Type:	Package
Title:	Digital PCR Analysis
Version:	0.6
LazyData:	true
Date:	2025-06-12
Description:	Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.
License:	GPL-3
URL:	https://github.com/michbur/dpcR
BugReports:	https://github.com/michbur/dpcR/issues
Depends:	R (≥ 3.0.0), methods
Imports:	binom, chipPCR, e1071, evd, dgof, multcomp, qpcR, pracma, rateratio.test, readxl, signal, shiny, spatstat.explore, spatstat.geom
Suggests:	digest, DT, ggplot2, knitr, markdown, rhandsontable, rmarkdown, shinythemes, xtable
VignetteBuilder:	knitr
Packaged:	2025-06-17 04:13:43 UTC; User
NeedsCompilation:	no
Repository:	CRAN
Encoding:	UTF-8
RoxygenNote:	6.1.1
Collate:	'AUCtest.R' 'BioradCNV.R' 'White.R' 'adpcr2panel.R' 'adpcr2ppp.R' 'binarize.R' 'classes.R' 'bind_dpcr.R' 'bioamp.R' 'calc_breaks.R' 'calc_coordinates.R' 'calc_lambda.R' 'compare_dens.R' 'count_test-class.R' 'create_dpcr.R' 'dPCRmethyl.R' 'ddpcRquant.R' 'df2dpcr.R' 'dpcR-package.R' 'dpcReport_gui.R' 'dpcr2df.R' 'dpcr_calculator.R' 'dpcr_density.R' 'dpcr_density_gui.R' 'dpcr_density_table.R' 'extract_run.R' 'fit_adpcr.R' 'fl.R' 'get_k_n.R' 'hsm.R' 'limit_cq.R' 'many_peaks.R' 'modlist.R' 'moments.R' 'num2int.R' 'pds.R' 'pds_raw.R' 'plot_distr.R' 'plot_panel.R' 'plot_vf_circ.R' 'plot_vic_fam.R' 'print_summary.R' 'qdpcr.R' 'qpcr2pp.R' 'qpcr_analyser.R' 'read_amp.R' 'read_dpcr.R' 'rename_dpcr.R' 'rtadpcr.R' 'safe_efficiency.R' 'show_dpcr.R' 'sim_adpcr.R' 'sim_ddpcr_bkm.R' 'sim_dpcr.R' 'simulations.R' 'six_panels.R' 'summary_dpcr.R' 'test_counts.R' 'test_counts_gui.R' 'test_panel.R' 'test_peaks.R' 'test_pooled.R' 'valid_amp.R' 'y_val_conf.R'
Date/Publication:	2025-06-18 08:40:30 UTC
Author:	Michal Burdukiewicz [aut], Stefan Roediger [aut], Bart Jacobs [aut], Piotr Sobczyk [ctb], Andrej-Nikolai Spiess [cre, ctb]
Maintainer:	Andrej-Nikolai Spiess <draspiess@gmail.com>

Digital PCR Analysis

Description

The dpcR package is a collection of functions for a digital Polymerase Chain Reaction (dPCR) analysis. dPCR comprises methods to quantify nucleic acids, copy number variations (CNV), homo- and heterozygosity, as well as rare mutations (including single nucleotide polymorphisms (SNP)). The chemical basis of dPCR is similar to conventional PCR but the reaction-mix is divided into hundredths to thousands of small compartments with parallel amplifications reactions. The analysis is based on counting the number of positive compartments and relating it to the total number of compartments by means of Poission statistics which enables an absolute quantification.

Details

The package includes plot functions, summary functions, data sets and simulations for dPCR and customizable GUI for droplet digital PCRs and array-based digital PCRs. We aim to include all statistical approaches published in peer-review literature and additional selected sources of expertise currently available. We intent to make these methods available to the scientific community in an open and cross-platform environment. Using the naming convention derived from the MIQE Guidelines for digital PCR, we hope to become a reference to a unified nomenclature in dpcR.

The package is primarily targeted at researchers who wish to use it with an existing technology or during the development of novel digital PCR systems. In addition the dpcR package provides interactive tools that can be used to better learn about digital PCR concepts and data interpretation.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

Maintainer: Michal Burdukiewicz <michalburdukiewicz@gmail.com>

References

Huggett J, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, Hellemans J, Kubista M, Mueller RD, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT, Bustin SA The Digital MIQE Guidelines: Minimum Information for Publication of Quantitative Digital PCR Experiments Clinical Chemistry, 2013. 59(6): p.892-902.

Vogelstein B, Kinzler KW, Digital PCR. PNAS, 1999. 96(16): p. 9236-9241.

Examples

adpcr <- sim_adpcr(m = 400, n = 765, times = 20, pos_sums = FALSE, n_panels = 1)
plot_panel(adpcr, col = "green")
pos_chambers <- sum(adpcr > 0)
dpcr_density(k = pos_chambers, n = 765)

Copy number variation experiment

Description

Copy Number Variation of MYC Gene for seven patients measured using QX200 (Bio-Rad)

Format

An object of class adpcr with "tnp" type containing four runs from seven experiments (four runs per each experiment).

Author(s)

Robert M. Dorazio, Margaret E. Hunter.

Source

Dorazio RM, Hunter ME, Statistical Models for the Analysis and Design of Digital Polymerase Chain Reaction (dPCR) Experiments. Analytical Chemistry 2015. 87(21): p.10886-10893

Examples

data(BioradCNV)
summary(BioradCNV)

Digitalized Data from a Fluidigm Array

Description

These are the results data from the White data as measured by the UT digital PCR on Fluidigm 12.765 digital Array. The data were digtilized from a supplementary figure "1471-2164-10-116-S1.pdf" by White et al. (2009) BMC Genomics

Format

A dataframe with 9180 rows and 10 columns.

Image_position

Position of an array in the figure 1471-2164-10-116-S1.pdf from White et al. (2009) BMC Genomics (e.g., 11 is the image in the first colum and the first row, 24 is second column and fourth image)

Sample

is the sample (e.g., "Ace 1:100") as described by White et al. (2009) BMC Genomics

X.1

Running index for *all* samples

Index

Index within an array

Row

Row within an array

Column

Column within an array

Area

is the area that was measured with "MicroArray Profile"

Min

is the minimum intensity of an area that was measured with "MicroArray Profile"

Max

is the maximum intensity of an area that was measured with "MicroArray Profile"

Mean

is the mean intensity of an area that was measured with "MicroArray Profile"

Details

Setup: Experimental details were described be White et al. (2009) BMC Genomics. The digitalization of the figure was done with imageJ and the "MicroArray Profile" plugin by Bob Dougherty (rpd@optinav.com) and Wayne Rasband.

Annotation: See the White et al. (2009) BMC Genomics paper for details.

Author(s)

Stefan Roediger, Michal Burdukiewcz, White et al. (2009) BMC Genomics

Source

Data were digitalized from the supplement material (Additional file 1. dPCR analysis of mock library control.) "1471-2164-10-116-S1.pdf" by White et al. (2009) BMC Genomics

References

White RA, Blainey PC, Fan HC, Quake SR. Digital PCR provides sensitive and absolute calibration for high throughput sequencing. BMC Genomics 2009;10:116. doi:10.1186/1471-2164-10-116.

Dougherty B, Rasband W. MicroArray Profile ImageJ Plugin n.d. http://www.optinav.com/imagej.html (accessed August 20, 2015).

Examples

str(White)
oldpar <- par(mfrow = c(1, 1))
par(mfrow = c(3,3))
White_data <- sapply(unique(White[["Image_position"]]), function(i)
	     White[White[["Image_position"]] == i, "Mean"])
assays <- sapply(unique(White[["Image_position"]]), function(i)
	 unique(White[White[["Image_position"]] == i, "Sample"]))
White_adpcr <- create_dpcr(White_data > 115, n = 765, assay = assays, 
		   type = "np", adpcr = TRUE)
White_k <- colSums(White_data > 115)
sapply(2:4, function(i) {
   plot_panel(extract_run(White_adpcr, i))

   # Create the ECDF of the image scan data to define
   # a cut-off for positive and negative partitions
   # Plot the ECDF of the image scan data an define a cut-off
   plot(ecdf(White_data[, i]), main = paste0("ECDF of Image Scan Data\n", assays[i]),
   xlab = "Grey value", ylab = "Density of Grey values")
   abline(v = 115, col = 2, cex = 2)
   text(80, 0.5, "User defined cut-off", col = 2, cex = 1.5)

   # Plot the density of the dPCR experiment
   dpcr_density(k = White_k[i], n = 765, bars = TRUE)
}
)

par(oldpar)

Class "adpcr" - end-point array digital PCR experiments

Description

A class specifically designed to contain results from end-point array digital PCR experiments. Data is represented as matrix, where each column describes different experiment. Type of data in all columns is specified in slot "type". Inherits from dpcr.

Details

For more in-depth explanation of digital PCR data structure, see dpcr.

Slots

col_names

"character" vector naming columns in the array.

row_names

"character" vector naming rows in the array.

row_id

"integer" vector providing row indices of all runs.

col_id

"integer" vector providing column indices of all runs.

panel_id

"factor" naming the panel to which experiment belong.

Author(s)

Michal Burdukiewicz.

See Also

Data management: adpcr2panel, bind_dpcr, extract_run.

Plotting: plot_panel.

Tests: test_panel.

Simulation: sim_adpcr.

Real-time array digital PCR: rtadpcr.

Droplet digital PCR: dpcr.

Examples

rand_array <- sim_adpcr(400, 1600, 100, pos_sums = FALSE, n_panels = 5)
one_rand_array <- extract_run(rand_array, 1)
plot_panel(one_rand_array, 40, 40)

Convert adpcr object to array

Description

Converts adpcr object into the list of array-like matrices.

Usage

adpcr2panel(input, breaks = FALSE)

Arguments

Value

A named list of length equal to the number of arrays in the input. Each element is a single array in matrix-like form, where dimensions are set exactly as in case of the real plate. Names of the list corresponds to the names of assays ("tnp" data) or runs (any other type of adpcr data).

The matrices contain values from array, either integers (when use_break is FALSE) or characters (when use_break is TRUE).

Author(s)

Michal Burdukiewicz.

Examples

#generate data
ttest <- sim_adpcr(m = 400, n = 765, times = 20, pos_sums = FALSE, 
                   n_panels = 3)
#convert object into three arrays
arrays <- adpcr2panel(ttest)
length(arrays)
#print an array
arrays[[1]]

Convert adpcr to ppp

Description

Converts adpcr object to the list of ppp.objects.

Usage

adpcr2ppp(input, marks = TRUE, plot = FALSE)

Arguments

Details

Each array is independently converted by ppp function. marks attached to each point represent values contained by the adpcr object.

Value

A list containing objects with class ppp.object with the length equal to the number of arrays (minimum 1).

Author(s)

Michal Burdukiewcz, Stefan Roediger.

Examples

many_panels <- sim_adpcr(m = 400, n = 765, times = 1000, pos_sums = FALSE, 
                   n_panels = 5)

# Convert all arrays to ppp objects
adpcr2ppp(many_panels)

# Convert all arrays to ppp objects and get third plate
third_plate <- adpcr2ppp(many_panels)[[3]]

# Convert only third plate to ppp object
third_plate2 <- adpcr2ppp(extract_run(many_panels, 3))

# Check the class of a new object
class(third_plate2)

# It's a list with the length 1. The third plate is a first element on this 
#list
class(third_plate2[[1]])

Binarize digital PCR data

Description

Transforms multinomial (number of molecules per partition) or continuous (fluorescence) digital PCR data to binary (positive/negative partition) format.

Usage

binarize(input)

Arguments

Value

object of the class adpcr or dpcr (depending on input) with type "np".

Author(s)

Michal Burdukiewicz.

Examples

#adpcr object
rand_array <- sim_adpcr(200, 300, 100, pos_sums = FALSE, n_panels = 1)
binarize(rand_array)

#dpcr object
rand_droplets <- sim_dpcr(200, 300, 100, pos_sums = FALSE, n_exp = 1)
binarize(rand_droplets)

Bind dpcr objects

Description

A convinient wrapper around cbind and rbind tailored specially for binding multiple objects containing results from digital PCR experiments.

Usage

bind_dpcr(input, ...)

Arguments

Details

In case of adpcr or dpcr objects, bind_dpcr works analogously to cbind, but without recycling. In case on unequal length, shorter objects will be filled in with additional NA values. The original length is always preserved in n slot.

bind_dpcr automatically names binded experiments using format x.y, where x is number of object passed to function and y is a number of experiment in a given object.

Value

An object of class adpcr or dpcr, depending on the input.

Note

Because bind_dpcr calls do.call function, binding together at the same time more than 500 objects can lead to 'stack overflow' error.

Author(s)

Michal Burdukiewicz

See Also

Opposite function: extract_run

Examples

bigger_array <- sim_adpcr(400, 765, 1000, pos_sums = FALSE, n_panels = 5)
smaller_array <- sim_adpcr(100, 700, 1000, pos_sums = FALSE, n_panels = 3)
bound_arrays <- bind_dpcr(bigger_array, smaller_array)

smaller_droplet <- sim_dpcr(m = 7, n = 20, times = 5, n_exp = 2)
bigger_droplet <- sim_dpcr(m = 15, n = 25, times = 5, n_exp = 4)
biggest_droplet <- sim_dpcr(m = 15, n = 35, times = 5, n_exp = 1)
bound_droplets <- bind_dpcr(smaller_droplet, bigger_droplet, biggest_droplet)

Analyze and plot a Bio-Rad droplet digital PCR experiment

Description

bioamp is a function to plot and analyze the amplitude data of a Bio-Rad droplet digital PCR experiment.

Usage

bioamp(data = data, amp_x = 1, amp_y = 2, cluster = 3,
  robust = TRUE, plot = TRUE, stat = TRUE,
  xlab = "Assay 1 Amplitude", ylab = "Assay 2 Amplitude", ...)

Arguments

Value

A matrix with the summary results together with the raw data plot.

Author(s)

Stefan Roediger, Michal Burdukiewcz

Examples

oldpar <- par(mfrow = c(1, 1))
par(mfrow = c(1,2))
bioamp(data = pds_raw[["D01"]], main = "Well D01", pch = 19)
bioamp(data = pds_raw[["D02"]], main = "Well D02", pch = 19)
par(oldpar)

Calculate Array Coordinates

Description

Calculates coordinates of points on plot to represent a digital PCR array.

Usage

calc_coordinates(array, half)

Arguments

Value

Returns two sets of coordinates of each microfluidic well: coords is a list of coordinates suitable for usage with functions from graphics package. The second element is a data frame of coordinates useful for users utilizing ggplot2 package.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

plot_panel - plots adpcr data. adpcr2panel - converts adpcr object to arrays.

Plot and Compare Densities

Description

Plots empirical and theoretical density of the digital PCR experiment.

Usage

compare_dens(input, moments = TRUE, ...)

Arguments

Value

A density plot.

Author(s)

Michal Burdukiewcz.

See Also

moments is used to calculate moments of Poisson distribution.

Examples

adpcr_big <- sim_adpcr(m = 35, n = 40, times = 50, pos_sums = FALSE, n_panels = 1)
compare_dens(adpcr_big, moments = TRUE)

Class "count_test"

Description

Objects of this class are created by test_counts.

Usage

## S4 method for signature 'count_test'
summary(object)

## S4 method for signature 'count_test'
coef(object)

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

## S4 method for signature 'count_test,ANY'
plot(x, aggregate = FALSE, nice = TRUE)

Arguments

Details

In case of the aggregated plot, mean confidence intervals for groups are presented as dashed lines.

Methods (by generic)

• summary: Summary statistics of assigned groups.

• coef: Extract coefficients of groups.

• show: Print both group_coef and test_res.

• plot: plots mean number of molecules per partition and its confidence intervals.

Slots

group_coef

"data.frame" containing experiments, groups to which they belong and calculated values of rate (lambda).

test_res

"matrix" containing result of multiple comparisions t-test.

model

"character" name of GLM used to compare experiments.

Author(s)

Michal Burdukiewicz.

See Also

test_counts.

Create dpcR object

Description

Creates adpcr and dpcr objects from data.

Usage

create_dpcr(data, n, exper = "Experiment 1", replicate = NULL,
  assay = "Unknown", type, v = 1, uv = 0, threshold = NULL, adpcr,
  col_names = NULL, row_names = NULL, panel_id = NULL)

Arguments

Details

This constructor function assists in creation of objects used by other functions of the package. It is also responsible for checking the correctness of arguments.

A warning is prompted whenever any of arguments is converted to other type.

Value

An adpcr or dpcr object.

Note

create_dpcr is a preferred to calling directly new.

Currently only end-point measurements are supported.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

Streamlined, but more limited version: df2dpcr

Examples

# Droplet digital PCR example
sample_runs <- matrix(rpois(60, lambda = 1.5), ncol = 2)
ddpcr1 <- create_dpcr(sample_runs[,1], n = 30L, 
threshold = 1, type = "nm", adpcr = FALSE)
ddpcr2 <- create_dpcr(sample_runs[,2], n = 30L, 
threshold = 1, type = "nm", adpcr = FALSE)
plot_vic_fam(ddpcr1, ddpcr2)

# Array digital PCR example
sample_adpcr <- create_dpcr(rpois(765, lambda = 0.8), n = 765L, 
			    type = "nm", adpcr = TRUE)
plot_panel(sample_adpcr, 45, 17)

Methylated human gDNA

Description

Results of dPCR experiment: serial dilutions (0.25. 0.50, 0.75, 1.00) of methylated human gDNA. For each concentration level, three samples were measured using QX100 (Bio-Rad).

Format

A adpcr object

Author(s)

Mario Menschikowski, Stefan Roediger, Michal Burdukiewcz

Source

Mario Menschikowski group / Institute of Clinical Chemistry and Laboratory Medicine, TU-Dresden, Dresden, Germany

Examples

summary(dPCRmethyl)

Quantify droplets

Description

Cluster raw data from QX100 and QX200 systems.

Usage

ddpcRquant(path, threshold.int = 0.9995, reps = 10, blocks = 150,
  threshold.manual = NULL)

Arguments

Value

dpcr object.

Note

This function is a modification of code was implemented using the code found on https://ddpcrquant.ugent.be/.

Author(s)

Wim Trypsteen, Matthijs Vynck, Jan De Neve, Pawel Bonczkowski, Maja Kiselinova, Eva Malatinkova, Karen Vervisch, Olivier Thas, Linos Vandekerckhove, Ward De Spiegelaere.

References

Trypsteen W, Vynck M, De Neve J, Bonczkowski P, Kiselinova M, Malatinkova E, Vervisch K, Thas O, Vandekerckhove L, De Spiegelaere W, ddpcRquant: Threshold Determination for Single Channel Droplet Digital PCR Experiments. Analytical and Bioanalytical Chemistry 2015. 407(19): p.5827-34.

Convert data.frame to dpcr object

Description

Converts data.frame object to to adpcr or dpcr object. The resulting object will have "tnp" type.

Usage

df2dpcr(df)

Arguments

Details

The data frame must have REDF structure. It means that data must contain following columns with exactly specified names:

experiment

names of experiments

replicate

indices of replicates

assay

names of assays

k

number of positive partitions

n

total number of partitions

v

volume of partition (nL)

uv

uncertainty of partition's volume (nL)

threshold

partitions with k equal or higher than threshold are treated as positve.

There are also one optional column:

panel_id

indices of panels

If the additional column is present, the resulting object has adpcr type.

Value

An object of adpcr or dpcr type, depends on the presence of additional column with panel indices (see Details).

Author(s)

Michal Burdukiewcz, Stefan Roediger

See Also

Flexibly create dpcr objects: create_dpcr Inverse function: dpcr2df

Examples

dat <- data.frame(experiment = factor(rep(paste0("Experiment", 1L:2), 3)),
                  replicate = c(1, 1, 2, 2, 3, 3),
                  assay = "Assay1",
                  k = c(55, 121, 43, 150, 70, 131),
                  n = 765,
                  v = 1,
                  uv = 0)
df2dpcr(dat)

Digital PCR Report Graphical User Interface

Description

Launches graphical user interface that generates reports from digital PCR data.

Usage

dpcReport()

Value

A Shiny GUI with a report.

Warning

Any ad-blocking software may cause malfunctions.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

Class "dpcr" - general digital PCR

Description

A class containing results of any digital PCR experiment. Type of data in all columns is specified in slot "type".

Details

Possible type values of dpcr objects:

1. "ct": cycle threshold of each partition,

2. "fluo": fluorescence of each partition,

3. "nm": number of molecules in each partition,

4. "np": status (positive (1) or negative(0)) of each partition,

5. "tnp": total number of positive partitions in the run (single value per each run, not per partition).

Digital PCR data is always a matrix, where columns and rows represent respectively runs and data points. For example, matrix with 2 columns and 765 rows means two runs with 765 data points each. In case of "tnp" data, each run is represented by only one measurement, the count of all positive partitions.

The number of partitions is defined in slot n. In the previous example, two runs have 765 data points, but they can have less detected partitions (for example some reads may be not available). In this case, the data point will have value NA.

The structure of dpcr class is described more deeply in the vignette.

Slots

.Data

matrix data from digital PCR experiments. See Details.

n

integer equal to the number of partitions in each run.

exper

factor the id or name of experiments.

replicate

factor the id or name of replicates.

assay

factor the id or name of the assay.

v

"numeric" volume of the partition [nL].

uv

"numeric" uncertainty of the volume of the partition [nL].

threshold

"numeric" value specifying the threshold. Partition with the value equal or bigger than threshold are considered positive.

type

Object of class "character" defining type of data. See Details.

Note

This class represent the most general droplet-based digital PCR. In more specific cases, the user is directed to other classes: adpcr, where results can be placed over a plate, qdpcr where digital assay is based on multiple qPCR experiments and rtadpcr, where data points represent the status of partitions measured in the real time.

Author(s)

Michal Burdukiewicz.

Examples

dpcr_fluo <- sim_dpcr(m = 10, n = 20, times = 5, fluo = list(0.1, 0))
plot(dpcr_fluo)

dpcr <- sim_dpcr(m = 10, n = 20, times = 5)

Convert dpcr object to data frame

Description

Converts adpcr or dpcr object to data.frame object.

Usage

dpcr2df(input)

Arguments

Value

data frame with 5 (if input was dpcr) or 8 columns (if input was adpcr).

Author(s)

Michal Burdukiewcz, Stefan Roediger

See Also

Inverse function: df2dpcr

Examples

dpcr2df(six_panels)

Calculate Density of Single dPCR Run

Description

Calculates and plots the density of the number of positive molecules or the average number of molecules per partition. Can be used for both array digital PCR and droplet digital PCR.

Usage

dpcr_density(k, n, average = FALSE, methods = "wilson",
  conf.level = 0.95, plot = TRUE, bars = FALSE, ...)

Arguments

Value

A data frame with one row containing bounds of the confidence intervals and a name of the method used to calculate them.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

References

Brown, Lawrence D., T. Tony Cai, and Anirban DasGupta. Confidence Intervals for a Binomial Proportion and Asymptotic Expansions. The Annals of Statistics 30, no. 1 (February 2002): 160–201.

See Also

Computation of confidence intervals: binom.confint,

The browser-based graphical user interface for this function: dpcr_density_gui.

Examples

# Calculate the average number of molecules per partition and show the area
# of the confidence interval (left plot) and the area within the 
# confidence interval
oldpar <- par(mfrow = c(1,1))
par(mfrow = c(1,2))
dpcr_density(k = 25, n = 55, average = TRUE, methods = "wilson", 
	     conf.level = 0.95)
dpcr_density(k = 25, n = 55, average = TRUE, methods = "wilson", 
	     conf.level = -0.95)
par(oldpar)

# By setting average to FALSE the total number of positive molecules is 
# calculated
dpcr_density(k = 25, n = 55, average = FALSE, methods = "wilson", 
	     conf.level = 0.95)

Digital PCR Density Graphical User Interface

Description

Launches graphical user interface that allows calculating density of positive partitions distribution.

Usage

dpcr_density_gui()

Value

A Shiny GUI for density calculation.

Warning

Any ad-blocking software may cause malfunctions.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

dpcr_density.

Calculate Density of Multiple dPCR runs

Description

Calculates the density of the number of positive molecules or the average number of molecules per partition of dpcr objects.

Usage

dpcr_density_table(input, average = FALSE, methods = "wilson",
  conf.level = 0.95)

Arguments

Value

A list (with the length equal to the number of runs in input) of data frames containing densities and borders of confidence intervals.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

dpcr_density for easy analysis and plots of single runs.

Examples

dens <- dpcr_density_table(six_panels)

# create plot using ggplot2
library(ggplot2)

ggplot(dens[["Experiment2.2"]], aes(x = x, y = y)) + 
  geom_line() + 
  geom_area(aes(fill = !(conf_up | conf_low))) +
  scale_y_continuous("Density") +
  scale_fill_discrete("0.95 CI")

Extract Assays or Experiments

Description

Extract all runs belonging to specific assay or experiment(s) from a dpcr object while preserving all other attributes.

Usage

extract_dpcr(input, id_exper = NULL, id_assay = NULL)

Arguments

Value

The object of the input's class (adpcr or dpcr).

Note

The standard Extract operator x[i] treats dpcr objects as matrix and extracts values without preserving other attributies of the object.

Author(s)

Michal Burdukiewicz.

See Also

Extract run(s): extract_run.

Examples

#extract using only experiment's ID
extract_dpcr(six_panels, id_exper = 1)

#extract using assay name
extract_dpcr(six_panels, id_assay = "MYC")

#extract using multiple names
extract_dpcr(six_panels, id_exper = c("Experiment1", "Experiment2"))

Extract Digital PCR Run

Description

Extract runs from a dpcr object while preserving all other attributes.

Usage

extract_run(input, id)

Arguments

Details

The extract_run function allows to choose one or more panels from an object of the adpcr or dpcr class and save it without changing other attributes. It is the most recommended method of extracting a subset from an array of panels, because it preserves class and structure of the object in contrary to standard operator Extract.

Value

The object of the input's class (adpcr or dpcr).

Note

The standard Extract operator x[i] treats dpcr objects as matrix and extracts values without preserving other attributies of the object.

Author(s)

Michal Burdukiewicz.

See Also

Opposite function: bind_dpcr Extract multiple runs belonging to an experiment of assay: extract_dpcr

Examples

#sample extracting
panels <- sim_adpcr(10, 40, 1000, pos_sums = FALSE, n_panels = 50)
single_panel <- extract_run(panels, 5)
random_three <- extract_run(panels, sample.int(ncol(panels), 3))
all_but_one <- extract_run(panels, -5)

#the same for fluorescence data
fluos <- sim_dpcr(10, 40, 1000, pos_sums = FALSE, n_exp = 50, 
                   fluo = list(0.1, 0))
single_fluo <- extract_run(fluos, 5)

Limit Cy0 values

Description

Calculates the Cq values of a qPCR experiment within a defined range of cycles. The function can be used to extract Cq values of a chamber based qPCR for conversion into a dPCR experiment. All Cq values are obtained by Second Derivative Maximum or by Cy0 method (Guescini et al. (2008)).

Usage

limit_cq(data, cyc = 1, fluo = NULL, Cq_range = c(1, max(data[cyc])),
  model = l5, SDM = TRUE, pb = FALSE)

Arguments

Details

The Cq_range for this function an be defined be the user. The default is to take all amplification curves into consideration. However, under certain circumstances it is recommended to define a range. For example if amplifications are positive in early cycle numbers (less than 10).

Approximated second derivative is influenced both by how often interpolation takes place in each data interval and by the smoothing method used. The user is encouraged to seek optimal parameters for his data himself. See inder for details.

The calculation of the Cy0 value (equivalent of Cq) is based on a five-parameter function. From experience this functions leads to good fitting and avoids overfitting of critical data sets. Regardless, the user is recommended to test for the optimal fitting function himself (see mselect for details).

Value

A data frame with two columns and number of rows equal to the number of runs analyzed. The column Cy0 contains calculated Cy0 values. The column in.range contains adequate logical constant if given Cy0 value is in user-defined Cq_range.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

References

Guescini M, Sisti D, Rocchi MB, Stocchi L & Stocchi V (2008) A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition. BMC Bioinformatics, 9: 326.

Ruijter JM, Pfaffl MW, Zhao S, et al. (2013) Evaluation of qPCR curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications. Methods, San Diego Calif 59:32–46.

See Also

SDM method: inder, summary.der.

Cy0 method: mselect, efficiency.

Examples

library(qpcR)
test <- cbind(reps[1L:45, ], reps2[1L:45, 2L:ncol(reps2)], reps3[1L:45, 
	      2L:ncol(reps3)])

# results.dPCR contains a column with the Cy0 values and a column with 
# converted values.
Cq.range <- c(20, 30)
ranged <- limit_cq(data = test, cyc = 1, fluo = NULL,
                           Cq_range = Cq.range, model = l5)
# Same as above, but without Cq.range
no_range <- limit_cq(data = test, cyc = 1, fluo = NULL, model = l5)

# Same as above, but only three columns
no_range234 <- limit_cq(data = test, cyc = 1, fluo = c(2:4), model = l5)

Artificial data set with many peaks

Description

A set of data from an artificial droplet flow experiment. It contains various kinds of peaks - positive peaks, negative peaks and peak-like noise.

Format

A data frame with 493 observations on the following 2 variables.

t

a numeric vector

F

fluorescence value

Examples

data(many_peaks)
plot(many_peaks, type = "b")

Calculate Moments of Poisson Distribution

Description

Computes moments of a Poisson distribution. The calculations are based on values of positive and total partitions or the theoretical lambda value.

Usage

moments(input)

Arguments

Value

A data frame with four columns: name of the experiment, name of the replicate, method of computation (theoretical or empirical), name of the moment and the value of the moment. The theoretical moments are computed using the lambda value and the empirical using the sample values.

Note

Four first moments of a Poisson distribution.

Mean : \lambda.

Variance: \lambda.

Skewness: \sqrt{\lambda}.

Kurtosis: \frac{1}{\lambda}.

Author(s)

Michal Burdukiewicz.

Examples

# moments for 100 positive partitions of 765 total partitions
moments(c(100, 765))

# calculate moments for an array digital PCR
moments(six_panels)

Convert numeric to integer

Description

Converts numeric values to positive integers with a warning.

Usage

num2int(x)

Arguments

Details

num2int uses as.integer functionality.

Value

The positive integer of x.

Examples

suppressWarnings(num2int(pi) == 3L)

Plasmid dilution series results

Description

These are the results data from the pds_raw data as calculated by the Bio-Rad QX100 Droplet Digital PCR System.

Format

A data frame with 64 observations on the following 44 variables.

Well

a factor with levels A01 to H04

ExptType

a factor with levels Absolute Quantification

Experiment

a factor with levels ABS

Sample

a factor with levels B B + P 10^2 gDNA gDNA + P 10^0 gDNA + P 10^1 gDNA + P 10^-1 gDNA + P 10^2 gDNA + P 10^3 gDNA + P 10^4

TypeAssay

a factor with levels Ch1NTC Ch1Unknown Ch2NTC Ch2Unknown

Assay

a factor with levels ileS styA

Status

a factor with levels Manual

Concentration

a numeric vector

TotalConfMax

a logical vector

TotalConfMin

a logical vector

PoissonConfMax

a numeric vector

PoissonConfMin

a numeric vector

Positives

a numeric vector

Negatives

a numeric vector

Ch1.Ch2.

a numeric vector

Ch1.Ch2..1

a numeric vector

Ch1.Ch2..2

a numeric vector

Ch1.Ch2..3

a numeric vector

Linkage

a numeric vector

AcceptedDroplets

a numeric vector

CNV

a logical vector

TotalCNVMax

a logical vector

TotalCNVMin

a logical vector

PoissonCNVMax

a logical vector

PoissonCNVMin

a logical vector

ReferenceCopies

a logical vector

UnknownCopies

a logical vector

Ratio

a numeric vector

TotalRatioMax

a logical vector

TotalRatioMin

a logical vector

PoissonRatioMax

a numeric vector

PoissonRatioMin

a numeric vector

FractionalAbundance

a numeric vector

TotalFractionalAbundanceMax

a logical vector

TotalFractionalAbundanceMin

a logical vector

PoissonFractionalAbundanceMax

a numeric vector

PoissonFractionalAbundanceMin

a numeric vector

ReferenceAssayNumber

a numeric vector

TargetAssayNumber

a numeric vector

MeanAmplitudeofPositives

a numeric vector

MeanAmplitudeofNegatives

a numeric vector

MeanAmplitudeTotal

a numeric vector

ExperimentComments

a logical vector

MergedWells

a logical vector

Details

Setup: Duplex assay with constant amount of genomic DNA and six 10-fold dilutions of plasmid DNA with 4 replicates, ranging theoretically from ~ 10^4 to 10^-1 copies/ micro L plus 4 replicates without plasmid DNA. Included are No-gDNA-control and No-template-control, 2 replicates each.

Annotation: FX.Y (X = dilution number, Y = replicate number). Hardware: Bio-Rad QX100 Droplet digital PCR system Details: Genomic DNA isolated from Pseudomonas putida KT2440. Plasmid is pCOM10-StyA::EGFP StyB [Jahn et al., 2013, Curr Opin Biotechnol, Vol. 24 (1): 79-87]. Template DNA was heat treated at 95 degree Celsius for 5 min prior to PCR. Channel 1, primers for genomic DNA marker ileS, Taqman probes (FAM labelled). Channel 2, primers for plasmid DNA marker styA, Taqman probes (HEX labelled).

Author(s)

Michael Jahn, Stefan Roediger, Michal Burdukiewcz

Source

Michael Jahn Flow cytometry group / Environmental microbiology Helmholtz Centre for Environmental Research - UFZ Permoserstrasse 15 / 04318 Leipzig / Germany phone +49 341 235 1318 michael.jahn [at] ufz.de / www.ufz.de

References

Jahn et al., 2013, Curr Opin Biotechnol, Vol. 24 (1): 79-87

Examples

summary(extract_run(read_QX100(pds), 1L:10))

Plasmid dilution series raw data

Description

Subset of raw data from the pds_raw data set as measured by the Bio-Rad QX100 Droplet Digital PCR System.

Format

A list of 3 data frames.

Well

| ExptType | Experiment | Sample + Dilution step | TypeAssay | Assay |

D01

| Absolute Quantification | ABS | gDNA + P 10^4 | Ch1Unknown | ileS |

D02

| Absolute Quantification | ABS | gDNA + P 10^2 | Ch1Unknown | ileS |

C04

| Absolute Quantification | ABS | gDNA | Ch1Unknown | ileS |

Details

The results can be found in pds.

Setup: Duplex assay with a constant amount of genomic DNA and six 10-fold dilutions of plasmid DNA with 4 replicates, ranging theoretically from ~ 10^4 to 10^-1 copies/ micro L plus 4 replicates without plasmid DNA. Included are No-gDNA-control and No-template-control, 2 replicates each.

Annotation: FX.Y (X = dilution number, Y = replicate number). Hardware: Bio-Rad QX100 Droplet digital PCR system Details: Genomic DNA isolated from Pseudomonas putida KT2440. Plasmid is pCOM10-StyA::EGFP StyB [Jahn et al., 2013, Curr Opin Biotechnol, Vol. 24 (1): 79-87]. Template DNA was heat treated at 95 degree Celsius for 5 min prior to PCR. Channel 1, primers for genomic DNA marker ileS, Taqman probes (FAM labelled). Channel 2, primers for plasmid DNA marker styA, Taqman probes (HEX labelled).

#' Setup: Duplex assay with a constant amount of genomic DNA and six 10-fold dilutions of plasmid DNA with 4 replicates, ranging theoretically from ~ 10^4 to 10^-1 copies/ micro L plus 4 replicates without plasmid DNA. Included are No-gDNA-control and No-template-control, 2 replicates each.

Annotation: FX.Y (X = dilution number, Y = replicate number). Hardware: Bio-Rad QX100 Droplet digital PCR system Details: Genomic DNA isolated from Pseudomonas putida KT2440. Plasmid is pCOM10-StyA::EGFP StyB [Jahn et al., 2013, Curr Opin Biotechnol, Vol. 24 (1): 79-87]. Template DNA was heat treated at 95 degree Celsius for 5 min prior to PCR. Channel 1, primers for genomic DNA marker ileS, Taqman probes (FAM labelled). Channel 2, primers for plasmid DNA marker styA, Taqman probes (HEX labelled).

The full data are located at https://github.com/michbur/dpcR_data as QX100_rawdata.zip.

Author(s)

Michael Jahn, Stefan Roediger, Michal Burdukiewcz

Source

Michael Jahn Flow cytometry group / Environmental microbiology Helmholtz Centre for Environmental Research - UFZ Permoserstrasse 15 / 04318 Leipzig / Germany phone +49 341 235 1318 michael.jahn [at] ufz.de / www.ufz.de

References

Jahn et al., 2013, Curr Opin Biotechnol, Vol. 24 (1): 79-87

Jahn M, Vorpahl C, Tuerkowsky D, Lindmeyer M, Buehler B, Harms H, et al. Accurate Determination of Plasmid Copy Number of Flow-Sorted Cells using Droplet Digital PCR. Anal Chem 2014; 86:5969–76. doi:10.1021/ac501118v.

Examples

bioamp(data = pds_raw[["D02"]], main = "Well D02", pch = 19)

Plot qdpcr objects

Description

An analytical plot describing relationship between the cycle number and the current value of Poisson mean. The plot can be used for quality control of process.

Arguments

Details

The rug parameter allows user to add density of the number of events to the plot.

Value

The plot.

Author(s)

Stefan Roediger, Michal Burdukiewicz

See Also

qdpcr

Examples

library(qpcR)
test <- cbind(reps[1L:45, ], reps2[1L:45, 2L:ncol(reps2)], reps3[1L:45, 
        2L:ncol(reps3)])
        
plot(qpcr2pp(data = test, cyc = 1, fluo = NULL, model = l5, delta = 5), rug = TRUE)

Plot Panel

Description

The plot_panel function takes objects of the class adpcr to enable customizable graphical representations of a chamber-based digital PCR experiments (e.g., Digital Array (R) IFCs (integrated fluidic circuits) of the BioMark (R) and EP1 (R)).

Usage

plot_panel(input, col = "red", legend = TRUE, half = "none",
  plot = TRUE, ...)

Arguments

Details

Currently, only objects containing tnp data can be plotted as a whole. For the any other type of the adpcr data, only just one column of data (one panel) can be plotted at the same time (see Examples how easily plot multipanel objects). Moreover the object must contain fluorescence intensities or exact number of molecules or the positive hits derived from the Cq values for each well. The Cq values can be obtained by custom made functions (see example in dpcr_density)) or the yet to implement "qpcr_analyser function from the dpcR package.

If the col argument has length one, a color is assigned for each interval of the input, with the brightest colors for the lowest values.

Value

Invisibly returns two sets of coordinates of each microfluidic well as per calc_coordinates: coords is a list of coordinates suitable for usage with functions from graphics package. The second element is a data frame of coordinates useful for users utilizing ggplot2 package.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

extract_run - extract experiments. adpcr2panel - convert adpcr object to arrays.

Examples

# Create a sample dPCR experiment with 765 elements (~> virtual compartments)   
# of target molecule copies per compartment as integer numbers (0,1,2)
ttest <- sim_adpcr(m = 400, n = 765, times = 20, pos_sums = FALSE, 
                   n_panels = 1)
# Plot the dPCR experiment results with default settings
plot_panel(ttest)

# Apply a two color code for number of copies per compartment
plot_panel(ttest, col = c("blue", "red"))

# plot few panels
ttest2 <- sim_adpcr(m = 400, n = 765, times = 40, pos_sums = FALSE, 
                    n_panels = 4)
oldpar <- par(mfcol = c(1, 1))
par(mfcol = c(2, 2))
four_panels <- lapply(1:ncol(ttest2), function(i) 
       plot_panel(extract_run(ttest2, i), legend = FALSE, 
         main = paste("Panel", LETTERS[i], sep = " ")))
par(oldpar)

# two different channels 
plot_panel(extract_run(ttest2, 1), legend = FALSE, 
           half = "left")
par(new = TRUE)
plot_panel(extract_run(ttest2, 2), col = "blue", 
           legend = FALSE, half = "right")

# plot two panels with every well as only the half of the rectangle
ttest3 <- sim_adpcr(m = 400, n = 765, times = 40, pos_sums = FALSE, 
                    n_panels = 2)
oldpar <- par(mfcol = c(1, 1))
par(mfcol = c(1, 2))
two_panels <- lapply(1:ncol(ttest3), function(i) 
       plot_panel(extract_run(ttest3, i), legend = FALSE, 
         main = paste("Panel", LETTERS[i], sep = " ")))
par(oldpar)

Amplitude Plot VIC and FAM Channels of a Droplet Digital PCR Experiment

Description

This function generates an amplitude plot of two fluorescence channels as found in droplet digital PCR.

Usage

plot_vic_fam(vic, fam, col_vic = "green", col_fam = "blue",
  circle = TRUE)

Arguments

Details

Droplet digital PCR experiments consist of three steps (droplet generation, clonal amplification, droplet amplitude analysis). Typically 20000 nano-sized droplets are analyzed and separated into amplification-positive and amplification-negative droplets. An example of such system is the Bio-Rad QX100 and QX200 (Pinheiro et al. 2012). Such systems have applications in the detection of rare DNA target copies, the determination of copy number variations (CNV), detection of mutation, or expression analysis of genes or miRNA. Each droplet is analyzed individually using a virtual two-color detection system. The channels are treated separately but finally aligned (e.g., FAM and VIC or FAM and HEX).

Value

The plot.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

References

Pinheiro, L.B., Coleman, V.A., Hindson, C.M., Herrmann, J., Hindson, B.J., Bhat, S., and Emslie, K.R. (2012). Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Anal. Chem. 84, 1003 - 1011.

Examples

# Generate an amplitude plot for the first fluorescence channel (e.g., FAM)
fluos1 <- sim_dpcr(m = 16, n = 30, times = 100, pos_sums = FALSE, n_exp = 1, 
  fluo = list(0.1, 0))

# Generate an amplitude plot for the second fluorescence channel (e.g., VIC)
fluos2 <- sim_dpcr(m = 16, n = 30, times = 100, pos_sums = FALSE, n_exp = 1, 
  fluo = list(0.1, 0))

# Plot the amplitudes of both fluorescence channel in an aligned fashion
plot_vic_fam(fam = fluos1, vic = fluos2)

# Same as above but different colors
plot_vic_fam(fam = fluos1, vic = fluos2, col_vic = "red", col_fam = "yellow")

# Same as above without circles
plot_vic_fam(fam = fluos1, vic = fluos2, col_vic = "red", col_fam = "yellow", circle = FALSE)

# Generate two channels in one object and plot them
fluos_both <- sim_dpcr(m = 16, n = 30, times = 100, pos_sums = FALSE, n_exp = 2, 
  fluo = list(0.1, 0))
plot_vic_fam(extract_run(fluos_both, 1), extract_run(fluos_both, 2))

Class "qdpcr"

Description

An object representing digital PCR reaction depicted as Poisson process. Inherits from dpcr.

Slots

list("mu")

"numeric" of the expected number of events in a defined interval.

list("C0")

"numeric" the occurrence in a defined interval.

list("CT")

"numeric" value of the "average time" between the occurrence of a positive reaction and another positive reaction.

Author(s)

Stefan Roediger, Michal Burdukiewicz.

See Also

plot.qdpcr,

qPCR to Poisson Process

Description

Describes qPCR as Poisson process.

Usage

qpcr2pp(data, cyc = 1, fluo = NULL, Cq_range = c(min(data[cyc]) + 6,
  max(data[cyc]) - 6), model = l5, SDM = TRUE, NuEvents = 1,
  delta = 1, exper = "qPCR1", replicate = 1, assay = "Unknown",
  type = "np")

Arguments

Details

Selected platforms (e.g., Open Array) are real-time platforms. dPCR can be described by Poisson statistics. The function qpcr2pp takes a step further and interprets the dPCR as a Poisson process if it is analyzed as a "time" based process.

The dPCR Technology breaks fundamentally with the previous concept of nucleic acid quantification. dPCR can be seen as a next generation nucleic acid quantification method based on PCR. The key difference between dPCR and traditional PCR lies in the method of measuring (absolute) nucleic acids amounts. This is possible after “clonal DNA amplification” in thousands of small separated partitions (e.g., droplets, nano chambers). Partitions with no nucleic acid remain negative and the others turn positive. Selected technologies (e.g., OpenArray(R) Real-Time PCR System) monitor amplification reactions in the chambers in real-time. Cq values are calculated from the amplification curves and converted into discrete events by means of positive and negative partitions and the absolute quantification of nucleic acids is done by Poisson statistics.

PCR data derived from a qPCR experiment can be seen as a series of events over time. We define t_i as the time between the first (i - 1)^st and the i^th event. Therefore, the time S_n is the sum of all t_i from i = 1 to i = n. This is the time to the n^th event. S(t) is the number of events in [0, t]. This can be seen as a Poisson process. The Poisson statistics is the central theorem to random processes in digital PCR.

The function qpcr2pp is used to model random point events in time units (PCR cycles), such as the increase of signal during a qPCR reaction in a single compartment. A Poisson process can be used to model times at which an event occurs in a "system". The qpcr2pp (quantitative Real-Time PCR to Poisson process) function transforms the qPCR amplification curve data to quantification points (Cq), which are visualized as Poisson process. This functions helps to spot differences between replicate runs of digital PCR experiments. In ideal scenarios the qpcr2pp plots are highly similar.

This tool might help to spot differences between experiments (e.g., inhibition of amplification reactions, influence of the chip arrays). The qPCR is unique because the amplification of conventional qPCRs takes place in discrete steps (cycles: 1, 2 ... 45), but the specific Cq values are calculated with continuous outcomes (Cq: 18.2, 25.7, ...). Other amplification methods such as isothermal amplifications are time based and thus better suited for Poisson process.

Value

An object of qdpcr class.

Author(s)

Stefan Roediger, Michal Burdukiewicz.

Examples

library(qpcR)
test <- cbind(reps[1L:45, ], reps2[1L:45, 2L:ncol(reps2)], 
	      reps3[1L:45, 2L:ncol(reps3)])

# before interpolation qPCR experiment must be converted into dPCR
qpcrpp <- qpcr2pp(data = test, cyc = 1, fluo = NULL, Cq_range = c(20, 30), 
                  model = l5, delta = 5)
summary(qpcrpp)

qPCR Analyser

Description

Calculate statistics based on fluorescence. The function can be used to analyze amplification curve data from quantitative real-time PCR experiments. The analysis includes the fitting of the amplification curve by a non-linear function and the calculation of a quantification point (often referred to as Cp (crossing-point), Cq or Ct) based on a user defined method. The function can be used to analyze data from chamber based dPCR machines.

Arguments

Details

The qpcRanalyzer is a functions to automatize the analysis of amplification curves from conventional quantitative real-time PCR (qPCR) experiments and is adapted for the needs in dPCR. This function calls instances of the qpcR package to calculate the quantification points (cpD1, cpD2, Cy0 (default), TOP (optional)), the amplification efficiency, fluorescence at the quantification point (Cq), the absolute change of fluorescence and the take-off point (TOP). Most of the central functionality of the qpcR package is accessible. The user can assign concentrations to the samples. One column contains binary converted (pos (1) and neg (0)) results for the amplification reaction based on a user defined criteria (Cq-range, fluorescence cut-off, ...). qpcr_analyser tries to detect cases where an amplification did not take place of was impossible to analyze. By default qpcr_analyser analyses uses the Cy0 as described in Guescini et al. (2008) for estimation of the quantification point since method is considered to be better suited for many probe systems. By default a 5-parameter model is used to fit the amplification curves. As such qpcr_analyser is a function, which serves for preliminary data inspection (see Example section) and as input for other R functions from the dpcR package (e.g., plot_panel).

Value

A matrix where each column represents crossing point, efficiency, the raw fluorescence value at the point defined by type and difference between minimum and maximum of observed fluorescence. If takeoff parameter is TRUE, additional two column represents start and the end of the fluorescence growth.

Author(s)

Stefan Roediger, Andrej-Nikolai Spiess, Michal Burdukiewicz.

References

Ritz C, Spiess An-N, qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis. Bioinformatics 24 (13), 2008.

Andrej-Nikolai Spiess (2013). qpcR: Modelling and analysis of real-time PCR data.
https://CRAN.R-project.org/package=qpcR

See Also

modlist.

Examples

# Take data of guescini1 data set from the qpcR R package.
library(qpcR)
# Use the first column containing the cycles and the second column for sample F1.1.
data(guescini1)
qpcr_analyser(guescini1, cyc = 1, fluo = 2)

# Use similar setting as before but set takeoff to true for an estimation of
# the first significant cycle of the exponential region.
qpcr_analyser(guescini1, cyc = 1, fluo = 2, takeoff = TRUE)

# Use similar setting as before but use qpcr_analyser in a loop to calculate the results for the
# first four columns containing the fluorescence in guescini1
print(qpcr_analyser(guescini1, cyc = 1, fluo = 2:5, takeoff = TRUE))

# Run qpcr_analyser on the list of models (finer control on fitting model process)
models <- modlist(guescini1)
qpcr_analyser(models)

Read BioMark

Description

Reads digital PCR data from the BioMark (Fluidigm).

Usage

read_BioMark(input, ext = NULL, detailed = FALSE)

Arguments

Value

An object of adpcr class.

Author(s)

Michal Burdukiewcz, Stefan Roediger

References

Dong, L. et al (2015). Comparison of four digital PCR platforms for accurate quantification of DNA copy number of a certified plasmid DNA reference material. Scientific Reports. 2015;5:13174.

See Also

See read_dpcr for detailed description of input files.

Read QX100

Description

Reads digital PCR data from the QX100 Droplet Digital PCR System (Bio-Rad).

Usage

read_QX100(input, ext = NULL)

Arguments

Value

An object of adpcr class.

Note

The volume and its uncertainty are taken from the literature (see references).

Author(s)

Michal Burdukiewcz, Stefan Roediger

References

Corbisier, P. et al (2015). DNA copy number concentration measured by digital and droplet digital quantitative PCR using certified reference materials. Analytical and Bioanalytical Chemistry 407, 1831-1840.

See Also

See read_dpcr for detailed description of input files.

Example of QX100 data: pds.

Read QX200

Description

Reads digital PCR data from the QX200 Droplet Digital PCR System (Bio-Rad).

Usage

read_QX200(input, ext = NULL)

Arguments

Value

An object of adpcr class.

Note

The volume and its uncertainty are taken from the literature (see references).

Author(s)

Michal Burdukiewcz, Stefan Roediger

Source

Droplet Digital PCR Applications Guide, Rev. A, Bulletin 6407

References

Corbisier, P. et al (2015). DNA copy number concentration measured by digital and droplet digital quantitative PCR using certified reference materials. Analytical and Bioanalytical Chemistry 407, 1831-1840.

See Also

See read_dpcr for detailed description of input files.

Read digital PCR amplitude raw data

Description

Reads digital PCR amplitude data.

Usage

read_amp(input, ext = NULL)

Arguments

Details

The amplitude data means a compressed directory of amplification.

Value

An object of adpcr.

Author(s)

Michal Burdukiewcz, Stefan Roediger

Read digital PCR data

Description

Reads digital PCR data in various formats.

Usage

read_dpcr(input, format, ext = NULL, ...)

Arguments

Details

Input files may have .csv, .xls or .xlsx extension. In case of Excel files with multiple sheets, only the first sheet will be analyzed.

Value

Always an object of adpcr or dpcr type.

Author(s)

Michal Burdukiewcz, Stefan Roediger

See Also

Read raw format files: read_redf. Read BioMark format files: read_BioMark. Read QX100 format files: read_QX100. Read QX200 format files: read_QX200.

Read digital PCR raw data

Description

Reads REDF (Raw Exchange Digital PCR format) data.

Usage

read_redf(input, ext = NULL)

Arguments

Details

REDF (Raw Exchange Digital PCR format) data is preferably a .csv file with following columns:

experiment

names of experiments

replicate

indices of replicates

assay

names of assays

k

number of positive partitions

n

total number of partitions

v

volume of partition (nL)

uv

uncertainty of partition's volume (nL)

threshold

partitions with k equal or higher than threshold are treated as positve.

panel_id

indices of panels

Column panel_id should be specified only in case of array-based dPCR.

Value

An object of adpcr or dpcr type, depends on the value of adpcr parameter.

Author(s)

Michal Burdukiewcz, Stefan Roediger

Rename object

Description

Renames objects of class adpcr or dpcr.

Usage

rename_dpcr(x, exper = NULL, replicate = NULL, assay = NULL)

Arguments

Details

The valid exper, replicate and assay names are factors. For the sake of convenience, this function converts other types to factors if it is possible.

Value

The renamed adpcr or dpcr object.

Class "rtadpcr" - real-time array digital PCR experiments

Description

A class designed to contain results from real-time array digital PCR experiments. Data is represented as matrix, where each column describes different measurement point (i.e. cycle number) and every row different partition.

Slots

list(".Data")

"matrix" containing data from array. See Description.

:

"matrix" containing data from array. See Description.

list("n")

Object of class "integer" equal to the number of partitions.

:

Object of class "integer" equal to the number of partitions.

list("type")

Object of class "character" defining type of data.

:

Object of class "character" defining type of data.

Author(s)

Michal Burdukiewicz.

See Also

End-point array digital PCR: adpcr.

Droplet digital PCR: dpcr.

Examples

#none

Methods for Function show

Description

Expands function show allowing showing objects of the class adpcr or dpcr.

Arguments

Author(s)

Michal Burdukiewicz.

Examples

#array dpcr
ptest <- sim_adpcr(400, 765, 5, FALSE, n_panels = 1)
show(ptest)

#multiple experiments
ptest <- sim_adpcr(400, 765, 5, FALSE, n_panels = 5)
show(ptest)

#droplet dpcr - fluorescence
dropletf <- sim_dpcr(7, 20, times = 5, fluo = list(0.1, 0))
show(dropletf)

#droplet dpcr - number of molecules
droplet <- sim_dpcr(7, 20, times = 5)
show(droplet)

Simulate Array Digital PCR

Description

A function that simulates results of an array digital PCR.

Usage

sim_adpcr(m, n, times, n_panels = 1, dube = FALSE, pos_sums = FALSE)

Arguments

Details

The array digital PCR is performed on plates containing many microfluidic chambers with a randomly distributed DNA template, fluorescence labels and standard PCR reagents. After the amplification reaction, performed independently in each chamber, the chambers with the fluorescence level below certain threshold are treated as negative. From differences between amplification curves of positive chambers it is possible to calculate both total number of template molecules and their approximate number in a single chamber.

The function contains two implementations of the array digital PCR simulation. First one was described in Dube at. al (2008). This method is based on random distributing m \times times molecules between n \times times chambers. After this step, the required number of plates is created by the random sampling of chambers without replacement. The above method is used, when the dube argument has value TRUE.

The second method treats the total number of template molecules as random variable with a normal distribution \mathcal{N}(n, 0.05n). The exact sum of total molecules per plate is calculated and randomly adjusted to the value of m \times times. The above method is used, when the dube argument has value FALSE. This implementation is much faster than previous one, especially for big simulations. The higher the value of the argument times, the simulation result is closer to theoretical calculations.

Value

If the pos_sums argument has value FALSE, the function returns a matrix with n rows and n_panels columns. Each column represents one plate. The type of such simulation would be "nm". If the pos_sums argument has value TRUE, the function returns a matrix with one row and n_panels columns. Each column contains the total number of positive chambers in each plate and type of simulation would be set as "tnp".

In each case the value is an object of the adpcr class.

Author(s)

Michal Burdukiewicz.

References

Dube S, Qin J, Ramakrishnan R, Mathematical Analysis of Copy Number Variation in a DNA Sample Using Digital PCR on a Nanofluidic Device. PLoS ONE 3(8), 2008.

See Also

sim_dpcr.

Examples

# Simulation of a digital PCR experiment with a chamber based technology.
# The parameter pos_sums was altered to change how the total number of positive 
# chamber per panel are returned. An alteration of the parameter has an impact 
# in the system performance.
adpcr_big <- sim_adpcr(m = 10, n = 40, times = 1000, pos_sums = FALSE, n_panels = 1000)
adpcr_small <- sim_adpcr(m = 10, n = 40, times = 1000, pos_sums = TRUE, n_panels = 1000)
# with pos_sums = TRUE, output allocates less memory
object.size(adpcr_big)
object.size(adpcr_small)

# Mini version of Dube et al. 2008 experiment, full requires replic <- 70000
# The number of replicates was reduced by a factor of 100 to lower the computation time.
replic <- 700
dube <- sim_adpcr(400, 765, times = replic, dube = TRUE, 
    pos_sums = TRUE, n_panels = replic)
mean(dube) # 311.5616
sd(dube) # 13.64159

# Create a barplot from the simulated data similar to Dube et al. 2008
bp <- barplot(table(factor(dube, levels = min(dube):max(dube))), 
	      space = 0)
lines(bp, dnorm(min(dube):max(dube), mean = 311.5, sd = 13.59)*replic, 
      col = "green", lwd = 3)

# Exact Dube's method is a bit slower than other one, but more accurate
system.time(dub <- sim_adpcr(m = 400, n = 765, times = 500, n_panels = 500, 
  pos_sums = TRUE))
system.time(mul <- sim_adpcr(m = 400, n = 765, times = 500, n_panels = 500, 
  pos_sums = FALSE))

Simulate Droplet Digital PCR

Description

A function that simulates results of a droplet digital PCR.

Usage

sim_dpcr(m, n, times, n_exp = 1, dube = FALSE, pos_sums = FALSE,
  fluo = NULL)

Arguments

Details

The function contains two implementations of the array digital PCR simulation. First one was described in Dube at. al (2008). This method is based on random distributing m \times times molecules between n \times times chambers. After this step, the required number of plates is created by the random sampling of chambers without replacement. The above method is used, when the dube argument has value TRUE.

The higher the value of the argument times, the simulation result is closer to theoretical calculations.

Value

If the pos_sums argument has value FALSE, the function returns matrix with n rows and n_panels columns. Each column represents one plate. The type of such simulation would be "nm". If the pos_sums argument has value TRUE, the function return matrix with one row and n_panels columns. Each column contains the total number of positive chambers in each plate and type of simulation would be set as "tnp".

In each case the value is an object of the dpcr class.

Note

Although Dube's simulation of digital PCR was developed for array digital PCR, it's also viable for simulating droplet-based methods.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

sim_adpcr.

Examples

#simulate fluorescence data
tmp_VIC <- sim_dpcr(m = 7, n = 20, times = 5, fluo = list(0.1, 0))
tmp_FAM <- sim_dpcr(m = 15, n = 20, times = 5, fluo = list(0.1, 0))
oldpar <- par(mfrow = c(1, 1))
par(mfrow = c(2,1))
plot(tmp_VIC, col = "green", type = "l")
plot(tmp_FAM, col = "blue", type = "l")
par(oldpar)
summary(tmp_FAM)
summary(sim_dpcr(m = 7, n = 20, times = 5, n_exp = 5))

Simulated Digital PCR data

Description

Simulated data from array-based digital PCR experiment (see sim_adpcr).

Format

An object of class adpcr containing six runs from three experiments (two runs per each experiment).

Examples

#code below was used to create six_panels data set
set.seed(1944)
adpcr1 <- sim_adpcr(m = 10, n = 765, times = 10000, pos_sums = FALSE, n_panels = 2)
adpcr2 <- sim_adpcr(m = 40, n = 765, times = 10000, pos_sums = FALSE, n_panels = 2)
adpcr2 <- rename_dpcr(adpcr2, exper = "Experiment2")
adpcr3 <- sim_adpcr(m = 100, n = 765, times = 10000, pos_sums = FALSE, n_panels = 2)
adpcr3 <- rename_dpcr(adpcr3, exper = "Experiment3")
six_panels_example <- bind_dpcr(adpcr1, adpcr2, adpcr3)
six_panels_example <- rename_dpcr(six_panels_example, assay = factor(rep(c("Chr4", "MYC"), 3)))

Methods for Function summary

Description

Expands function summary allowing printing summaries objects of the class adpcr to or dpcr.

Arguments

Details

The function prints a summary of the dPCR reaction, including k (number of positive chambers), n (total number of chambers), estimated lambda and concentration, as well as confidence intervals for the last two variables.

Value

The data frame with estimated values of lambda, m and corresponding confidence intervals.

Note

If summary is used on an object containing results of many experiments, all experiments would be independently summarized. Currently supported only for objects of class adpcr.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

References

Bhat S, Herrmann J, Corbisier P, Emslie K, Single molecule detection in nanofluidic digital array enables accurate measurement of DNA copy number. Analytical and Bioanalytical Chemistry 2 (394), 2009.

Dube S, Qin J, Ramakrishnan R, Mathematical Analysis of Copy Number Variation in a DNA Sample Using Digital PCR on a Nanofluidic Device. PLoS ONE 3(8), 2008.

Examples

# array dpcr
# Simulates a chamber based digital PCR with m total number of template molecules  
# and n number of chambers per plate and assigns it as object ptest of the class 
# adpcr for a single panel. The summary function on ptest gets assigned to summ 
# and the result with statistics according to Dube et al. 2008 and Bhat et al. 2009 
# gets printed.
ptest <- sim_adpcr(m = 400, n = 765, times = 5, dube = FALSE, n_panels = 1)
summ <- summary(ptest) #save summary
print(summ)

# multiple experiments
# Similar to the previous example but with five panels
ptest <- sim_adpcr(m = 400, n = 765, times = 5, dube = FALSE, n_panels = 5)
summary(ptest)

# droplet dpcr - fluorescence
# Simulates a droplet digital PCR with m = 7 total number of template molecules 
# and n = 20 number of  droplets. The summary function on dropletf gives the 
# statistics according to Dube et al. 2008 and Bhat et al. 2009. The fluo parameter 
# is used to change the smoothness of the fluorescence curve and the space between
#  two consecutive measured peaks (droplets).
dropletf <- sim_dpcr(m = 7, n = 20, times = 5, fluo = list(0.1, 0))
summary(dropletf)

# droplet dpcr - number of molecules
# Similar to the previous example but with five panels but without and modifications
#  to the peaks.
droplet <- sim_dpcr(m = 7, n = 20, times = 5)
summary(droplet)

# Visualize the results of dropletf and dropletf
# The curves of dropletf are smoother.
oldpar <- par(mfrow = c(1, 1))
par(mfrow = c(1,2))
plot(dropletf, main = "With fluo parameter", type = "l")
plot(droplet, main = "Without fluo parameter", type = "l")
par(oldpar)

Test counts

Description

The test for comparing counts from two or more digital PCR experiments.

Usage

test_counts(input, model = "ratio", conf.level = 0.95)

Arguments

Details

test_counts incorporates two different approaches to models: GLM (General Linear Model) and multiple pair-wise tests. The GLM fits counts data from different digital PCR experiments using quasibinomial or quasipoisson family. Comparisons between single experiments utilize Tukey's contrast and multiple t-tests (as provided by function glht).

In case of pair-wise tests, (rateratio.test or prop.test) are used to compare all pairs of experiments. The p-values are adjusted using the Benjamini & Hochberg method (p.adjust). Furthermore, confidence intervals are simultaneous.

Value

an object of class count_test.

Note

Mean number of template molecules per partition and its confidence intervals will vary depending on input.

Author(s)

Michal Burdukiewicz, Stefan Roediger, Piotr Sobczyk.

References

Bretz F, Hothorn T, Westfall P, Multiple comparisons using R. Boca Raton, Florida, USA: Chapman & Hall/CRC Press (2010).

Examples

adpcr1 <- sim_adpcr(m = 10, n = 765, times = 1000, pos_sums = FALSE, n_panels = 3)
adpcr2 <- sim_adpcr(m = 60, n = 550, times = 1000, pos_sums = FALSE, n_panels = 3)
adpcr2 <- rename_dpcr(adpcr2, exper = "Experiment2")
adpcr3 <- sim_adpcr(m = 10, n = 600, times = 1000, pos_sums = FALSE, n_panels = 3)
adpcr3 <- rename_dpcr(adpcr3, exper = "Experiment3")

#compare experiments using binomial regression
two_groups_bin <- test_counts(bind_dpcr(adpcr1, adpcr2), model = "binomial")
summary(two_groups_bin)
plot(two_groups_bin)
#plot aggregated results
plot(two_groups_bin, aggregate = TRUE)
#get coefficients
coef(two_groups_bin)

#this time use Poisson regression
two_groups_pois <- test_counts(bind_dpcr(adpcr1, adpcr2), model = "poisson")
summary(two_groups_pois)
plot(two_groups_pois)

#see how test behaves when results aren't significantly different
one_group <- test_counts(bind_dpcr(adpcr1, adpcr3))
summary(one_group)
plot(one_group)

Compare digital PCR runs - interactive presentation

Description

Launches graphical user interface allowing multiple comparisons of simulated digital PCR reactions.

Usage

test_counts_gui()

Value

A Shiny GUI for the different analyses described in this package.

Warning

Any ad-blocking software may be cause of malfunctions.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

test_counts.

Dispersion Test for Spatial Point Pattern in Array dPCR Based on Quadrat Counts

Description

Performs a test of Complete Spatial Randomness for each plate. This function is a wrapper around quadrat.test function working directly on the objects of adpcr.

Usage

test_panel(X, nx = 5, ny = 5, alternative = c("two.sided", "regular",
  "clustered"), method = c("Chisq", "MonteCarlo"), conditional = TRUE,
  nsim = 1999)

Arguments

Details

Under optimal conditions, the point pattern of dPCR events (e.g., positive droplet & negative droplets) should be randomly distrubuted over a planar chip. This function verifies this assumption using chi-square or Monte Carlo test. Arrays with non-random patterns should be checked for integrity.

Value

A list of objects of class "htest" with the length equal to the number of plates (minimum 1).

Note

A similar result can be achived by using adpcr2ppp and quadrat.test. See Examples.

Author(s)

Adrian Baddeley, Rolf Turner, Michal Burdukiewcz, Stefan Roediger.

References

http://www.spatstat.org/

Examples

many_panels <- sim_adpcr(m = 400, n = 765, times = 1000, pos_sums = FALSE, 
                   n_panels = 5)
test_panel(many_panels)

#test only one plate
test_panel(extract_run(many_panels, 3))

#do test_panel manually
library(spatstat.explore)
ppp_data <- adpcr2ppp(many_panels)
lapply(ppp_data, function(single_panel) quadrat.test(single_panel))

Peak Test

Description

Detect, separate and count positive and negative peaks, as well as peak-like noise. Additionally, function calculates area of the peaks.

Arguments

Details

The localization of peaks is determined by the findpeaks function. The area under the peak is calculated by integration of approximating spline.

Value

A list of length 2. The first element is a data frame containing: peak number, peak group (noise, negative, positive), position of the peak maximum, area under the peak, peak width, peak height, position of the peak and time resolution.

The second element contains smoothed data.

Author(s)

Stefan Roediger, Michal Burdukiewicz.

References

Savitzky, A., Golay, M.J.E., 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 36, 1627-1639.

Examples

data(many_peaks)
oldpar <- par(mfrow = c(1, 1))
par(mfrow = c(3,1))
plot(many_peaks, type = "l", main = "Noisy raw data")
abline(h = 0.01, col = "red")

tmp.out <- test_peaks(many_peaks[, 1], many_peaks[, 2], threshold = 0.01, noise_cut = 0.1, 
                    savgol = TRUE)
plot(tmp.out[["data"]], type = "l", main = "Only smoothed")
abline(h = 0.01, col = "red")
abline(v = many_peaks[tmp.out[["peaks"]][, 3], 1], lty = "dashed")

tmp.out <- test_peaks(many_peaks[, 1], many_peaks[, 2], threshold = 0.01, noise_cut = 0.1, 
                    savgol = TRUE, norm = TRUE)
plot(tmp.out[["data"]], type = "l", main = "Smoothed and peaks detected")
abline(v = many_peaks[tmp.out[["peaks"]][, 3], 1], lty = "dashed")
for(i in 1:nrow(tmp.out$peaks)) {
  if(tmp.out$peaks[i, 2] == 1) {col = 1}
  if(tmp.out$peaks[i, 2] == 2) {col = 2}
  if(tmp.out$peaks[i, 2] == 3) {col = 3}
  points(tmp.out$peaks[i, 7], tmp.out$peaks[i, 6], col = col, pch = 19)
}

positive <- sum(tmp.out$peaks[, 2] == 3)
negative <- sum(tmp.out$peaks[, 2] == 2)
total <- positive + negative

par(oldpar)

Compare pooled digital PCR

Description

Estimates mean number of template molecules per partition and concentration of sample from pooled replicates of experiments.

Usage

test_pooled(input, conf.level = 0.05)

Arguments

Value

data frame with the number of rows equal to the number of experiments (not runs). The unit of concentration is the number template molecules per nanoliter (nL).

Note

This function was implemented using the code in supplemental materials in Dorazio, 2015 (see References).

Author(s)

Robert M. Dorazio, Margaret E. Hunter.

References

Dorazio RM, Hunter ME, Statistical Models for the Analysis and Design of Digital Polymerase Chain Reaction (dPCR) Experiments. Analytical Chemistry 2015. 87(21): p.10886-10893

Examples

test_pooled(six_panels)