Package 'dPCP' reference manual

Title:	Automated Analysis of Multiplex Digital PCR Data
Description:	The automated clustering and quantification of the digital PCR data is based on the combination of 'DBSCAN' (Hahsler et al. (2019) <doi:10.18637/jss.v091.i01>) and 'c-means' (Bezdek et al. (1981) <doi:10.1007/978-1-4757-0450-1>) algorithms. The analysis is independent of multiplexing geometry, dPCR system, and input amount. The details about input data and parameters are available in the vignette.
Authors:	Alfonso De Falco [aut, cre] , Michel Mittelbronn [ctb], Christophe Olinger [ctb], Daniel Stieber [ctb], Laboratoire national de santé [cph]
Maintainer:	Alfonso De Falco <[email protected]>
License:	MIT + file LICENSE
Version:	2.0.1
Built:	2025-02-05 05:13:12 UTC
Source:	https://github.com/alfodefalco/dpcp

Prediction of clusters centroid position

Description

This function calculates the coodintaes of all clusters centroid.

Usage

centers_data(sample.subquality, sample.table, referenceDB)

## S3 method for class 'centers_data'
plot(x, ..., sample = "all")
centers_data(sample.subquality, sample.table, referenceDB)

## S3 method for class 'centers_data'
plot(x, ..., sample = "all")

Arguments

`sample.subquality`	an object of class `read_sample`, inherited from `read_sample`.
`sample.table`	object of class `sample_table`, inherited from `read_sampleTable`.
`referenceDB`	an object of class `reference_dbscan`, inherited from `reference_dbscan`
`x`	an object of class `centers_data`
`...`	Arguments to be passed to methods
`sample`	'all' to show all samples, or a numeric vector indicating the row number of samples in the sample table.

Value

An object of class centers_data containing a sublist for each sample. Each sublist has the following components:

`quality`	quality threshold used in `read_sample`.
`reference`	reference ID.
`centers`	a data frame with the centroids coordinates.
`data`	a data frame with the fluorescence intensities.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

plot(cent, sample = "all")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

plot(cent, sample = "all")

Cluster analysis with fuzzy c-means algorithm

Description

This function carries out the c-means cluster analysis, using the centroids position as initial values for cluster centers.

Usage

cmeans_clus(centers.data)

## S3 method for class 'cmeans_clus'
plot(x, ..., sample = "all", color.blind = FALSE)
cmeans_clus(centers.data)

## S3 method for class 'cmeans_clus'
plot(x, ..., sample = "all", color.blind = FALSE)

Arguments

`centers.data`	an object of class `centers_data`, inherited from `centers_data`.
`x`	an object of class `cmeans_clus`
`...`	Arguments to be passed to methods
`sample`	'all' to show all samples, or a numeric vector indicating the row number of samples in the sample table.
`color.blind`	logical. If TRUE colors optimized for colorblind readers are used.

Value

An object of class cmeans_clus containing a sublist for each sample. Each sublist has the following components:

`quality`	quality threshold used in `read_sample`.
`reference`	reference ID.
`centers`	a data frame with the centroids coordinates.
`data`	a data frame with the fluorescence intensities and clusters name.
`membership`	a matrix with the membership values of the data elements to the clusters. See also `cmeans`

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

plot(cmclus, sample = "all")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

plot(cmclus, sample = "all")

Test eps and minPts combinations for DBSCAN analysis

Description

This function tests all combinations of eps and minPts for DBSCAN analysis of reference samples indicated in refID. The results are represented in scatterplots exported to a pdf file.

Usage

dbscan_combination(
  refID,
  system = NULL,
  file.location = ".",
  reference.quality = 0.5,
  eps = c(120, 150, 180, 200),
  minPts = c(20, 50, 80, 100)
)
dbscan_combination(
  refID,
  system = NULL,
  file.location = ".",
  reference.quality = 0.5,
  eps = c(120, 150, 180, 200),
  minPts = c(20, 50, 80, 100)
)

Arguments

`refID`	a string or a character vector of chipID (Thermo Fisher) or the complete file name with the extension (Bio-Rad) of reference sample(s) to be analysed.
`system`	character. The name of digital PCR system used to generate the data. It must be either Thermo Fisher or Bio-Rad. Abbreviations are also accepted.
`file.location`	character. Full path name to reference and sample files location. The default corresponds to the working directory, (`getwd`). Tilde expansion (see (`path.expand`)) is performed.
`reference.quality`	numeric. Between 0 and 1. Quality threshold to subset the data (just for Thermo Fisher). If different thresholds have to be applied to various reference samples, a vectror of the same length of `refID` has to be provided.
`eps`	a numeric vector of values to be tested. Maximum distance between elements within a cluster in a DBSCAN analysis. See also `dbscan`.
`minPts`	a numeric vector of values to be tested. Number of minimum elements to assemble a cluster in a DBSCAN analysis. See also `dbscan`.

Value

A pdf file containing the scatterplots of DBSCAN analysis performed with all combinations of eps and minPts. Each reference generates a different pdf file.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

dbscan_combination("dilution20200313_B01_Amplitude.csv",
                   file.location = fileLoc, system = "bio-rad",
                   eps = c(150, 160, 180, 190), minPts = c(80, 100, 120))

unlink("dilution20200313_B01_Amplitude.pdf")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

dbscan_combination("dilution20200313_B01_Amplitude.csv",
                   file.location = fileLoc, system = "bio-rad",
                   eps = c(150, 160, 180, 190), minPts = c(80, 100, 120))

unlink("dilution20200313_B01_Amplitude.pdf")

Automated analysis of digital PCR data

Description

This function carries out the autometed clustering of digital PCR data.

Usage

dPCP(
  file,
  system = NULL,
  file.location = ".",
  reference.quality = 0.5,
  sample.quality = 0.5,
  eps = 200,
  minPts = 50,
  save.template = FALSE,
  rain = TRUE,
  QC.reference = FALSE,
  partition.volume = NULL
)

## S3 method for class 'dPCP'
plot(
  x,
  ...,
  sample = "all",
  reference = "all",
  type = "dPCP",
  color.blind = FALSE
)
dPCP(
  file,
  system = NULL,
  file.location = ".",
  reference.quality = 0.5,
  sample.quality = 0.5,
  eps = 200,
  minPts = 50,
  save.template = FALSE,
  rain = TRUE,
  QC.reference = FALSE,
  partition.volume = NULL
)

## S3 method for class 'dPCP'
plot(
  x,
  ...,
  sample = "all",
  reference = "all",
  type = "dPCP",
  color.blind = FALSE
)

Arguments

`file`	character. The name or the path of csv file to be read. If it does not contain an absolute path, the file name is relative to the current working directory, (`getwd`).
`system`	character. The name of digital PCR system used to generate the data. It must be either Thermo Fisher or Bio-Rad. Abbreviations are also accepted.
`file.location`	character. Full path name to reference and sample files location. The default corresponds to the working directory, (`getwd`). Tilde expansion (see (`path.expand`)) is performed.
`reference.quality`	numeric. Between 0 and 1. Quality threshold to subset the data. If different thresholds have to be applied to various reference samples, a vectror of the same length of number of reference samples has to be provided. Used only when the `system` is Thermo Fisher.
`sample.quality`	numeric. Between 0 and 1. Quality threshold to subset data. If different thresholds have to be applied to various samples, a vectror of the same length of number of samples has to be provided. Used only when the `system` is Thermo Fisher.
`eps`	numeric. Input parameter for the DBSCAN algorithm. It represents the maximum distance between the elements within a cluster. See also `dbscan`. If different values have to be applied to various reference samples, a vectror of the same length of number of reference samples has to be provided.
`minPts`	numeric. Input parameter for the DBSCAN algorithm. It represents the number of minimum elements to assemble a cluster. See also `dbscan`. If different values have to be applied to various reference samples, a vectror of the same length of number of reference samples has to be provided.
`save.template`	logical. If TRUE a template of DBSCAN analysis of reference samples is saved. When `system` is Thermo Fisher, `save.template` can be also a character vector indicating the chipID.
`rain`	logical. If TRUE the rain analysis is carried out.
`QC.reference`	logical. If TRUE the fraction of rain elements in the reference samples is carried out. Warning messages are displayed when the percentage of rain is high.
`partition.volume`	numeric. This parameters is taken into account when the parameter 'system' is set on Other. Indicate the partion volume in microliters spcific to the digital PCR system.
`x`	an object of class `dPCP`
`...`	Arguments to be passed to methods
`sample`	'all' to show all samples, or a numeric vector indicating the row number of samples in the sample table.
`reference`	'all' to show all reference samples, or a character vector with chip ID (Thermo Fisher) or the file name (Bio-rad) of reference samples to be showed.
`type`	string. Type of plot to be showed. Available plots: 'reference dbscan', 'centers', 'cmeans', 'rain', 'dPCP'. @param color.blind logical. If TRUE colors optimized for colorblind readers are used.
`color.blind`	logical. If TRUE colors optimized for colorblind readers are used.

Value

An object of class dPCP containing the following components:

`referenceDB`	an object of class `reference_dbscan`.
`samples`	a list of samples. Each sample sublist contains the information about the cluster analysis.
`results`	an object of class `replicates_quant`.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE, rain = TRUE,
                QC.reference = FALSE)

plot(results, sample = 1, type = "dPCP")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE, rain = TRUE,
                QC.reference = FALSE)

plot(results, sample = 1, type = "dPCP")

Export dPCP analysis results to a csv file

Description

This function exports dPCP analysis results to a csv file.

Usage

export_csv(data, filename)
export_csv(data, filename)

Arguments

`data`	an object of class `dPCP`, `target_quant` or `replicates_quant`.
`filename`	character. File name (no extension) for csv and pdf files to create on disk.

Value

A csv file with the information and results of dPCP analysis.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE,
                rain = TRUE)

export_csv(results, filename = "dPCRproject_1")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE,
                rain = TRUE)

export_csv(results, filename = "dPCRproject_1")

Manual correction of dPCP cluster analysis

Description

This function builds an interactive app to manually correct the dPCP cluster analysis.

Usage

manual_correction(
  data,
  filename,
  save.plot = FALSE,
  format = "png",
  dpi = 300,
  color.blind = FALSE
)
manual_correction(
  data,
  filename,
  save.plot = FALSE,
  format = "png",
  dpi = 300,
  color.blind = FALSE
)

Arguments

`data`	an object of class `dPCP`, inherited from `dPCP`.
`filename`	character. File name (no extension) for csv and pdf files to create on disk.
`save.plot`	logical. If TRUE the plots are exported to a file.
`format`	a string indicating the file format for the export. Available formats: 'eps', 'ps', 'tex', 'pdf', 'jpeg', 'tiff', 'png', 'bmp', 'svg', 'wmf'.
`dpi`	numeric. Image resolution.
`color.blind`	logical. If TRUE colors optimized for colorblind readers are used.

Value

A Shiny session.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE,
                rain = TRUE)

manual_correction(results, filename = "manual_dPCR", save.plot = FALSE)

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE,
                rain = TRUE)

manual_correction(results, filename = "manual_dPCR", save.plot = FALSE)

Identification and clustering of "rain" data

Description

This function identifies the "rain" elements and re-clusters them using the Mahalanobis distance. Each "rain" element is assigned to the cluster whose Mahalanobis distance is the lowest.

Usage

rain_reclus(cmeans.cluster)

## S3 method for class 'rain_reclus'
plot(x, ..., sample = "all", color.blind = FALSE)
rain_reclus(cmeans.cluster)

## S3 method for class 'rain_reclus'
plot(x, ..., sample = "all", color.blind = FALSE)

Arguments

`cmeans.cluster`	an object of class `cmeans_clus`, inherited from `cmeans_clus`.
`x`	an object of class `rain_reclus`
`...`	Arguments to be passed to methods
`sample`	'all' to show all samples, or a numeric vector indicating the row number of samples in the sample table.
`color.blind`	logical. If TRUE colors optimized for colorblind readers are used.

Value

An object of class rain_reclus containing a sublist for each sample. Each sublist has the following components:

`quality`	quality threshold used in `read_sample`.
`reference`	reference ID.
`centers`	a data frame with the centroids coordinates.
`data`	a data frame with the fluorescence intensities and clusters name.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                   file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

#Rain classification.
rainclus <- rain_reclus(cmclus)

plot(rainclus, sample = "all")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                   file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

#Rain classification.
rainclus <- rain_reclus(cmclus)

plot(rainclus, sample = "all")

Read reference files

Description

This function reads the results files of reference samples listed in the sample table. Fluoresce intensity and quality value (just for Thermo Fisher) are collected. If a reference_dbscan template file with the same input paramters (reference ID, eps, minPts) is available, fluorescence data, quality value and dbscan analysis results are retrived from the template file.

Usage

read_reference(
  sample.table,
  system = NULL,
  file.location = ".",
  reference.quality = 0.5,
  eps = NULL,
  minPts = NULL
)
read_reference(
  sample.table,
  system = NULL,
  file.location = ".",
  reference.quality = 0.5,
  eps = NULL,
  minPts = NULL
)

Arguments

`sample.table`	object of class `sample_table`, inherited from `read_sampleTable`.
`system`	character. The name of digital PCR system used to generate the data. It must be either Thermo Fisher or Bio-Rad. Abbreviations are also accepted.
`file.location`	character. Full path name to reference and sample files location. The default corresponds to the working directory, (`getwd`). Tilde expansion (see (`path.expand`)) is performed.
`reference.quality`	numeric. Between 0 and 1. Quality threshold to subset the data. If different thresholds have to be applied to various reference samples, a vectror of the same length of number of reference samples has to be provided. Used only when the `system` is Thermo Fisher.
`eps`, `minPts`	numeric. Input parameters for the DBSCAN algorithm. If they match the paramters of `reference_dbscan` template file, the data are retrived from the template.

Value

An object of class read_reference containing a sublist for each reference. Each sublist has the following components:

`quality`	value of the `reference.quality` parameter.
`data`	a matrix with the fluorescence intensities and quality values.
`dbscan`	an object of class `dbscan_fast`, inherited from `dbscan`. This component is available only if a `reference_dbscan` template file is used to retrive the data.

Examples

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)
library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

Read sample files

Description

This function reads the results files of samples listed in the sample table. Fluoresce intensity and quality value (just for Thermo Fisher) are collected.

Usage

read_sample(
  sample.table,
  system = NULL,
  file.location = ".",
  sample.quality = 0.5,
  partition.volume = NULL
)
read_sample(
  sample.table,
  system = NULL,
  file.location = ".",
  sample.quality = 0.5,
  partition.volume = NULL
)

Arguments

`sample.table`	object of class `sample_table`, inherited from `read_sampleTable`.
`system`	character. The name of digital PCR system used to generate the data. It must be either Thermo Fisher or Bio-Rad. Abbreviations are also accepted.
`file.location`	character. Full path name to reference and sample files location. The default corresponds to the working directory, (`getwd`). Tilde expansion (see (`path.expand`)) is performed.
`sample.quality`	numeric. Between 0 and 1. Quality threshold to subset data. If different thresholds have to be applied to various samples, a vectror of the same length of number of samples has to be provided. Used only when the `system` is Thermo Fisher.
`partition.volume`	numeric. This parameters is taken into account when the parameter 'system' is set on Other. Indicate the partion volume in microliters spcific to the digital PCR system.

Value

An object of class read_sample containing a sublist for each sample. Each sublist has the following components:

`quality`	value of the `sample.quality` parameter.
`data`	a matrix with the fluorescence intensities and quality values.

Examples

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)
library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

Read sample table

Description

This function reads a file containing the essential information about the samples and experimental settings. The file has to be filled out by the user and formatted as described in the vignette.

Usage

read_sampleTable(file, system = NULL, file.location = ".")
read_sampleTable(file, system = NULL, file.location = ".")

Arguments

`file`	character. The name or the path of csv file to be read. If it does not contain an absolute path, the file name is relative to the current working directory, (`getwd`).
`system`	character. The name of digital PCR system used to generate the data. It must be either Thermo Fisher or Bio-Rad. Abbreviations are also accepted.
`file.location`	character. Full path name to reference and sample files location. The default corresponds to the working directory, (`getwd`). Tilde expansion (see (`path.expand`)) is performed.

Value

An object of class sample_table.

Examples

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)
library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

Find the empty partitions and single target clusters in the reference sample

Description

This function computes a DBSCAN analysis to identify single target clusters in the reference samples listed in the sample table. If a reference_dbscan template file with the same input paramters (reference ID, eps, minPts) is available, data are retrived from the template file.

Usage

reference_dbscan(
  reference.subquality,
  sample.table,
  eps = 200,
  minPts = 50,
  save.template = FALSE
)

## S3 method for class 'reference_dbscan'
plot(x, ..., reference = "all")
reference_dbscan(
  reference.subquality,
  sample.table,
  eps = 200,
  minPts = 50,
  save.template = FALSE
)

## S3 method for class 'reference_dbscan'
plot(x, ..., reference = "all")

Arguments

`reference.subquality`	an object of class `read_reference`, inherited from `read_reference`.
`sample.table`	object of class `sample_table`, inherited from `read_sampleTable`.
`eps`, `minPts`	numeric. Input parameters for the DBSCAN algorithm. If they match the paramters of `reference_dbscan` template file, the data are retrived from the template.
`save.template`	logical. If TRUE a template of DBSCAN analysis of reference samples is saved. When `system` is Thermo Fisher, `save.template` can be also a character vector indicating the chipID.
`x`	an object of class `reference_dbscan`
`...`	Arguments to be passed to methods
`reference`	'all' to show all reference samples, or a character vector with chip ID (Thermo Fisher) or the file name (Bio-rad) of reference samples to be showed.

Value

An object of class reference_dbscan containing a sublist for each reference. Each sublist has the following components:

`quality`	quality threshold used in `read_reference`.
`data`	a matrix with the fluorescence intensities and quality values.
`dbscan`	an object of class `dbscan_fast`, inherited from `dbscan`.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

plot(dbref, reference = "all")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata",package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

plot(dbref, reference = "all")

Calculation of targets concentration, pooling the sample replicates

Description

This function calculates the concentration of the targets, combining the results of the replicates of each sample.

Usage

replicates_quant(raw.results, sample.table)
replicates_quant(raw.results, sample.table)

Arguments

`raw.results`	an object of class `target_quant`, inherited from `target_quant`.
`sample.table`	object of class `sample_table`, inherited from `read_sampleTable`.

Value

An object of class replicates_quant containing a sublist for every sample. Each sublist has the following components:

`quality`	quality threshold used in `read_sample`.
`reference`	reference ID.
`raw results`	a data frame with the results of quantification.
`replicates results`	a data frame with the results of quantification of pooled replicates.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                   file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

#Rain classification.
rainclus <- rain_reclus(cmclus)

#Quantification
quantcm <- target_quant(cmclus, sample.table)
quant <- target_quant(rainclus, sample.table)

#Replicates pooling
rep.quant <- replicates_quant(quant, sample.table)

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                   file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

#Rain classification.
rainclus <- rain_reclus(cmclus)

#Quantification
quantcm <- target_quant(cmclus, sample.table)
quant <- target_quant(rainclus, sample.table)

#Replicates pooling
rep.quant <- replicates_quant(quant, sample.table)

Export dPCP analysis results to a pdf report

Description

This function generates a pdf report of the dPCP analysis.

Usage

report_dPCP(data, filename, sample = "all", color.blind = FALSE)
report_dPCP(data, filename, sample = "all", color.blind = FALSE)

Arguments

`data`	an object of class `dPCP`, inherited from `dPCP`.
`filename`	character. File name (no extension) for csv and pdf files to create on disk.
`sample`	'all' to show all samples, or a numeric vector indicating the row number of samples in the sample table.
`color.blind`	logical. If TRUE colors optimized for colorblind readers are used.

Value

A pdf file with the information and results of the dPCP analysis.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE,
                rain = TRUE)

report_dPCP(results, filename = "dPCRproject_1")

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#dPCP analysis
results <- dPCP(sampleTable, system = "bio-rad", file.location = fileLoc,
                eps = 200, minPts = 50, save.template = FALSE,
                rain = TRUE)

report_dPCP(results, filename = "dPCRproject_1")

Calculation of targets concentration.

Description

This function calculates the concentration of the targets according to the Poisson distribution.

Usage

target_quant(data.cluster, sample.table)
target_quant(data.cluster, sample.table)

Arguments

`data.cluster`	an object of class `rain_reclus` or `cmeans_clus`.
`sample.table`	object of class `sample_table`, inherited from `read_sampleTable`.

Value

An object of class target_quant containing a sublist for each sample. Each sublist has the following components:

`quality`	quality threshold used in `read_sample`.
`reference`	reference ID.
`raw results`	a data frame with the results of the quantification.

Examples


library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

#Rain classification.
rainclus <- rain_reclus(cmclus)

#Quantification
quantcm <- target_quant(cmclus, sample.table)
quant <- target_quant(rainclus, sample.table)

library(dPCP)

#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
                     package = "dPCP")

fileLoc <- system.file("extdata", package = "dPCP")

#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
                                 file.location = fileLoc)

#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
                      file.location = fileLoc)

#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
                    file.location = fileLoc)

#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)

#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)

#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)

#Rain classification.
rainclus <- rain_reclus(cmclus)

#Quantification
quantcm <- target_quant(cmclus, sample.table)
quant <- target_quant(rainclus, sample.table)

Package 'dPCP'

Help Index

Prediction of clusters centroid position

Description

Usage

Arguments

Value

Examples

Cluster analysis with fuzzy c-means algorithm

Description

Usage

Arguments

Value

Examples

Test eps and minPts combinations for DBSCAN analysis

Description

Usage

Arguments

Value

Examples

Automated analysis of digital PCR data

Description

Usage

Arguments

Value

Examples

Export dPCP analysis results to a csv file

Description

Usage

Arguments

Value

Examples

Manual correction of dPCP cluster analysis

Description

Usage

Arguments

Value

Examples

Identification and clustering of "rain" data

Description

Usage

Arguments

Value

Examples

Read reference files

Description

Usage

Arguments

Value

Examples

Read sample files

Description

Usage

Arguments

Value

Examples

Read sample table

Description

Usage

Arguments

Value

Examples

Find the empty partitions and single target clusters in the reference sample

Description

Usage

Arguments

Value

Examples

Calculation of targets concentration, pooling the sample replicates

Description

Usage

Arguments

Value

Examples

Export dPCP analysis results to a pdf report

Description

Usage

Arguments

Value

Examples