| Title: | Analysis and Visualization of Droplet Digital PCR in R and on the Web |
|---|---|
| Description: | An interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R. |
| Authors: | Dean Attali [aut, cre] |
| Maintainer: | Dean Attali <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.15.2.9000 |
| Built: | 2024-09-22 04:02:13 UTC |
| Source: | https://github.com/daattali/ddpcr |
Check if a ddPCR plate has been fully analyzed or if there are remaining steps.
analysis_complete(plate)analysis_complete(plate)
plate |
A ddPCR plate |
TRUE if the plate's analysis has been fully carried out;
FALSE otherwise.
Every ddPCR plate has a set of defined steps that are taken in order, that
together constitute "analyzing" the plate. Calling the analyze function
will perform all the analysis steps, which may take several minutes. Running
the analysis will classify the droplets in the plate into clusters (available
via plate_data) and will add variables to the plate
metadata (available via plate_meta).
analyze(plate, restart = FALSE)analyze(plate, restart = FALSE)
plate |
A ddPCR plate |
restart |
If |
This function will run an analysis to completion. If you want to run each
step one at a time, use next_step.
The analyzed ddPCR plate
Most analysis steps result in some progress messages being printed to
the screen. You can turn off these messages by disabling the verbose option
with the command options(ddpcr.verbose = FALSE).
next_stepplot.ddpcr_platenew_platestepsplate_dataplate_meta
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate <- analyze(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate <- analyze(plate) ## End(Not run)
Each ddPCR plate type has a specific set of potential clusters the droplets can be assigned to.
clusters(plate)clusters(plate)
plate |
a ddPCR plate. |
See the README for more information on plate types.
A character vector with the names of the clusters supported by the plate type.
## Not run: dir <- sample_data_dir() new_plate(dir) %>% clusters new_plate(dir, plate_types$fam_positive_pnpp) %>% clusters ## End(Not run)## Not run: dir <- sample_data_dir() new_plate(dir) %>% clusters new_plate(dir, plate_types$fam_positive_pnpp) %>% clusters ## End(Not run)
The custom_thresholds plate type is used when you want to gate ddPCR
droplet data into four quadrants according to HEX and FAM values that you
manually set. All wells in the plate will use the same threshold values.
Plates with this type have only three analysis steps: INITIALIZE,
REMOVE_OUTLIERS, and CLASSIFY (according to the custom thresholds).
Plates with this type have the following droplet clusters:
UNDEFINED, OUTLIER, EMPTY (bottom-left quadrant),
X_POSITIVE (bottom-right quadrant), Y_POSITIVE (top-left quadrant),
BOTH_POSITIVE (top-right quadrant).
See the README for more information on plate types.
plate_typesx_thresholdy_thresholdthresholdsanalyze remove_outliersclassify_thresholds
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) type(plate) plate %>% analyze %>% plot ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) type(plate) plate %>% analyze %>% plot ## End(Not run)
The default plate type that all other plates inherit from. If you initialize
a ddPCR plate without specifying a plate type, ddpcr_plate will be the
plate's type.
Plates with this type have the following analysis steps: INITIALIZE,
REMOVE_FAILURES, REMOVE_OUTLIERS, REMOVE_EMPTY.
Plates with this type have the following droplet clusters: UNDEFINED,
FAILED, OUTLIER, EMPTY.
See the README for more information on plate types.
plate_typesremove_failuresremove_outliersremove_empty
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$ddpcr_plate) type(plate) plate %>% analyze %>% plot ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$ddpcr_plate) type(plate) plate %>% analyze %>% plot ## End(Not run)
A ddPCR plate of type fam_positive_pnpp, which can also be expressed as
(FAM+)/(FAM+HEX+), is a subtype of both pnpp_experiment
and wildtype_mutant_pnpp. Use this plate type if your data
has three main clusters of droplets: double-negative (empty droplets),
FAM+HEX+ (wildtype droplets) and FAM+HEX- (mutant droplets).
Plates with this type have the following analysis steps: INITIALIZE,
REMOVE_FAILURES, REMOVE_OUTLIERS, REMOVE_EMPTY,
CLASSIFY, RECLASSIFY.
Plates with this type have the following droplet clusters:
UNDEFINED, FAILED, OUTLIER, EMPTY (double-negative),
RAIN (not empty but not wildtype nor negative), POSITIVE (wildtype),
NEGATIVE (mutant).
See the README for more information on plate types.
plate_typeswildtype_mutant_pnpphex_positive_pnppanalyzeremove_failuresremove_outliersremove_emptyclassify_dropletsreclassify_droplets
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) type(plate) plate %>% analyze %>% plot ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) type(plate) plate %>% analyze %>% plot ## End(Not run)
A ddPCR plate of type hex_positive_pnpp, which can also be expressed as
(HEX+)/(FAM+HEX+), is a subtype of both pnpp_experiment
and wildtype_mutant_pnpp. Use this plate type if your data
has three main clusters of droplets: double-negative (empty droplets),
FAM+HEX+ (wildtype droplets) and HEX+FAM- (mutant droplets).
Plates with this type have the following analysis steps: INITIALIZE,
REMOVE_FAILURES, REMOVE_OUTLIERS, REMOVE_EMPTY,
CLASSIFY, RECLASSIFY.
Plates with this type have the following droplet clusters:
UNDEFINED, FAILED, OUTLIER, EMPTY (double-negative),
RAIN (not empty but not wildtype nor negative), POSITIVE (wildtype),
NEGATIVE (mutant).
See the README for more information on plate types.
plate_typeswildtype_mutant_pnppfam_positive_pnppanalyzeremove_failuresremove_outliersremove_emptyclassify_dropletsreclassify_droplets
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$hex_positive_pnpp) type(plate) plate %>% analyze %>% plot ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$hex_positive_pnpp) type(plate) plate %>% analyze %>% plot ## End(Not run)
In addition to the functions provided in this package, the ddpcr package
also provides an interactive tool that can be used to analyze ddPCR data
more easily. The tool will be launched in a web browser.
launch()launch()
Reloads a plate that has been saved with save_plate.
load_plate(file)load_plate(file)
file |
Name of the file where the plate was saved. |
The plate that was saved in the given file.
plate <- new_plate(sample_data_dir()) save_plate(plate, "myplate") plate2 <- load_plate("myplate") plate3 <- load_plate("myplate.rds") identical(plate, plate2) identical(plate, plate3) unlink("myplate.rds")plate <- new_plate(sample_data_dir()) save_plate(plate, "myplate") plate2 <- load_plate("myplate") plate3 <- load_plate("myplate.rds") identical(plate, plate2) identical(plate, plate3) unlink("myplate.rds")
Get or set the name of a dataset.
name(plate) name(plate) <- valuename(plate) name(plate) <- value
plate |
A ddpcrPlate |
value |
New name |
Plate name
## Not run: plate <- new_plate(sample_data_dir()) name(plate) name(plate) <- "foo" name(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir()) name(plate) name(plate) <- "foo" name(plate) ## End(Not run)
Any ddPCR analysis must start by creating a ddPCR plate object. Use this function to read ddPCR data into R and create a plate object that can then be analyzed.
new_plate(dir, type, data_files, meta_file, name, params)new_plate(dir, type, data_files, meta_file, name, params)
dir |
The directory containing the ddPCR droplet data files, and potentially the plate results file |
type |
A ddPCR plate type (see |
data_files |
If |
meta_file |
If |
name |
Name of the dataset. If not provided, the name will be guessed based on the filenames. |
params |
List of parameters to set for the plate. Only advanced users should consider using this feature. |
See the README for more information on plate types.
A new ddPCR plate object with droplet data loaded that is ready to be analyzed.
The first step to using the ddpcr package is to get the ddPCR data into
R. This package uses as input the data files that are exported by QuantaSoft.
For a dataset with 20 wells, QuantaSoft will create 20 well files (each ending
with "_Amplitude.csv") and one results file. The well files are essential for
analysis since they contain the actual droplet data, and the results file
is optional because the only information used from it is the mapping from
well IDs to sample names.
The easiest way to use your ddPCR data with this package is to Export the data
from QuantaSoft into some directory, and providing that directory as the
dir argument. This way, this package will automatically find all the
data files as well as the results file. Alternatively, you can provide the
data files (well files) manually as a list of filenames using the data_files
argument. If you use the data_files argument instead of dir, you
can also optionally provide the results file as the meta_file argument.
If no results file is provided then the wells will not be mapped to their sample
names.
Every plate has a set of default parameters that are used in the analysis.
You can see all the parameters of a plate with the params
function. If you want to provide different values for some parameters when
initializing a plate, you can do that with the params argument. This
is considered an advanced feature.
For example, if you inspect the parameters of any ddPCR plate, you will see that by defalt the random seed used by default is 8. If you want to create a new plate that uses a different random seed, you could do so like this:
plate <- new_plate(sample_data_dir(), params = list('GENERAL' = list('RANDOM_SEED' = 10)))
plate
Most numeric parameters that are used in the algorithms of the analysis steps can be modified in a similar fashion. This can be used to fine-tune the analysis of a plate if you require different parameters.
plate_typestyperesetanalyzeplot.ddpcr_plateparams
## Not run: plate <- new_plate(sample_data_dir()) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir()) ## End(Not run)
Every ddPCR plate has a set of defined steps that are taken in order, that
together constitute "analyzing" the plate. Calling the next_step function
will run the next step in the analysis, which may take several minutes. If you
want to run all the remaining steps at once, use analyze instead.
next_step(plate, n = 1)next_step(plate, n = 1)
plate |
A ddPCR plate |
n |
The number of steps to run |
The ddPCR plate after running the next step
plot.ddpcr_plateanalyzestepsplate_dataplate_meta
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate <- next_step(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate <- next_step(plate) ## End(Not run)
Every ddPCR plate object has adjustable parameters associated with it.
Each parameter belongs to a category of parameters, and has a unique name.
For example, there are general parameters (category 'GENERAL') that apply to
the plate as a whole, and each analysis step has its own set of parameters
that are used for the algorithm in that step.
You can either view all parameters of a plate by not providing any arguments,
view all parameters in a category by providing the category, or view the value
of a specific parameter by providing both the category and the parameter name.
params(plate, category, name) params(plate, category, name) <- valueparams(plate, category, name) params(plate, category, name) <- value
plate |
A ddPCR plate |
category |
Category of parameters |
name |
Parameter name |
value |
New parameter value |
Setting new parameter values should only be done by advanced users. Note that if you change any parameters, you need to re-run the analysis in order for the parameter changes to take effect.
Tip: it can be easier to visually inspect the parameters by wrapping the
return value in a str().
Warning: Do not directly set the GENERAL-X_VAR or GENERAL-Y_VAR parameters.
Instead, use x_var or y_var.
If no category is provided, return all parameters. If a category is provided, return all parameters in that category. If both a category and a name are provided, return the value of the specific parameter.
## Not run: plate <- new_plate(sample_data_dir()) # retrieving plate parameters str(params(plate)) str(params(plate, 'GENERAL')) params(plate, 'GENERAL', 'RANDOM_SEED') # setting plate parameters params(plate, 'GENERAL', 'RANDOM_SEED') <- 10 str(params(plate, 'GENERAL')) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir()) # retrieving plate parameters str(params(plate)) str(params(plate, 'GENERAL')) params(plate, 'GENERAL', 'RANDOM_SEED') # setting plate parameters params(plate, 'GENERAL', 'RANDOM_SEED') <- 10 str(params(plate, 'GENERAL')) ## End(Not run)
The main piece of information in every ddPCR plate is the droplets data,
which contains the fluorescence intensities for every single droplet in
every well. After a ddPCR plate gets analyzed, this data also includes the
assigned cluster for each droplet. The plate data may be useful programatically,
but it's not very useful to a human, so if you want to visualize the plate data
you should instead plot it using plot.ddpcr_plate.
plate_data(plate)plate_data(plate)
plate |
A ddPCR plate |
A dataframe containing all the droplets in the plate, along with the assigned cluster of each droplet.
## Not run: plate <- new_plate(sample_data_dir()) plate_data(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir()) plate_data(plate) ## End(Not run)
The metadata is a collection of variables that describe each well in the plate. The metadata of an unanalyzed plate only contains basic information about each well, such as the sample name, whether the well was used, and the number of droplets in the well. Analyzing a plate adds many more variables to the metadata, such as the number of empty droplets, the number of outliers, the template concentration, and more.
plate_meta(plate, only_used = FALSE)plate_meta(plate, only_used = FALSE)
plate |
A ddPCR plate |
only_used |
If |
A dataframe containing the plate metadata
## Not run: plate <- new_plate(sample_data_dir()) plate %>% plate_meta(only_used = TRUE) plate %>% analyze %>% plate_meta(only_used = TRUE) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir()) plate %>% plate_meta(only_used = TRUE) plate %>% analyze %>% plate_meta(only_used = TRUE) ## End(Not run)
Each ddPCR plate has a plate type which determines what type of analysis to run
on the data. plate_types is a list containing the plate types that are
supported. If no plate type is specified, the default assumed type is
ddpcr_plate.
The most useful built-in plate types are:
fam_positive_pnpp,
hex_positive_pnpp,
custom_thresholds.
For full details on the differences between plate types or to learn how to
add a new plate type, see the package README.
new_platefam_positive_pnpphex_positive_pnppcustom_thresholdspnpp_experimentwildtype_mutant_pnppddpcr_platetype
## Not run: dir <- sample_data_dir() new_plate(dir, type = plate_types$ddpcr_plate) new_plate(dir, type = plate_types$custom_thresholds) new_plate(dir, type = plate_types$fam_positive_pnpp) ## End(Not run)## Not run: dir <- sample_data_dir() new_plate(dir, type = plate_types$ddpcr_plate) new_plate(dir, type = plate_types$custom_thresholds) new_plate(dir, type = plate_types$fam_positive_pnpp) ## End(Not run)
Same plot as plot.ddpcr_plate but with a few extra
features that are specific to plates with custom thresholds. Take a look
at plot.ddpcr_plate to see all supported parameters
and more information.
## S3 method for class 'custom_thresholds' plot( x, wells, samples, ..., show_thresholds = TRUE, col_thresholds = "black", show_drops_empty = TRUE, col_drops_x_positive = "green3", col_drops_y_positive = "blue", col_drops_both_positive = "orange" )## S3 method for class 'custom_thresholds' plot( x, wells, samples, ..., show_thresholds = TRUE, col_thresholds = "black", show_drops_empty = TRUE, col_drops_x_positive = "green3", col_drops_y_positive = "blue", col_drops_both_positive = "orange" )
x |
A ddPCR plate. |
wells |
Only plot selected wells. Supports range notation, see
|
samples |
Only plot selected samples. |
... |
Parameters to pass to |
show_thresholds |
If |
col_thresholds |
The colour of the threshold lines. |
show_drops_empty |
Whether or not to show the droplets defined as empty. |
col_drops_x_positive |
The colour to use for droplets that are in the X+Y- quadrant. |
col_drops_y_positive |
The colour to use for droplets that are in the X-Y+ quadrant. |
col_drops_both_positive |
The colour to use for droplets that are in the X+Y+ quadrant. |
A ggplot2 plot object.
plot.ddpcr_platecustom_thresholds
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate %>% set_thresholds(c(5500, 8000)) %>% analyze %>% plot ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate %>% set_thresholds(c(5500, 8000)) %>% analyze %>% plot ## End(Not run)
Plot the data of a ddPCR plate. A plate can be plotted throughout any stage of the analysis, and the most up-to-date data will be shown. For example, a plot performed after initializing a plat will show all the raw data, but a plot performed after analyzing a plate will show information such as empty drops and failed wells.
## S3 method for class 'ddpcr_plate' plot( x, wells, samples, superimpose = FALSE, show_full_plate = FALSE, show_drops = TRUE, show_drops_empty = FALSE, show_drops_outlier = FALSE, show_failed_wells = TRUE, col_drops = "black", col_drops_undefined = col_drops, col_drops_failed = col_drops, col_drops_empty = col_drops, col_drops_outlier = "orange", bg_plot = "transparent", bg_failed = "#111111", bg_unused = "#FFFFFF", alpha_drops = 0.2, alpha_drops_outlier = 1, alpha_bg_failed = 0.7, xlab = x_var(plate), ylab = y_var(plate), title = NULL, show_grid = FALSE, show_grid_labels = FALSE, drops_size = 1, text_size_title = 14, text_size_row_col = 12, text_size_axes_labels = 12, text_size_grid_labels = 12, ... )## S3 method for class 'ddpcr_plate' plot( x, wells, samples, superimpose = FALSE, show_full_plate = FALSE, show_drops = TRUE, show_drops_empty = FALSE, show_drops_outlier = FALSE, show_failed_wells = TRUE, col_drops = "black", col_drops_undefined = col_drops, col_drops_failed = col_drops, col_drops_empty = col_drops, col_drops_outlier = "orange", bg_plot = "transparent", bg_failed = "#111111", bg_unused = "#FFFFFF", alpha_drops = 0.2, alpha_drops_outlier = 1, alpha_bg_failed = 0.7, xlab = x_var(plate), ylab = y_var(plate), title = NULL, show_grid = FALSE, show_grid_labels = FALSE, drops_size = 1, text_size_title = 14, text_size_row_col = 12, text_size_axes_labels = 12, text_size_grid_labels = 12, ... )
x |
A ddPCR plate. |
wells |
Only plot selected wells. Supports range notation, see
|
samples |
Only plot selected samples. |
superimpose |
If |
show_full_plate |
If |
show_drops |
Whether or not to show the droplets. Setting to |
show_drops_empty |
Whether or not to show the droplets defined as empty. See 'Droplet visibility options' below. |
show_drops_outlier |
Whether or not to show the droplets defined as outliers. See 'Droplet visibility options' below. |
show_failed_wells |
Whether or not to include wells that are deemed as failed ddPCR runs. |
col_drops |
The default colour to use for any droplet. |
col_drops_undefined |
The colour to use for droplets that have not been analyzed yet. See 'Droplet visibility options' below. |
col_drops_failed |
The colour to use for droplets in failed wells. See 'Droplet visibility options' below. |
col_drops_empty |
The colour to use for empty droplets. See 'Droplet visibility options' below. |
col_drops_outlier |
The colour to use for outlier droplets. See 'Droplet visibility options' below. |
bg_plot |
The background colour for the plot. |
bg_failed |
The background colour to use for failed wells. |
bg_unused |
The background colour to use for unused wells. |
alpha_drops |
The transparency of droplets. |
alpha_drops_outlier |
The transparency of outlier droplets. See 'Droplet visibility options' below. |
alpha_bg_failed |
The transparency of the background of failed wells. |
xlab |
The label on the X axis. |
ylab |
The label on the Y axis. |
title |
The title for the plot. |
show_grid |
Whether or not to show grid lines. |
show_grid_labels |
Whether or not to show numeric labels for the grid lines along the axes. |
drops_size |
Size of droplets. |
text_size_title |
Text size of the title. |
text_size_row_col |
Text size of the row and column labels. |
text_size_axes_labels |
Text size of the X/Y axis labels. |
text_size_grid_labels |
Text size of the numeric grid line labels. |
... |
Ignored. |
A ggplot2 plot object.
To make it easier to support any plate type with any types of droplet clusters, there are three categories of special parameters that can always be used:
show_drops_* Whether or not to show a specific group of
droplets.
col_drops_* What colour to use for a specific group of
droplets.
alpha_drops_* What transparency to use for a specific group
of droplets.
The * in the parameter name can be replaced by the name of any
droplet cluster. Use the clusters function to
find out what clusters the droplets in a plate can be assigned to.
For example, the default clusters that exist in a plain ddpcr_plate
are "UNDEFINED", "FAILED", "OUTLIER", and "EMPTY". This means that if you
want to hide the empty drops and make the transparency of drops in failed
wells 0.5, you could add the two parameters show_drops_empty = FALSE
and alpha_drops_failed = 0.5. Note that letter case is not important.
If another plate type defines a new clsuter of type "MUTANT" and you want to
show these drops in red, you can add the parameter
col_drops_mutant = "red".
Note that some of the more common combinations of these parameters are
defined by default (for example, col_drops_failed is defined in the
list of parameters), but these three parameter categories will work for
any cluster type.
If you create your own plate type, this default plot function might be
enough if there is no extra information you want to display in a plot.
If you do need to provide a more customized plot function, it can be
a good idea to use the output from this plot function as a basis and only
add the code that is necessary to append to the plot. See
plot.custom_thresholds as an example of how to
extend this plot function.
## Not run: plate <- new_plate(sample_data_dir()) plot(plate) plate <- plate %>% analyze plot(plate) plot(plate, "A01:C05", show_drops_empty = TRUE, col_drops_empty = "red") ## End(Not run)## Not run: plate <- new_plate(sample_data_dir()) plot(plate) plate <- plate %>% analyze plot(plate) plot(plate, "A01:C05", show_drops_empty = TRUE, col_drops_empty = "red") ## End(Not run)
Same plot as plot.pnpp_experiment but with a few extra
features that are specific to wildtype/mutant PNPP plates. Take a look
at plot.pnpp_experiment to see all supported parameters
and more information.
## S3 method for class 'wildtype_mutant_pnpp' plot( x, wells, samples, ..., col_drops_mutant = "purple3", col_drops_wildtype = "green3", col_drops_rain = "black", show_mutant_freq = TRUE, text_size_mutant_freq = 4, alpha_drops_low_mutant_freq = 0.5, show_low_high_mut_freq = TRUE, bg_mutant = "purple3", bg_wildtype = "green3", alpha_bg_low_high_mut_freq = 0.1 )## S3 method for class 'wildtype_mutant_pnpp' plot( x, wells, samples, ..., col_drops_mutant = "purple3", col_drops_wildtype = "green3", col_drops_rain = "black", show_mutant_freq = TRUE, text_size_mutant_freq = 4, alpha_drops_low_mutant_freq = 0.5, show_low_high_mut_freq = TRUE, bg_mutant = "purple3", bg_wildtype = "green3", alpha_bg_low_high_mut_freq = 0.1 )
x |
A ddPCR plate. |
wells |
Only plot selected wells. Supports range notation, see
|
samples |
Only plot selected samples. |
... |
Parameters to pass to |
col_drops_mutant |
The colour to use for mutant droplets. |
col_drops_wildtype |
The colour to use for wildtype droplets. |
col_drops_rain |
The colour to use for rain droplets. |
show_mutant_freq |
If |
text_size_mutant_freq |
Text size of the printed mutant frequencies. |
alpha_drops_low_mutant_freq |
Transparency of mutant droplets in wells with mostly wildtype droplets. In wells where there are very few mutant droplets, it might be useful to make them more visible by increasing their transparency. |
show_low_high_mut_freq |
Differentiate between wells with a high vs low mutant frequency by having a different background colour to the well. |
bg_mutant |
The background colour for wells that have a significant mutant cluster. |
bg_wildtype |
The background colour for wells that have mostly wildtype drops. |
alpha_bg_low_high_mut_freq |
The transparency value for |
A ggplot2 plot object.
plot.ddpcr_plateplot.pnpp_experimentwildtype_mutant_pnpp
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) %>% analyze wells_wildtype(plate) plot(plate) plate <- plate %>% analyze plot(plate) plot(plate, "A01:C05", col_drops_rain = "blue") ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) %>% analyze wells_wildtype(plate) plot(plate) plate <- plate %>% analyze plot(plate) plot(plate, "A01:C05", col_drops_rain = "blue") ## End(Not run)
PNPP stands for "Positive-Negative;Positive-Positive", which is a reflection of the clusters of non-empty droplets in the wells. Use this plate type when your ddPCR data has three main clusters: double-negative (FAM-HEX-; empty droplets), double-positive (FAM+HEX+; represent the "PP" in PNPP), and singly-positive (either FAM+HEX- or HEX+FAM-; represent the "NP" in PNPP).
Every pnpp_experiment plate must define which dimension is its positive
dimension. The positive dimension is defined as the dimension that corresponds
to the dye that has a high fluoresence intensity in all non-empty droplets. The other
dimension is defined as the variable dimension. For example, assuming
the HEX dye is plotted along the X axis and the FAM dye is along the Y axis,
a FAM+/FAM+HEX+ plate will have "Y" as its positive dimension because both
non-empty clusters have FAM+ droplets. Similarly, a HEX+/FAM+HEX+ plate will
have "X" as its positive dimension.
The positive dimension must be set in order to use a pnpp_experiment.
It is not recommended to use this type directly; instead you should use one
of the subtypes (fam_positive_pnpp or
hex_positive_pnpp). If you do use this type directly,
you must set the positive dimension with positive_dim.
Plates with this type have the following analysis steps: INITIALIZE,
REMOVE_FAILURES, REMOVE_OUTLIERS, REMOVE_EMPTY,
CLASSIFY, RECLASSIFY.
Plates with this type have the following droplet clusters:
UNDEFINED, FAILED, OUTLIER, EMPTY (double-negative),
RAIN, POSITIVE, NEGATIVE.
See the README for more information on plate types.
plate_typesfam_positive_pnpphex_positive_pnppwildtype_mutant_pnpppositive_dimwells_positivewells_negativeanalyzeremove_failuresremove_outliersremove_emptyclassify_dropletsreclassify_droplets
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$pnpp_experiment) type(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$pnpp_experiment) type(plate) ## End(Not run)
Reset a ddPCR plate object back to its original state. After resetting a plate, all the analysis progress will be lost, but the original droplet data and plate metadata will be kept. Two common reasons to reset a plate are either to restart the analysis, or to re-analyze the plate as a different plate type.
reset(plate, type, params, keep_type = FALSE, keep_params = FALSE)reset(plate, type, params, keep_type = FALSE, keep_params = FALSE)
plate |
A ddPCR plate |
type |
A ddPCR plate type (see |
params |
List of parameters to set for the plate. Only advanced users
should consider using this feature. See |
keep_type |
If |
keep_params |
If |
A new unanalyzed ddPCR plate
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate <- reset(plate, type=plate_types$fam_positive_pnpp) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) plate <- reset(plate, type=plate_types$fam_positive_pnpp) ## End(Not run)
These functions return sample data files or folders and can be used to
load ddPCR plates with sample data. They are used primarily in the
documentation examples, but you can also use them for learning purposes.
There are two sample datasetes: a small dataset and a large dataset. The
small dataset contains the full raw data, but the large dataset only
includes the processed data because the raw data would be too large.
sample_data_dir: get the directory of the small or large sample datasetsample_data_file: get path to one of the data files in the small sample datasetsample_results_file: get path to the results file of the small sample datasetsample_plate: get the ddpcr plate object containing the data of the small or large dataset
sample_data_dir() sample_data_file() sample_results_file() sample_plate(size = c("small", "large"))sample_data_dir() sample_data_file() sample_results_file() sample_plate(size = c("small", "large"))
size |
The dataset to retrieve, either |
plate1 <- new_plate(dir = sample_data_dir()) plate2 <- new_plate(data_files = sample_data_file(), meta_file = sample_results_file()) plate3 <- sample_plate()plate1 <- new_plate(dir = sample_data_dir()) plate2 <- new_plate(data_files = sample_data_file(), meta_file = sample_results_file()) plate3 <- sample_plate()
Saves a plate to a file, including all its data, parameters, and current
analysis state. The file can be read back later using
load_plate. The file is not human-readable - if
you want to save the droplets data or the metadata of a plate, then first
retrieve the data using plate_data or
plate_meta and save it with
write.csv.
save_plate(plate, file)save_plate(plate, file)
plate |
Plate object to save. |
file |
Name of the file where the plate will be saved. |
The given plate, unchanged.
plate <- new_plate(sample_data_dir()) save_plate(plate, "myplate") unlink("myplate.rds")plate <- new_plate(sample_data_dir()) save_plate(plate, "myplate") unlink("myplate.rds")
Use this function to reset a ddPCR plate's parameters back to their default values.
set_default_params(plate)set_default_params(plate)
plate |
A ddPCR plate. |
The plate with the parameters set to the plate type's default values.
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) x_var(plate) <- "VIC" plate <- set_default_params(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) x_var(plate) <- "VIC" plate <- set_default_params(plate) ## End(Not run)
Every ddPCR plate type has an ordered set of steps that are run to analyze
the data. You can run all the steps with analyze or
run the analysis step by step with next_step. The order
of the steps in the list is the order in which they are run on the dataset.
steps(plate)steps(plate)
plate |
a ddPCR plate. |
A named character vector, where every name is the human-readable name of an analysis step, and every value is the name of the function used to perform the step.
## Not run: dir <- sample_data_dir() new_plate(dir) %>% steps new_plate(dir, plate_types$fam_positive_pnpp) %>% steps ## End(Not run)## Not run: dir <- sample_data_dir() new_plate(dir) %>% steps new_plate(dir, plate_types$fam_positive_pnpp) %>% steps ## End(Not run)
Select specific wells or samples from a ddPCR plate.
## S3 method for class 'ddpcr_plate' subset(x, wells, samples, targets_ch1, targets_ch2, ...)## S3 method for class 'ddpcr_plate' subset(x, wells, samples, targets_ch1, targets_ch2, ...)
x |
The ddPCR plate to subset from. |
wells |
Vector or range notation of wells to select (see Range Notation section for more information). |
samples |
Vector of sample names to select. |
targets_ch1 |
Vector of target names in channel 1 to select. |
targets_ch2 |
Vector of target names in channel 2 to select. |
... |
Ignored |
Keeps only data from the selected wells. If sample names are provided instead of well IDs, then any well corresponding to any of the sample names will be kept. Either well IDs or sample names must be provided, but not both.
Plate with data only from the specified wells/samples.
The most basic way to select wells is to provide a vector of wells such as
c("B03", "C12"). When selecting wells, a special range notation is
supported to make it easier to select many wells: use a colon (:) to specify a
range of wells, and use a comma (,) to add another well or range. When
specifying a range, all wells in the rectangular area between the two wells
are selected. For example, B04:D06 is equivalent to
B04, B05, A05, C04, C05, C06, D04, D05, D06. You can combine multiple
ranges in one selection; see the Examples section below. Note that this
notation is only supported for the wells parameter, but not for the
samples parameter.
plate <- new_plate(sample_data_dir()) plate %>% wells_used plate %>% subset("C01") %>% wells_used plate %>% subset(c("C01", "F05")) %>% wells_used plate %>% subset("C01, F05") %>% wells_used plate %>% subset("C01:F05") %>% wells_used plate %>% subset("C01:F05, A01") %>% wells_used plate %>% subset("A01:C03") %>% wells_used plate %>% subset("A01:C05") %>% wells_used plate %>% subset("A01, A05:F05") %>% wells_used plate %>% subset("A01, A05:C05, F05") %>% wells_used plate %>% subset("A01:A05, C01:C05, F05") %>% wells_used plate %>% subset(samples = "Dean") %>% wells_used plate %>% subset(samples = c("Dean", "Mike")) %>% wells_usedplate <- new_plate(sample_data_dir()) plate %>% wells_used plate %>% subset("C01") %>% wells_used plate %>% subset(c("C01", "F05")) %>% wells_used plate %>% subset("C01, F05") %>% wells_used plate %>% subset("C01:F05") %>% wells_used plate %>% subset("C01:F05, A01") %>% wells_used plate %>% subset("A01:C03") %>% wells_used plate %>% subset("A01:C05") %>% wells_used plate %>% subset("A01, A05:F05") %>% wells_used plate %>% subset("A01, A05:C05, F05") %>% wells_used plate %>% subset("A01:A05, C01:C05, F05") %>% wells_used plate %>% subset(samples = "Dean") %>% wells_used plate %>% subset(samples = c("Dean", "Mike")) %>% wells_used
For ddPCR plates of type custom_thresholds, get or set the thresholds
that divide the four droplet quadrants.
thresholds(plate) thresholds(plate) <- value set_thresholds(plate, value)thresholds(plate) thresholds(plate) <- value set_thresholds(plate, value)
plate |
A ddPCR plate. |
value |
The new thresholds as a 2-element numeric vector |
The current thresholds
custom_thresholdsx_thresholdy_threshold
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) thresholds(plate) thresholds(plate) <- c(5500, 8000) set_thresholds(plate, c(5500, 8000)) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) thresholds(plate) thresholds(plate) <- c(5500, 8000) set_thresholds(plate, c(5500, 8000)) ## End(Not run)
Get the type of a ddPCR plate. See the README for more information on plate types.
type(plate, all = FALSE)type(plate, all = FALSE)
plate |
A ddPCR plate |
all |
If |
A character vector with the plate type(s).
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) type(plate) type(plate, TRUE) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) type(plate) type(plate, TRUE) ## End(Not run)
Each ddPCR plate has associated metadata that stores infromation for every well. Use this function to retrieve any metadata information for a single well or for a list of wells.
well_info(plate, well_ids, var)well_info(plate, well_ids, var)
plate |
A ddPCR plate |
well_ids |
A character vecotr of well IDs denoting the wells to get information for |
var |
The metadata variable to get (to see a list of all possible metadata
variables, use |
A character vector with the wanted metadata variable value for each well.
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) well_info(plate, "A01", "drops") ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) well_info(plate, "A01", "drops") ## End(Not run)
After a ddPCR plate of type wildtype_mutant_pnpp has been analyzed,
get the wells that were deemed as mutant.
wells_mutant(plate)wells_mutant(plate)
plate |
A ddPCR plate. |
Character vector with well IDs of mutant wells
wildtype_mutant_pnppwells_wildtype
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) %>% analyze wells_mutant(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) %>% analyze wells_mutant(plate) ## End(Not run)
After a ddPCR plate of type pnpp_experiment has been analyzed,
get the wells that were not deemed as having mostly positive droplets.
wells_negative(plate)wells_negative(plate)
plate |
A ddPCR plate. |
Character vector with well IDs of negative wells
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$pnpp_experiment) %>% analyze wells_negative(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$pnpp_experiment) %>% analyze wells_negative(plate) ## End(Not run)
After a ddPCR plate of type pnpp_experiment has been analyzed,
get the wells that were deemed as having mostly positive droplets.
wells_positive(plate)wells_positive(plate)
plate |
A ddPCR plate. |
Character vector with well IDs of positive wells
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$pnpp_experiment) %>% analyze wells_positive(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$pnpp_experiment) %>% analyze wells_positive(plate) ## End(Not run)
Get a list of wells that had successful or failed ddPCR runs. One of the analysis steps for ddPCR plates includes identifying failed wells, which are wells where the ddPCR run was not successful and did not produce useful droplet data.
wells_success(plate) wells_failed(plate)wells_success(plate) wells_failed(plate)
plate |
A ddPCR plate |
List of wells that had a successful/failed ddPCR run.
## Not run: dir <- sample_data_dir() plate <- new_plate(dir) %>% analyze plate %>% wells_success plate %>% wells_failed ## End(Not run)## Not run: dir <- sample_data_dir() plate <- new_plate(dir) %>% analyze plate %>% wells_success plate %>% wells_failed ## End(Not run)
Get a list of the wells that have any data in a ddPCR plate.
wells_used(plate)wells_used(plate)
plate |
A ddPCR plate |
List of wells that have any data in the given plate.
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) wells_used(plate) plate <- subset(plate, "A01:C05") wells_used(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) wells_used(plate) plate <- subset(plate, "A01:C05") wells_used(plate) ## End(Not run)
After a ddPCR plate of type wildtype_mutant_pnpp has been analyzed,
get the wells that were deemed as wildtype.
wells_wildtype(plate)wells_wildtype(plate)
plate |
A ddPCR plate. |
Character vector with well IDs of wildtype wells
wildtype_mutant_pnppwells_mutant
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) %>% analyze wells_wildtype(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$fam_positive_pnpp) %>% analyze wells_wildtype(plate) ## End(Not run)
A plate of type wildtype_mutant_pnpp is a subtype of
pnpp_experiment that assumes the double-positive cluster
denotes wildtype and the other non-empty cluster denotes mutant droplets.
There are two plate types that are subtypes of wildtype_mutant_pnpp:
fam_positive_pnpp and hex_positive_pnpp.
It is not recommended to use this type directly; instead you should use one
of the subtypes.
Plates with this type have the following analysis steps: INITIALIZE,
REMOVE_FAILURES, REMOVE_OUTLIERS, REMOVE_EMPTY,
CLASSIFY, RECLASSIFY.
Plates with this type have the following droplet clusters:
UNDEFINED, FAILED, OUTLIER, EMPTY (double-negative),
RAIN (not empty but not wildtype nor negative), POSITIVE (wildtype),
NEGATIVE (mutant).
See the README for more information on plate types.
plate_typesfam_positive_pnpphex_positive_pnpppnpp_experimentanalyzeremove_failuresremove_outliersremove_emptyclassify_dropletsreclassify_droplets
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$wildtype_mutant_pnpp) type(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$wildtype_mutant_pnpp) type(plate) ## End(Not run)
For ddPCR plates of type custom_thresholds, get or set the threshold
along the X axis that divides the droplet quadrants.
x_threshold(plate) x_threshold(plate) <- valuex_threshold(plate) x_threshold(plate) <- value
plate |
A ddPCR plate. |
value |
The new X threshold |
The current X threshold
custom_thresholdsy_thresholdthresholds
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) x_threshold(plate) x_threshold(plate) <- 5500 plot(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) x_threshold(plate) x_threshold(plate) <- 5500 plot(plate) ## End(Not run)
By default, the dye visualized along the X axis is HEX and the dye visualized along the Y axis is FAM. You can use these functions to get or set these values if your plate uses different dyes.
x_var(plate) y_var(plate) x_var(plate) <- value y_var(plate) <- valuex_var(plate) y_var(plate) x_var(plate) <- value y_var(plate) <- value
plate |
A ddPCR plate |
value |
New dye name |
The X/Y variables are simply parameters in the plate, which can also be accessed
or changed using params. You should use these functions
to change the X/Y variable rather than changing the parameters directly.
Dye name
## Not run: plate <- new_plate(sample_data_dir()) x_var(plate) x_var(plate) <- "VIC" x_var(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir()) x_var(plate) x_var(plate) <- "VIC" x_var(plate) ## End(Not run)
For ddPCR plates of type custom_thresholds, get or set the threshold
along the Y axis that divides the droplet quadrants.
y_threshold(plate) y_threshold(plate) <- valuey_threshold(plate) y_threshold(plate) <- value
plate |
A ddPCR plate. |
value |
The new Y threshold |
The current Y threshold
custom_thresholdsx_thresholdthresholds
## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) y_threshold(plate) y_threshold(plate) <- 8000 plot(plate) ## End(Not run)## Not run: plate <- new_plate(sample_data_dir(), type = plate_types$custom_thresholds) y_threshold(plate) y_threshold(plate) <- 8000 plot(plate) ## End(Not run)