Package 'flowTime' reference manual

Title:	Annotation and analysis of biological dynamical systems using flow cytometry
Description:	This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample.
Authors:	R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut]
Maintainer:	R. Clay Wright <[email protected]>
License:	Artistic-2.0
Version:	1.29.0
Built:	2024-07-24 05:30:43 UTC
Source:	https://github.com/bioc/flowTime

Add background subtraction to a summary data frame

Description

Makes a new column from column with the background value of a given baseline control from a chosen identifier column baseline_column subtracted from the values of column.

Usage

addbs(data, column, baseline_column, baseline = "noYFP")
addbs(data, column, baseline_column, baseline = "noYFP")

Arguments

`data`	the summary data frame of a flowSet (from `summarizeFlow` or `flsummary`) to be used in calculating the background subtracted column
`column`	the column containing the fluorescent (or other) measurement to be background subtracted
`baseline_column`	the column containing the identifier of the rows containing background values
`baseline`	`character` the identified or name of representing background fluorescent values

Value

A summary data frame with an additional column column_bs containing the background subtracted values

Examples

dat<-read.flowSet(path=system.file("extdata", "tc_example",
package = "flowTime"),alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "tc_example.csv",
package = "flowTime"))
annotation[which(annotation$treatment == 0), 'strain'] <- 'background'
adat <- annotateFlowSet(dat, annotation)
dat_sum <- summarizeFlow(adat, gated = TRUE,
channel = 'FL1.A')
dat_sum <- addbs(data = dat_sum, column = FL1.Amean,
baseline_column = strain,
baseline = "background")

dat<-read.flowSet(path=system.file("extdata", "tc_example",
package = "flowTime"),alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "tc_example.csv",
package = "flowTime"))
annotation[which(annotation$treatment == 0), 'strain'] <- 'background'
adat <- annotateFlowSet(dat, annotation)
dat_sum <- summarizeFlow(adat, gated = TRUE,
channel = 'FL1.A')
dat_sum <- addbs(data = dat_sum, column = FL1.Amean,
baseline_column = strain,
baseline = "background")

Normalize fluorescence

Description

Produces a normalized fluorescence column 'normed'. Expects the 'FL1.A_bs' column to exist or a column to be specified. Has three different methods, version 1 and version 2, described in the script

Usage

addnorm(
  frame,
  factor_in = c("strain", "treatment"),
  method = 1,
  column = "FL3.Amean_bs"
)
addnorm(
  frame,
  factor_in = c("strain", "treatment"),
  method = 1,
  column = "FL3.Amean_bs"
)

Arguments

`frame`	`data frame` of summary statistics to be normalized
`factor_in`	`character vector` containing the varibles to split the data frame by
`method`	which normalization method to use, 1, 2 or 3.
`column`	`character` the column to apply the normalization to

Details

Method 1, the default normalization method, takes the highest point in each dataset grouped by 'factor_in' and normalizes all values in the group by this point. This method is default because it works regardless of whether the data is a time series. Method 2 finds the mean value of all time points with time values less than 0 for each group and normalizes each group by this respective value. Requires a time series with negative time values to work. Method 3 fits a linear model to the pre-zero time points for each groups, infers the y-intercept, and normalizes using this intercept. Method 3 also requires a time series with negative time values to work.

Value

data frame containing the additional normalized variable

Examples

dat <- read.flowSet(path=system.file("extdata", "tc_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "tc_example.csv",
package = "flowTime"))
adat <- annotateFlowSet(dat, annotation)
loadGates(gatesFile = 'C6Gates')
dat_sum <- summarizeFlow(adat, ploidy = "diploid", only = "singlets",
channel = "FL1.A")
dat_sum <- addnorm(dat_sum, c("strain", "treatment"), method = 1,
column = "FL1.Amean")

dat <- read.flowSet(path=system.file("extdata", "tc_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "tc_example.csv",
package = "flowTime"))
adat <- annotateFlowSet(dat, annotation)
loadGates(gatesFile = 'C6Gates')
dat_sum <- summarizeFlow(adat, ploidy = "diploid", only = "singlets",
channel = "FL1.A")
dat_sum <- addnorm(dat_sum, c("strain", "treatment"), method = 1,
column = "FL1.Amean")

Annotate a flowSet with experimental metadata

Description

Add annotations to a flowSets phenoData and plate numbers, strain names, and treatment also set T0

Usage

annotateFlowSet(yourFlowSet, annotation_df, mergeBy = "name")
annotateFlowSet(yourFlowSet, annotation_df, mergeBy = "name")

Arguments

`yourFlowSet`	a flowSet with sampleNames of the format 'plate#_Well', we typically use the following code chunk to read data from individual plates as exported from BD Accuri C6 software.
`annotation_df`	A data frame with columns 'well', 'strain', 'treatment', containing all of the wells in the flowset labeled with the strain and treatment in that well.
`mergeBy`	the unique identifier column

Value

An annotated flowSet

Examples

dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "ss_example.csv", package =
"flowTime"))
annotateFlowSet(dat, annotation, mergeBy = "name")

dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "ss_example.csv", package =
"flowTime"))
annotateFlowSet(dat, annotation, mergeBy = "name")

Create an annotation dataframe

Description

Creates a data frame with rows containing the sample names of your flow set that can then be filled in with experimental metadata.

Usage

createAnnotation(yourFlowSet)
createAnnotation(yourFlowSet)

Arguments

yourFlowSet

the flowSet to create an annotation data frame for

Value

annotation_df a data frame containing the sample names of your flow set

Examples

dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
annotation <- createAnnotation(yourFlowSet = dat)
head(annotation)
dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
annotation <- createAnnotation(yourFlowSet = dat)
head(annotation)

A gate for the set of all diploid doublets

Description

A gate for the set of all diploid doublets

Usage

data(dipdoubletGate)
data(dipdoubletGate)

Format

formal class polygonGate

A gate for the set of all diploid singlet yeast cells

Description

Typically set in FSC.A by FSC.H space Diploids are typically 5um x 6um ellipsoids while haploids are typically 4um x 4um spheroids. As a result, diploids are longer and you get a larger 'area/volume'.

Usage

data(dipsingletGate)
data(dipsingletGate)

Format

formal class polygonGate

Get summary statistics for fluorescence or other data channels of a flowSet

Description

Get summary statistics for fluorescence or other data channels of a flowSet

Usage

flsummary(flowset, channel)
flsummary(flowset, channel)

Arguments

`flowset`	the `flowSet` to create summary statistics for
`channel`	option `character vector` of the data channel(s) to summarize. By default all channels will be summarized. Setting channels does not reduce computation time

Value

A data frame containing summary statistics (mean, median, SD) for the specified fluorescent channel and time moments of the flowSet.

Examples

plate1 <- read.flowSet(path = system.file("extdata",
"ss_example", package = "flowTime"), alter.names = TRUE)
flsummary(flowset = plate1, channel = "FL1.A")
plate1 <- read.flowSet(path = system.file("extdata",
"ss_example", package = "flowTime"), alter.names = TRUE)
flsummary(flowset = plate1, channel = "FL1.A")

Get the time at which at flowFrame began collection

Description

Get the time at which at flowFrame began collection

Usage

getTime(flowframe)
getTime(flowframe)

Arguments

flowframe

The flowFrame for which you would like the initial time

Value

numeric time value in minutes

Examples

plate1<-read.flowSet(path = system.file("extdata", "ss_example", package =
"flowTime"),alter.names = TRUE)
getTime(plate1$A01.fcs)
plate1<-read.flowSet(path = system.file("extdata", "ss_example", package =
"flowTime"),alter.names = TRUE)
getTime(plate1$A01.fcs)

A gate for the set of all haploid doublets

Description

A gate for the set of all haploid doublets

Usage

data(hapdoubletGate)
data(hapdoubletGate)

Format

formal class polygonGate

A gate for the set of all haploid singlets

Description

A gate for the set of all haploid singlets

Usage

data(hapsingletGate)
data(hapsingletGate)

Format

formal class polygonGate

Load a yeast gate file

Description

Loads a set of yeast gates into active memory to be used in analysis functions

Usage

loadGates(gatesFile = NULL, path = NULL, envir = environment())
loadGates(gatesFile = NULL, path = NULL, envir = environment())

Arguments

`gatesFile`	the gates file to be loaded into memory, or path to the gates file
`path`	The path to the gates file. If 'NULL' this will look through lazy loaded data for the gatesFile
`envir`	The environment in which to load the gates

Value

gate objects created in the current environment

Examples

loadGates(system.file("extdata/SORPGates.RData", package = "flowTime"))
loadGates(system.file("extdata/SORPGates.RData", package = "flowTime"))

Summary statistic columns for a flow frame

Description

Summary statistic columns for a flow frame

Usage

meanMedianSD(frame)
meanMedianSD(frame)

Arguments

frame

a flowFrame

Value

a matrix with a single row for the flow frame and mean, median, and sd columns for each column of the expression measurements

Examples

plate1<-read.flowSet(path = system.file("extdata", "ss_example", package =
"flowTime"), alter.names = TRUE)
meanMedianSD(plate1@frames$A01.fcs)
fsApply(plate1, meanMedianSD)
plate1<-read.flowSet(path = system.file("extdata", "ss_example", package =
"flowTime"), alter.names = TRUE)
meanMedianSD(plate1@frames$A01.fcs)
fsApply(plate1, meanMedianSD)

Guess the ploidy of a given flowframe

Description

Use the FSC.A/FSC.H ratio. Diploids are typically 5um x 6um ellipsoids while haploids are typically 4um x 4um spheroids. As a result, diploids are longer and you get a larger 'area/volume' FSC.A. 'Width' might also be useful on certain cytometers.

Usage

ploidy(flowframe)
ploidy(flowframe)

Arguments

flowframe

the flowFrame you would like to identify the ploidy of

Value

"Diploid" or "Haploid" and the mean FSC.A/FSC.H quotient

Examples

dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
ploidy(dat$A01.fcs)

dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
ploidy(dat$A01.fcs)

Create a polygon gate

Description

Create a polygon gate

Usage

polyGate(x, y, filterID = "newGate", channels = c("FSC.A", "FSC.H"))
polyGate(x, y, filterID = "newGate", channels = c("FSC.A", "FSC.H"))

Arguments

`x`	a vector of x coordinates
`y`	a vector of y coordinates
`filterID`	name of the gate
`channels`	vector containing the channels matching the x and y coordinates above

Value

a polygon gate object

Examples

polyGate(x = c(1,1,10000,10000), y = c(1,10000, 10000, 1), )
polyGate(x = c(1,1,10000,10000), y = c(1,10000, 10000, 1), )

Quality assurance check

Description

Check whether a flowSet (or a single flowFrame) contains empty values, in which case normalization may fail (divide by zero). This is particularly useful for removing wash wells from a flowSet.

Usage

qaGating(x, threshold = 100)
qaGating(x, threshold = 100)

Arguments

`x`	`flowSet` or `flowFrame` to be checked
`threshold`	`flowFrames` with fewer events than this threshold will be identified.

Value

A vector containing the flowFrames with fewer events than the threshold.

Examples

plate1<-read.flowSet(path = system.file("extdata", "ss_example", package =
"flowTime"), alter.names = TRUE)
qaGating(plate1)

plate1<-read.flowSet(path = system.file("extdata", "ss_example", package =
"flowTime"), alter.names = TRUE)
qaGating(plate1)

Read FCS files from set of plates

Description

Reads all folders within the specified path containing the specified pattern in the folder names. Each folder contains a set a plate of FCS files. These folders typically make up a whole experiment. Plates are numbered according to the standard lexicographical ordering of your operating system.

Usage

read.plateSet(path = getwd(), pattern = "", ...)
read.plateSet(path = getwd(), pattern = "", ...)

Arguments

`path`	The path to search for folders containing FCS files
`pattern`	The regex pattern used to identify the folders of FCS files to be read
`...`	Additional arguments passed to read.flowSet. Note that 'alter.names' is forced to be TRUE in this implementation.

Value

A single flowSet containing all FCS files within the identified folders. The index of each folder in the list according to lexicographical ordering (1,2,...) is prepended to the sampleNames.

Examples

# Read in both of the example data sets as a single flowSet
plate1<-read.plateSet(path = system.file("extdata", package = "flowTime"),
pattern = "")

# Read in both of the example data sets as a single flowSet
plate1<-read.plateSet(path = system.file("extdata", package = "flowTime"),
pattern = "")

Save a yeast gate set

Description

Save a yeast gate set

Usage

saveGates(
  yeastGate = NULL,
  dipsingletGate = NULL,
  dipdoubletGate = NULL,
  hapsingletGate = NULL,
  hapdoubletGate = NULL,
  path = getwd(),
  fileName = "defaultGates.RData"
)
saveGates(
  yeastGate = NULL,
  dipsingletGate = NULL,
  dipdoubletGate = NULL,
  hapsingletGate = NULL,
  hapdoubletGate = NULL,
  path = getwd(),
  fileName = "defaultGates.RData"
)

Arguments

`yeastGate`	a gate object defining the population of yeast cells
`dipsingletGate`	a gate object defining the population of diploid singlet cells
`dipdoubletGate`	a gate object defining the population of diploid doublet cells
`hapsingletGate`	a gate object defining the population of haploid singlet cells
`hapdoubletGate`	a gate object defining the population of haploid doublet cells
`path`	path to the folder in which you would like to save the gates
`fileName`	name of the .Rdata file you would like to save these gates within

Value

a .RData file in the "extdata" folder of the package containing the specified gates

Examples

loadGates(system.file("extdata/SORPGates.RData", package = "flowTime"))
#not run:
#saveGates()
loadGates(system.file("extdata/SORPGates.RData", package = "flowTime"))
#not run:
#saveGates()

Analysis of steady state fluorescence flow cytometry

Description

Generates a data frame which can be used to visualize and analyze steady state flow cytometry data. Steady state in this case means that

Usage

steadyState(flowset, gated = FALSE, ploidy = NA, only = NA)
steadyState(flowset, gated = FALSE, ploidy = NA, only = NA)

Arguments

`flowset`	your flowSet to be analyzed
`gated`	`boolean` is the data already gated?
`ploidy`	`character` gate to subset your flowset based on the ploidy of you strains
`only`	`character` which population of events to analyze, 'yeast', 'singlets', or 'doublets'?

Value

a data frame containing all of the selected subset of events from the original flowSet

Examples

dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "ss_example.csv",
package = "flowTime"))
dat <- annotateFlowSet(dat, annotation, mergeBy = "name")
loadGates(gatesFile = 'SORPGates')
steadyState(dat, gated = FALSE, ploidy = "diploid", only = "singlets")

dat <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "ss_example.csv",
package = "flowTime"))
dat <- annotateFlowSet(dat, annotation, mergeBy = "name")
loadGates(gatesFile = 'SORPGates')
steadyState(dat, gated = FALSE, ploidy = "diploid", only = "singlets")

Generate summary statistics for a flowSet

Description

Gates a sample to all yeast, then singlet, then doublets. Also calculates singlet to doublet ratio. Returns a list of data frames, e.g. output$singlets, output$doublets, etc.

Usage

summarizeFlow(
  flowset,
  channel = NA,
  gated = FALSE,
  ploidy = FALSE,
  only = FALSE
)
summarizeFlow(
  flowset,
  channel = NA,
  gated = FALSE,
  ploidy = FALSE,
  only = FALSE
)

Arguments

`flowset`	the `flowSet` to be summarized
`channel`	`character vector` which data channel(s) should be summarized? If excluded (or `NA`) all channels will be summarized (default)
`gated`	`boolean` is the data already appropriately gated?
`ploidy`	`character` does the flowSet contain haploid or diploid cells?
`only`	`character` summarize only "singlet", "doublet", or all "yeast" cells, FALSE will return all

Value

data frame containing the specified summary statistics of the specified cell populations for each frame

Examples

plate1 <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
summarizeFlow(plate1, channel = "FL1.A", gated = TRUE,
ploidy = "diploid", only = "yeast")

plate1 <- read.flowSet(path = system.file("extdata", "ss_example",
package = "flowTime"), alter.names = TRUE)
summarizeFlow(plate1, channel = "FL1.A", gated = TRUE,
ploidy = "diploid", only = "yeast")

Generate a tidy dataset from time-course flow cytometry data

Description

Generates a tibble containing all parameters and phenoData from a flowSet which can be used to visualize and analyze timecourse flow cytometry data.

Usage

tidyFlow(flowset, gated = TRUE, ploidy = NA, only = NA)
tidyFlow(flowset, gated = TRUE, ploidy = NA, only = NA)

Arguments

`flowset`	your flowSet to be analyzed
`gated`	`boolean` is the data already gated?
`ploidy`	`character` gate to subset your flowset based on the ploidy of you strains
`only`	`character` which population of events to analyze, 'yeast', singlets', or 'doublets'?

Value

a data frame containing all of the selected subset of events from the original flowSet for all parameters including experiment time, etime, the time after the initial reading at which each event was collected.

Examples

plate1<-read.flowSet(path=system.file("extdata", "tc_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "tc_example.csv",
package = "flowTime"))
plate1 <- annotateFlowSet(plate1, annotation)
tidy_dat <- tidyFlow(plate1, gated = TRUE)
head(tidy_dat)

plate1<-read.flowSet(path=system.file("extdata", "tc_example",
package = "flowTime"), alter.names = TRUE)
annotation <- read.csv(system.file("extdata", "tc_example.csv",
package = "flowTime"))
plate1 <- annotateFlowSet(plate1, annotation)
tidy_dat <- tidyFlow(plate1, gated = TRUE)
head(tidy_dat)

A gate for the set of all yeast cells

Description

Typically set in FSC.A by SSC.A space to exclued any debris

Usage

data(yeastGate)
data(yeastGate)

Format

formal class polygonGate

Package 'flowTime'

Help Index

Add background subtraction to a summary data frame

Description

Usage

Arguments

Value

Examples

Normalize fluorescence

Description

Usage

Arguments

Details

Value

Examples

Annotate a flowSet with experimental metadata

Description

Usage

Arguments

Value

Examples

Create an annotation dataframe

Description

Usage

Arguments

Value

Examples

A gate for the set of all diploid doublets

Description

Usage

Format

A gate for the set of all diploid singlet yeast cells

Description

Usage

Format

Get summary statistics for fluorescence or other data channels of a flowSet

Description

Usage

Arguments

Value

Examples

Get the time at which at flowFrame began collection

Description

Usage

Arguments

Value

Examples

A gate for the set of all haploid doublets

Description

Usage

Format

A gate for the set of all haploid singlets

Description

Usage

Format

Load a yeast gate file

Description

Usage

Arguments

Value

Examples

Summary statistic columns for a flow frame

Description

Usage

Arguments

Value

Examples

Guess the ploidy of a given flowframe

Description

Usage

Arguments

Value

Examples

Create a polygon gate

Description

Usage

Arguments

Value

Examples

Quality assurance check