population", .trim = 0.1, .k = 2) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()

library(beadplexr)
library(dplyr)
library(ggplot2)

data("lplex")

lplex[[1]] |>
  # Speed things up a bit by selecting one fourth of the events.
  # Probably not something you'd usually do
  dplyr::sample_frac(0.25) |>
  bp_clara(.parameter = c("FSC-A", "SSC-A"),
           .column_name = "population", .trim = 0.1, .k = 2) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()

lplex[[1]] |>
  # Speed things up a bit by selecting one fourth of the events.
  # Probably not something you'd usually do
  dplyr::sample_frac(0.25) |>
  bp_clara(.parameter = c("FSC-A", "SSC-A"),
           .column_name = "population", .trim = 0, .k = 2) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()

## Not run: 
library(beadplexr)
library(dplyr)
library(ggplot2)

data("lplex")

lplex[[1]] |>
  # Speed things up a bit by selecting one fourth of the events.
  # Probably not something you'd usually do
  dplyr::sample_frac(0.25) |>
  bp_dbscan(.parameter = c("FSC-A", "SSC-A"), .column_name = "population",
            eps = 0.2, MinPts = 50) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()

pop1 <- lplex[[1]] |>
  # Speed things up a bit by selecting one fourth of the events.
  # Probably not something you'd usually do
  dplyr::sample_frac(0.25) |>
  bp_dbscan(.parameter = c("FSC-A", "SSC-A"), .column_name = "population",
    eps = 0.2, MinPts = 50) |>
  dplyr::filter(population == "1")

pop1 |>
  bp_dbscan(.parameter = c("FL6-H", "FL2-H"), .column_name = "population",
    eps = 0.2, MinPts = 50) |>
  pull(population) |>
  unique()

pop1 |>
  bp_dbscan(.parameter = c("FL6-H", "FL2-H"), .column_name = "population",
    eps = 0.2, MinPts = 50, scale = FALSE) |>
  pull(population) |>
  unique()

## End(Not run)
library(beadplexr)
library(ggplot2)

data("lplex")

lplex[[1]] |>
  bp_mclust(.parameter = c("FSC-A", "SSC-A"),
           .column_name = "population", .trim = 0, .k = 2) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()
library(beadplexr)
library(ggplot2)

data("lplex")

lplex[[1]] |>
  bp_density_cut(.parameter = c("FSC-A"),
           .column_name = "population", .trim = 0, .k = 2) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()

Description

Remove lonely, noisy data points in a 2D scatter matrix

Usage

despeckle(df, .parameters, .bins = 256L, .neighbours = 4L, .data = NULL, ...)

Arguments

Details

The values of the two parameters are binned into the given number of bins. They are then cast into a 2D matrix, with the bins of the first of the parameters ending up as rows, the bins of the second parameter as columns, and combinations are marked by 1.

The rows of the df where lonely points are found in .parameters are removed.

Value

A data.frame with noisy points removed.

Examples

library(beadplexr)
library(ggplot2)

data("lplex")

lplex[[1]] |>
  ggplot() +
  aes(x = `FL6-H`, y = `FL2-H`) +
  geom_point()

lplex[[1]] |>
  despeckle(.parameters = c("FL6-H", "FL2-H"), .neighbours = 8) |>
  ggplot() +
  aes(x = `FL6-H`, y = `FL2-H`) +
  geom_point()

lplex[[1]] |>
  despeckle(.parameters = c("FL6-H", "FL2-H"), .bin = 128) |>
  ggplot() +
  aes(x = `FL6-H`, y = `FL2-H`) +
  geom_point()

Description

Chebyshev distance

Usage

dist_chebyshev(x, diag = FALSE, upper = FALSE)

Arguments

Value

Chebyshev distance returns an object of class "dist".

Examples

x <- matrix(rnorm(100), nrow = 5)
dist_chebyshev(x)

x <- matrix(
c(1, 1, 0, 0,
  1, 0, 0, 0,
  0, 1, 1 ,1,
  0, 1, 1, 1),
nrow = 4,
ncol = 4,
byrow = TRUE
)
dist_chebyshev(x, diag = TRUE, upper = TRUE)

Description

Wrappers around building a ggplot with geom_point, geom_density_2d, and geom_hex.

Usage

facs_plot(
  df,
  .x = "FSC-A",
  .y = "SSC-A",
  .type = c("scatter", "density1d", "density2d", "hexbin"),
  .data = NULL,
  ...
)

facs_scatter(
  df,
  .x = "FSC-A",
  .y = "SSC-A",
  .beads = NULL,
  .plot_distinct = TRUE,
  .data = NULL
)

facs_density2d(df, .x = "FSC-A", .y = "SSC-A", .beads = NULL, .data = NULL)

facs_density1d(df, .x = "FSC-A", .beads = NULL, .data = NULL)

facs_hexbin(df, .x = "FSC-A", .y = "SSC-A", .bins = 75, .data = NULL)

Arguments

Details

These plot functions are meant to provide a quick way of viewing the FACS data. For more control, use ggplot2 directly.

Value

A ggplot

Examples

## Not run: 
library(beadplexr)
data("lplex")

df <-  lplex[[1]]
df$bead_group <- ifelse(df$`FSC-A` < 4e5L, "A", "B")

# Using facs_plot
facs_plot(df, .type = "scatter")
facs_plot(df, .type = "density1d")
facs_plot(df, .type = "density2d")
facs_plot(df, .type = "hexbin")

facs_plot(df, .type = "scatter", .beads = "bead_group")
facs_plot(df, .type = "density1d", .beads = "bead_group")
facs_plot(df, .type = "hexbin", .bins = 50)

facs_plot(df, .x = "FL2-H", .type = "scatter", .beads = "bead_group")

# Individual functions
facs_scatter(df)

facs_scatter(df, .beads = "bead_group", .plot_distinct = FALSE)
facs_scatter(df, .beads = "bead_group")

facs_scatter(df, .x = "FL2-H", .y = "FL6-H", .beads = "bead_group")

facs_density1d(df)
facs_density1d(df, .beads = "bead_group")

facs_density2d(df)
facs_density2d(df, .beads = "bead_group")

facs_hexbin(df)
facs_hexbin(df, .bins = 30)

## End(Not run)

Description

Fit a logistic function to the standard concentrations.

Usage

fit_standard_curve(
  df,
  .parameter = "FL2.H",
  .concentration = "Concentration",
  .fct = "LL.5",
  .data = NULL,
  ...
)

Arguments

Value

An object of class drc

Examples

library(beadplexr)
library(drc)
data(ryegrass)

ryegrass_m <-
  fit_standard_curve(df = ryegrass,
                     .parameter = "rootl",
                     .concentration = "conc")

summary(ryegrass_m)

Description

Identify analyte

Usage

identify_analyte(
  df,
  .parameter,
  .analyte_id,
  .column_name = "analyte",
  .k = length(.analyte_id),
  .trim = 0,
  .desc = FALSE,
  .method = c("clara", "kmeans", "dbscan", "mclust", "density_cut"),
  .data = NULL,
  ...
)

Arguments

Details

This function is a wrapper around the process of:

• Finding analyte clusters

• Trimming the clusters by removing the cluster members most distant from the cluster center

• Sorting the analyte clusters based on their centers

• Giving each analyte cluster a useful name

Value

A data.frame with analyte IDs in a separate column

Additional parameters

Information on additional arguments passed, can be found here:

clara

cluster::clara()

kmeans

kmeans()

dbscan

fpc::dbscan()

mclust

mclust::Mclust()

density_cut

approx_adjust()

See Also

cluster_events()

Examples

## Not run: 
library(beadplexr)
library(ggplot2)

data("lplex")

df <- lplex[[1]]
df |>
  identify_analyte(.parameter = c("FSC-A", "SSC-A"),
                      .analyte_id = c("A", "B"),
                      .column_name = "analyte",
                      .method = "clara", .trim = 0.02) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = analyte) +
  geom_point()

df |>
  identify_analyte(.parameter = c("FSC-A", "SSC-A"),
                      .analyte_id = c("A", "B"),
                      .column_name = "analyte",
                      .method = "clara", .desc = TRUE) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = analyte) +
  geom_point()

df |>
  identify_analyte(.parameter = c("FSC-A", "SSC-A"),
                      .analyte_id = c("A", "B"),
                      .column_name = "analyte",
                      .method = "dbscan") |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = analyte) +
  geom_point()

## End(Not run)

Description

Convenience functions to identify analytes in different multiplex systems.

Usage

identify_legendplex_analyte(df, .analytes, .method_args, .data = NULL)

identify_cba_analyte(
  df,
  .analytes,
  .method_args,
  .trim_fs = NULL,
  .parameter_fs = NULL,
  .data = NULL
)

identify_macsplex_analyte(
  df,
  .analytes,
  .method_args,
  .trim_fs = NULL,
  .parameter_fs = NULL,
  .data = NULL
)

Arguments

Details

These functions wraps around the process of:

• Trim or subset on forward side scatter

• Identifying analytes. For LEGENDplex in both bead groups

If the forward side scatter events are not trimmed, the function is equivalent to call identify_analyte() with CBA or MACSPlex data.

Value

A data.frame

Analytes

The parameter .analytes is either a simple vector with the IDs or, in the case of the LEGENDplex system, a list giving the IDs of analytes among the groups A and B.

A list for the LEGENDplex system might look like this:

  list(A = c("A1", "A2"),
       B = c("B1", "B2"))

The order of analyte IDs is important and must match the expected order of analytes.

Method arguments

The parameter .method_args is a list of key-value pairs passed to identify_analyte().

Examples

## Not run: 
library(beadplexr)
library(dplyr)
data("lplex")
df <- lplex[[1]]

panel_info <- load_panel(.panel_name = "Human Growth Factor Panel (13-plex)")

args_ident_analyte <- list(fs = list(.parameter = c("FSC-A", "SSC-A"),
                                     .column_name = "Bead group",
                                     .trim = 0.1,
                                     .method = "clara"),
                           analytes = list(.parameter = "FL6-H",
                                           .column_name = "Analyte ID",
                                           .trim = 0,
                                           .method = "clara"))

annot_events <- identify_legendplex_analyte(df = df,
                                            .analytes = panel_info$analytes,
                                            .method_args = args_ident_analyte)

annot_events |> facs_plot(.beads = "Bead group")

annot_events |>
  filter(`Bead group` == "A") |>
  facs_plot(.x = "FL2-H", .y = "FL6-H", .beads = "Analyte ID")

annot_events |>
  filter(`Bead group` == "B") |>
  facs_plot(.x = "FL2-H", .y = "FL6-H", .beads = "Analyte ID")

## End(Not run)
## Not run: 
library(beadplexr)
data(simplex)

df <- simplex[["cba"]]

analytes <- vector("list", 30) |> setNames(as.character(c(1:30)))

args_ident_analyte <- list(.parameter = c("APC", "APC-Cy7"),
                           .column_name = "Analyte ID",
                           .trim = 0.1,
                           .method = "clara")
annot_events <- identify_cba_analyte(df = df,
                     .analytes = analytes,
                     .method_args = args_ident_analyte)

annot_events |> facs_plot(.x = "FSC", .y = "SSC")

annot_events |>
  facs_plot(.x = "APC", .y = "APC-Cy7", .beads = "Analyte ID")

annot_events <- identify_cba_analyte(df = df,
                     .analytes = analytes,
                     .method_args = args_ident_analyte,
                     .trim_fs = 0.1,
                     .parameter_fs = c("FSC", "SSC"))

annot_events |> facs_plot(.x = "FSC", .y = "SSC", .beads = "Bead events")

# Looks strange because some true beads events have randomly been placed far
# from the center in the forward-side scatter when the data was created
annot_events |>
  facs_plot(.x = "APC", .y = "APC-Cy7", .beads = "Analyte ID")

## End(Not run)
## Not run: 
library(beadplexr)
data(simplex)

df <- simplex[["mplex"]]
analytes <- vector("list", 10) |> setNames(as.character(c(1:10)))

args_ident_analyte <- list(.parameter = c("FITC", "PE"),
                           .column_name = "Analyte ID",
                           .trim = 0.1,
                           .method = "clara")

annot_events <- identify_macsplex_analyte(df = df,
                                     .analytes = analytes,
                                     .method_args = args_ident_analyte)

annot_events |> facs_plot(.x = "FSC", .y = "SSC")

annot_events |>
  facs_plot(.x = "FITC", .y = "PE", .beads = "Analyte ID")

annot_events <- identify_macsplex_analyte(df = df,
                                     .analytes = analytes,
                                     .method_args = args_ident_analyte,
                                     .trim_fs = 0.1,
                                     .parameter_fs = c("FSC", "SSC"))

annot_events |> facs_plot(.x = "FSC", .y = "SSC", .beads = "Bead events")
# Looks strange because some true beads events have randomly been placed far
# from the center in the forward-side scatter when the data was created
annot_events |>
  facs_plot(.x = "FITC", .y = "PE", .beads = "Analyte ID")

## End(Not run)

Description

The panel information are stored in resources in the package directory and can be loaded by providing a file or panel name, or a search pattern

Usage

load_panel(.file_name = NULL, .panel_name = NULL, .panel_pattern = NULL)

Arguments

Details

If an explicit .file_name is given, .panel_name and .panel_pattern are ignored. If no file is given but a .panel_name is, the .panel_pattern i ignored. Only if no .file_name and .panel_name are given, is the .panel_pattern used.

Value

A list

Included panels

• Human Adipokine Panel* (13-plex)

• Human Anti-Virus Response Panel (13-plex)

• Human CD8/NK Panel (13-plex)

• Human Cytokine Panel 2 (13-plex)

• Human Growth Factor Panel (13-plex)

• Human Inflammation Panel (13-plex)

• Human Metabolic Panel 1 (4-plex)

• Human Proinflammatory Chemokine Panel (13-plex)

• Human T Helper Cytokine Panels (13-plex)

• Mouse Anti-Virus Response Panel (13-plex)

• Mouse Cytokine Panel 2 (13-plex)

• Mouse Free Active/Total TGF-b1 Panel (Mouse/Rat) (1-plex)

• Mouse HSC Panel (13-plex)

• Mouse IgE Panel (1-plex)

• Mouse Immunoglobulin Isotyping Panel (6-plex)

• Mouse Inflammation Panel (13-plex)

• Mouse Proinflammatory Chemokine Panel (13-plex)

• Mouse T Helper Cytokine Panels (13-plex)

Example panel file

The panel information files are formatted in YAML. It has three main parts:

• Some general information about the panel - The order of the major bead groups in the forward and side scatter - Analytes, where each bead ID is listed for each major bead group. The bead IDs are further complemented with the name of the analyte and the start concentration of the standard value

The 'Human Th Cytokine Panel (13-plex)' YAML file is found in /resources/legendplex_human_th_cytokine_panel_13-plex.yml of the package directory.

Examples

library(beadplexr)

.panel_name <- "Human T Helper Cytokine Panels (13-plex)"
panel_info <- load_panel(.panel_name = .panel_name)
panel_info$panel_name

.file_name <- system.file("resources",
                          "legendplex_human_cytokine_panel_2_13-plex.yml",
                          package = "beadplexr")
panel_info <- load_panel(.file_name = .file_name)
panel_info$panel_name

panel_info <- load_panel(.file_name = .file_name, .panel_name = .panel_name)
panel_info$panel_name

suppressWarnings(
# The pattern matches several files, which raises a warning
panel_info <- load_panel(.panel_pattern = "panel_2_13-plex")
)
panel_info$panel_name

Description

Data from a "Human Growth Factor Panel (13-plex)" LEGENDplex experiment, with 8 controls and 1 human serum samples, all in duplicates. The beads were measured on a CytoFLEX cytometer, and the fcs-files were processed using read_fcs(), with default settings.

Usage

data("lplex")

Format

A list with 18 elements. Each element is a data.frame about 5000 rows and 4 columns (the exact number varies a little due to the data acquisition):

FSC-A

The forward scatter parameter

SSC-A

The side scatter parameter

FL6-H

Intensity in the FL6 channel

FL2-H

Intensity in the FL2 channel

The list contains 8 standard samples in duplicates, and one serum sample, also in duplicate. The names of each element have the format K3 (internal panel shorthand), C:number: for standards and S:number: for serum sample, and a number indicating the replicate (1 or 2).

Source

Ulrik Stervbo, 2016, Unpublished

Description

Plot concentrations

Usage

plot_std_curve(
  df,
  .model,
  .title = NULL,
  .parameter = "FL2.H",
  .concentration = "Concentration",
  .data = NULL
)

plot_target_est_conc(
  df,
  .title = NULL,
  .concentration = "Calc.conc",
  .std_concentration = "Concentration",
  .data = NULL
)

plot_estimate(
  .sample_data,
  .standard_data,
  .model,
  .title = NULL,
  .parameter = "FL2.H",
  .concentration = "Concentration"
)

Arguments

Value

A ggplot

Examples

library(beadplexr)
library(drc)
data(ryegrass)

ryegrass_m <-
  fit_standard_curve(df = ryegrass,
                     .parameter = "rootl",
                     .concentration = "conc")
recalc_std <-
  calculate_concentration(df = ryegrass,
                          .model = ryegrass_m,
                          .parameter = "rootl")
sample_data <-
  calculate_concentration(df = ryegrass[sample(1:nrow(ryegrass), 5),],
                          .model = ryegrass_m,
                          .parameter = "rootl")

plot_std_curve(ryegrass,
               ryegrass_m,
               .parameter = "rootl",
               .concentration = "conc")

plot_target_est_conc(df = recalc_std,
                     .concentration = "Calc.conc",
                     .std_concentration = "conc")
plot_estimate(
  .sample_data = sample_data,
  .standard_data = ryegrass,
  .model = ryegrass_m,
  .parameter = "rootl",
  .concentration = "conc")

Description

Is deprecated. See the vignette "Preparing flow-data for use with with beadplexr" for an example of preparing flow-data to be used with beadplexr.

Usage

read_fcs(
  .file_name,
  .fsc_ssc = c("FSC-A", "SSC-A"),
  .bead_channels = c("FL6-H", "FL2-H"),
  .filter = list(`FSC-A` = c(200000L, 800000L), `SSC-A` = c(200000L, 1000000L), `FL6-H` =
    c(7.3, Inf)),
  .compensation = "guess",
  ...
)

Arguments

Description

Very simple, simulated multiplex data to demonstrate the clustering functionality of the beadplexr package on CBA and MACSPlex assays.

Usage

data(simplex)

Format

A list with three elements. Each element is a data.frame of 3000 to 9000 rows. The exact format depends on the assay simulated:

lplex

Simulated LEGENDplex data. A single data.frame with the columns:

FSC

The forward scatter parameter

SSC

The side scatter parameter

APC

Intensity in the APC channel

PE

Intensity in the PE channel

mplex

Simulated MACSPlex data. A single data.frame with the columns:

FSC

The forward scatter parameter

SSC

The side scatter parameter

FITC

Intensity in the FITC channel

PE

Intensity in the PE channel

APC

Intensity in the PE channel

cba

Simulated CBA data. A single data.frame with the columns:

FSC

The forward scatter parameter

SSC

The side scatter parameter

APC

Intensity in the APC channel

APC-Cy7

Intensity in the APC-Cy7 channel

PE

Intensity in the PE channel

Source

Artificial

Description

Remove the points furthest form the center of the cluster.

Usage

trim_population(
  df,
  .parameter,
  .column_name = "population",
  .trim = 0.1,
  .data = NULL
)

Arguments

Details

The euclidean distance is calculated for each point defined by .parameter to the center of the cluster. The cluster designation of the .trim most distant points are changed to NA.

Value

A data.frame

Examples

library(beadplexr)
library(dplyr)
library(ggplot2)

data("lplex")

lplex[[1]] |>
  filter(`FSC-A` > 3.2e5L) |>
  mutate(population = "1") |>
  trim_population(.parameter = c("FSC-A", "SSC-A"), .column_name = "population", .trim = 0.1) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()

lplex[[1]] |>
  filter(`FSC-A` > 3.2e5L) |>
  mutate(population = "1") |>
  trim_population(.parameter = c("FSC-A", "SSC-A"),
                  .column_name = "population", .trim = 0.8) |>
  ggplot() +
  aes(x = `FSC-A`, y = `SSC-A`, colour = population) +
  geom_point()

Description

Find turning points (minima and maxima) in a vector.

Usage

turning_point(
  .x,
  .which = c("both", "minima", "maxima"),
  .return = c("value", "index"),
  .adjust = 1.5,
  .k = NULL,
  ...
)

Arguments

Value

A list with the two elements maxima and minima. each element consist of a single data.frame.

Examples

set.seed(1234)
.x <- c(rnorm(100, 2, 1), rnorm(100, 9, 1))

turning_point(.x = .x, .adjust = 1)
turning_point(.x = .x, .k = 2)

turning_point(.x = .x, .which = "minima")
turning_point(.x = .x, .which = "maxima")

turning_point(.x = .x, .return = "index")