Package 'NormqPCR' reference manual

Title:	Functions for normalisation of RT-qPCR data
Description:	Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data.
Authors:	Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman
Maintainer:	James Perkins <[email protected]>
License:	LGPL-3
Version:	1.51.0
Built:	2024-07-03 05:47:52 UTC
Source:	https://github.com/bioc/NormqPCR

Functions for normalisation of RT-qPCR data.

Description

Functions for normalisation of real-time quantitative PCR data.

Details

Package:	NormqPCR
Type:	Package
Version:	1.7.1
Date:	2014-08-13
Depends:	R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR
Imports:	ReadqPCR
biocViews:	MicrotitrePlateAssay, GeneExpression, qPCR
License:	LGPL-3
LazyLoad:	yes
LazyData:	yes

require(NormqPCR)

Author(s)

Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman

Maintainer: James Perkins [email protected]

References

Perkins, JR, Dawes, JM, McMahon, SB, Bennett, DL, Orengo, C, Kohl, M (2012). ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT-qPCR quantification cycle (Cq) data. BMC Genomics, 13, 1:296.

Jo Vandesompele, Katleen De Preter, Filip Pattyn et al. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 2002. 3(7):research0034.1-0034.11. http://genomebiology.com/2002/3/7/research/0034/

Claus Lindbjerg Andersen, Jens Ledet Jensen and Torben Falck Orntoft (2004). Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets. CANCER RESEARCH 64, 5245-5250, August 1, 2004. http://cancerres.aacrjournals.org/cgi/content/full/64/15/5245

Kenneth Livak, Thomase Schmittgen (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2^ddCt Method. Methods 25, 402-408, 2001 http://www.ncbi.nlm.nih.gov/pubmed/11846609

Examples

## some examples are given in the vignette
## Not run: 
library(NormqPCR)
vignette("NormqPCR")

## End(Not run)
## some examples are given in the vignette
## Not run: 
library(NormqPCR)
vignette("NormqPCR")

## End(Not run)

Bladder dataset of Andersen et al (2004)

Description

This dataset was used in Andersen et al (2004) to demonstrate normalization of real-time quantitative RT-PCR data by geometric averaging of housekeeping genes.

Usage

data(Bladder)data(Bladder)

Format

A qPCRBatch object which contains an expression matrix with the expression of 14 genes measured in 28 samples. The sample information is saved in the phenoData slot with variables

Sample.no.: sample number.
Grade: Grade of bladder cancer.

The following information on the measured genes is saved in the variables Symbol and Gene.name of the featureData slot.

ATP5B: ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypetide.
HSPCB: Heat shock 90-kDa protein 1, beta.
S100A6: S100 calcium-binding protein A6 (calcylin).
FLOT2: Flotillin 2.
TEGT: Testis enhanced gene transcript (BAX inhibitor 1).
UBB: Ubiquitin B.
TPT1: Tumor protein, translationally controlled 1.
CFL1: Cofilin 1 (non-muscle).
ACTB: Actin, beta.
RPS13: Ribosomal protein S13.
RPS23: Ribosomal protein S23.
GAPD: Glyceraldehyde-3-phosphate dehydrogenase.
UBC: Ubiquitin C.
FLJ20030: Hypothetical protein FLJ20030.

For a detailed annotation see Table 1 in Anderson et al. (2004).

Details

The genes included in this data set were selected by screening 99 bladder sample expression profiles.

Source

The data set was obtained from http://www.mdl.dk/Publications_sup1.htm

References

Examples

  data(Bladder)
  Bladder
  head(exprs(Bladder))
  pData(Bladder)
  fData(Bladder)
data(Bladder)
  Bladder
  head(exprs(Bladder))
  pData(Bladder)
  fData(Bladder)

Dataset of Andersen et al (2004)

Description

This dataset was used in Andersen et al (2004) to demonstrate normalization of real-time quantitative RT-PCR data by geometric averaging of housekeeping genes.

Usage

data(BladderRepro)data(BladderRepro)

Format

A qPCRBatch object which contains an expression matrix with the expression of 8 genes measured in 26 samples. The sample information is saved in the phenoData slot with variables

Sample.no.: sample number.
Grade: Grade of bladder cancer.

The following information on the measured genes is saved in the variables Symbol and Gene.name of the featureData slot.

CD14: CD14 antigen.
FCN1: Ficolin (collagen/fibrinogen domain containing) 1.
CCNG2: Cyclin G2.
NPAS2: Neuronal PAS domain protein 2.
UBC: Ubiquitin C.
CFL1: Cofilin 1 (non-muscle).
ACTB: Actin, beta.
GAPD: Glyceraldehyde-3-phosphate dehydrogenase.

For a detailed annotation see Table 1 and Supplementary table 1 in Anderson et al. (2004).

Details

This data set was used to check the reproducibility of the results obtained in Andersen et al (2004).

Source

The data set was obtained from http://www.mdl.dk/Publications_sup1.htm

References

Examples

  data(BladderRepro)
  BladderRepro
  head(exprs(BladderRepro))
  pData(BladderRepro)
  fData(BladderRepro)
data(BladderRepro)
  BladderRepro
  head(exprs(BladderRepro))
  pData(BladderRepro)
  fData(BladderRepro)

Colon dataset of Andersen et al (2004)

Description

This dataset was used in Andersen et al (2004) to demonstrate normalization of real-time quantitative RT-PCR data by geometric averaging of housekeeping genes.

Usage

data(Colon)data(Colon)

Format

A qPCRBatch object which contains an expression matrix with the expression of 13 genes measured in 40 samples. The sample information is saved in the phenoData slot with variables

Sample.no.: sample number.
Classification: Classification of colon cancer.

The following information on the measured genes is saved in the variables Symbol and Gene.name of the featureData slot.

UBC: Ubiquitin C.
UBB: Ubiquitin B.
SUI1: Putative translation initiation factor.
NACA: Nascent-polypeptide-associated complex alpha polypeptide.
FLJ20030: Hypothetical protein FLJ20030.
CFL1: Cofilin 1 (non-muscle).
ACTB: Actin, beta.
CLTC: Clathrin, heavy polypeptide (Hc).
RPS13: Ribosomal protein S13.
RPS23: Ribosomal protein S23.
GAPD: Glyceraldehyde-3-phosphate dehydrogenase.
TPT1: Tumor protein, translationally controlled 1.
TUBA6: Tubulin alpha 6.

For a detailed annotation see Table 1 in Anderson et al. (2004).

Details

The genes included in this data set were selected by screening 161 colon sample expression profiles.

Source

The data set was obtained from http://www.mdl.dk/Publications_sup1.htm

References

Examples

  data(Colon)
  Colon
  head(exprs(Colon))
  pData(Colon)
  fData(Colon)
data(Colon)
  Colon
  head(exprs(Colon))
  pData(Colon)
  fData(Colon)

Combines Technical Replicates

Description

Takes expression set of qPCR values containing technical replicates and combines them.

Usage

combineTechReps(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
combineTechReps(qPCRBatch, calc="arith") 
combineTechReps(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
combineTechReps(qPCRBatch, calc="arith")

Arguments

`qPCRBatch`	Expression set containing qPCR data, read in by a ReadqPCR function and containing technical reps, denoted by `_TechRep.n` suffix.
`...`	Extra arguments, detailed below
`calc`	use median, arithmetic or geometric mean for combining the values

Details

Takes exprs of qPCR values containing technical replicates and combines them using a specified centrality measure.

Value

qPCRBatch with same number of samples, but with less features, since all technical replicates are replaced with a single value of their means.

Author(s)

James Perkins [email protected]

References

Examples

  path <- system.file("exData", package = "NormqPCR")
  qPCR.example.techReps <- file.path(path, "qPCR.techReps.txt")
  qPCRBatch.qPCR.techReps <- read.qPCR(qPCR.example.techReps)
  rownames(exprs(qPCRBatch.qPCR.techReps))
  combinedTechReps <- combineTechReps(qPCRBatch.qPCR.techReps)
  rownames(exprs(combinedTechReps))
path <- system.file("exData", package = "NormqPCR")
  qPCR.example.techReps <- file.path(path, "qPCR.techReps.txt")
  qPCRBatch.qPCR.techReps <- read.qPCR(qPCR.example.techReps)
  rownames(exprs(qPCRBatch.qPCR.techReps))
  combinedTechReps <- combineTechReps(qPCRBatch.qPCR.techReps)
  rownames(exprs(combinedTechReps))

Combines Technical Replicates

Description

Takes expression set of qPCR values containing technical replicates and combines them. In addition the appropriate standard deviation (SD) is computed.

Usage

combineTechRepsWithSD(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
combineTechRepsWithSD(qPCRBatch, calc="arith") 
combineTechRepsWithSD(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
combineTechRepsWithSD(qPCRBatch, calc="arith")

Arguments

`qPCRBatch`	Expression set containing qPCR data, read in by a ReadqPCR function and containing technical reps, denoted by `_TechRep.n` suffix.
`...`	Extra arguments, detailed below
`calc`	use median, arithmetic or geometric mean for combining the values

Details

Takes exprs of qPCR values containing technical replicates and combines them using a specified centrality measure.

The arithmetic mean (calc="arith") is combined with the classical standard deviation. In case of the geometric mean (calc="geom") the classical standard deviation of the log-values is exponentiated. The median (calc="median") is calculated in connection with the MAD.

Value

qPCRBatch with same number of samples, but with less features, since all technical replicates are replaced with a single value of their means. In addition the slot assayData includes a matrix with SD values which can be accessed via se.exprs.

Author(s)

Matthias Kohl [email protected]

References

Examples

  path <- system.file("exData", package = "NormqPCR")
  qPCR.example.techReps <- file.path(path, "qPCR.techReps.txt")
  qPCRBatch.qPCR.techReps <- read.qPCR(qPCR.example.techReps)
  rownames(exprs(qPCRBatch.qPCR.techReps))
  combinedTechReps <- combineTechRepsWithSD(qPCRBatch.qPCR.techReps)
  rownames(exprs(combinedTechReps))
  exprs(combinedTechReps)
  se.exprs(combinedTechReps)
path <- system.file("exData", package = "NormqPCR")
  qPCR.example.techReps <- file.path(path, "qPCR.techReps.txt")
  qPCRBatch.qPCR.techReps <- read.qPCR(qPCR.example.techReps)
  rownames(exprs(qPCRBatch.qPCR.techReps))
  combinedTechReps <- combineTechRepsWithSD(qPCRBatch.qPCR.techReps)
  rownames(exprs(combinedTechReps))
  exprs(combinedTechReps)
  se.exprs(combinedTechReps)

Compute Normalized Relative Quantities

Description

This function computes normalized relative quantities (NRQs) for a qPCRBatch.

Usage

ComputeNRQs(qPCRBatch, ...)
## S4 method for signature 'qPCRBatch'
ComputeNRQs(qPCRBatch, hkgs)
ComputeNRQs(qPCRBatch, ...)
## S4 method for signature 'qPCRBatch'
ComputeNRQs(qPCRBatch, hkgs)

Arguments

`qPCRBatch`	an object of class `qPCRBatch`.
`hkgs`	Names of reference/housekeeping genes.
`...`	other parameters to be passed to downstream methods.

Details

Allows the user to normalized relative quantities as defined in Hellemanns et al. (2007).

Value

Object of class "qPCRBatch".

Author(s)

Nor Izayu Abdul Rahman, Matthias Kohl [email protected]

References

Jan Hellemans, Geert Mortier, Anne De Paepe, Frank Speleman and Jo Vandesompele (2007). qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biology, 8:R19

Examples

  ## Example data
  path <- system.file("exData", package = "ReadqPCR")
  qPCR.example <- file.path(path, "qPCR.example.txt")
  Cq.data <- read.qPCR(qPCR.example)

  ## combine technichal replicates
  Cq.data1 <- combineTechRepsWithSD(Cq.data)

  ## add efficiencies
  Effs <- file.path(path, "Efficiencies.txt")
  Cq.effs <- read.table(file = Effs, row.names = 1, header = TRUE)
  rownames(Cq.effs) <- featureNames(Cq.data1)
  effs(Cq.data1) <- as.matrix(Cq.effs[,"efficiency",drop = FALSE])
  se.effs(Cq.data1) <- as.matrix(Cq.effs[,"SD.efficiency",drop = FALSE])
  
  ##
  res <- ComputeNRQs(Cq.data1, hkgs = c("gene_az", "gene_gx"))
  ## NRQs
  exprs(res)
  ## SD of NRQs
  se.exprs(res)
## Example data
  path <- system.file("exData", package = "ReadqPCR")
  qPCR.example <- file.path(path, "qPCR.example.txt")
  Cq.data <- read.qPCR(qPCR.example)

  ## combine technichal replicates
  Cq.data1 <- combineTechRepsWithSD(Cq.data)

  ## add efficiencies
  Effs <- file.path(path, "Efficiencies.txt")
  Cq.effs <- read.table(file = Effs, row.names = 1, header = TRUE)
  rownames(Cq.effs) <- featureNames(Cq.data1)
  effs(Cq.data1) <- as.matrix(Cq.effs[,"efficiency",drop = FALSE])
  se.effs(Cq.data1) <- as.matrix(Cq.effs[,"SD.efficiency",drop = FALSE])
  
  ##
  res <- ComputeNRQs(Cq.data1, hkgs = c("gene_az", "gene_gx"))
  ## NRQs
  exprs(res)
  ## SD of NRQs
  se.exprs(res)

Compute Cq value and amplification efficiency

Description

This function calculates Cq value and amplification efficiency for a CyclesSet. It is based on function pcrbatch of package qpcR.

Usage

CqValues(object, ...)
## S4 method for signature 'CyclesSet'
CqValues(object, Effmethod = "expfit", group = NULL, 
                      model = l5, check = "uni2", checkPAR = parKOD(), 
                      remove = "none", exclude = NULL, type = "cpD2", 
                      labels = NULL, norm = FALSE, baseline = NULL, 
                      basefac = 1, smooth = NULL, 
                      smoothPAR = list(span = 0.1), 
                      factor = 1, opt = FALSE, 
                      optPAR = list(sig.level = 0.05, crit = "ftest"), 
                      plot = FALSE, verbose = FALSE, ...)
CqValues(object, ...)
## S4 method for signature 'CyclesSet'
CqValues(object, Effmethod = "expfit", group = NULL, 
                      model = l5, check = "uni2", checkPAR = parKOD(), 
                      remove = "none", exclude = NULL, type = "cpD2", 
                      labels = NULL, norm = FALSE, baseline = NULL, 
                      basefac = 1, smooth = NULL, 
                      smoothPAR = list(span = 0.1), 
                      factor = 1, opt = FALSE, 
                      optPAR = list(sig.level = 0.05, crit = "ftest"), 
                      plot = FALSE, verbose = FALSE, ...)

Arguments

`object`	an object of class `CyclesSet`.
`Effmethod`	a character vector defining the methods for computing amplification efficiency.
`group`	a vector containing the grouping for possible replicates.
`model`	the model to be used for all runs. Default model is `l5`.
`check`	the method for kinetic outlier detection in `KOD`. Method `"uni2"` is set as default which is a test on sigmoidal structure.
`checkPAR`	parameters to be supplied to the `check` method. See `parKOD`.
`remove`	indicates which runs to be removed. Either `none` of them, those which failed to `fit` or from the outlier methods.
`exclude`	indicates samples to be excluded from calculation, either "" for samples with missing column names or a regular expression defining columns (samples); see 'Details' and 'Examples' in `modlist`.
`type`	the point on the amplification curve which is used for efficiency estimation; see `efficiency`.
`labels`	a vector containing labels which define replicate groups. See more details in `pcrbatch` and `ratiobatch`.
`norm`	a logical value which determines whether the raw data should be normalized within [0, 1] before model fitting or not.
`baseline`	type of baseline subtraction. More details in `efficiency`.
`basefac`	a factor when using averaged baseline cycles, such as `0.95`.
`smooth`	the curve smoothing method. See more details in `pcrbatch`.
`smoothPAR`	parameters to be supplied to smoothing method in `smooth`.
`factor`	a multiplication factor for the fluorescence response values.
`opt`	a logical value which determines whether model selection should be applied to each model or not.
`optPAR`	parameters to be supplied for model selection in `mselect`.
`plot`	a logical value. If `TRUE`, the single runs are plotted from the internal `modlist` for diagnostics.
`verbose`	a logical value. If `TRUE`, fitting and tagging results will be displayed in the console.
`...`	other parameters to be passed to downstream methods.

Details

Allows the user to compute Cq value and amplification efficiency. In addition, all values generated during the computations are saved. This function has four choices of methods for computing amplification efficiency values which are the methods provided by package qpcR.

More details on technical replication and normalization is given in the vignette NormqPCR.

Value

Object of class "qPCRBatch".

Author(s)

Nor Izayu Abdul Rahman, Matthias Kohl [email protected]

References

Examples

  ## Read in the raw qPCR data from file "LC480_Example.txt"
  path <- system.file("exData", package = "ReadqPCR")
  LC480.example <- file.path(path, "LC480_Example.txt")
  cycData <- read.LC480(file = LC480.example)

  ## Read in the sample information data from file "LC480_Example_SampleInfo.txt". 
  LC480.SamInfo <- file.path(path, "LC480_Example_SampleInfo.txt")
  samInfo <- read.LC480SampleInfo(LC480.SamInfo)

  ## Merge information
  cycData1 <- merge(cycData, samInfo)

  ## Compute Cq values
  ## 1) use sigmoidal model
  res1 <- CqValues(cycData1, Effmethod = "sigfit")
  res1
  effs(res1)
  se.effs(res1)

  ## 2) fit exponential model (default)
  res2 <- CqValues(cycData1, Effmethod = "expfit")
  res2
  effs(res2)
  se.effs(res2)

  ## 3) use window of linearity
  res3 <- CqValues(cycData1, Effmethod = "sliwin")
  res3
  effs(res3)
  se.effs(res3)

  ## 4) linear regression of efficiency
  res4 <- CqValues(cycData1, Effmethod = "LRE")
  res4
  effs(res4)
  se.effs(res4)
## Read in the raw qPCR data from file "LC480_Example.txt"
  path <- system.file("exData", package = "ReadqPCR")
  LC480.example <- file.path(path, "LC480_Example.txt")
  cycData <- read.LC480(file = LC480.example)

  ## Read in the sample information data from file "LC480_Example_SampleInfo.txt". 
  LC480.SamInfo <- file.path(path, "LC480_Example_SampleInfo.txt")
  samInfo <- read.LC480SampleInfo(LC480.SamInfo)

  ## Merge information
  cycData1 <- merge(cycData, samInfo)

  ## Compute Cq values
  ## 1) use sigmoidal model
  res1 <- CqValues(cycData1, Effmethod = "sigfit")
  res1
  effs(res1)
  se.effs(res1)

  ## 2) fit exponential model (default)
  res2 <- CqValues(cycData1, Effmethod = "expfit")
  res2
  effs(res2)
  se.effs(res2)

  ## 3) use window of linearity
  res3 <- CqValues(cycData1, Effmethod = "sliwin")
  res3
  effs(res3)
  se.effs(res3)

  ## 4) linear regression of efficiency
  res4 <- CqValues(cycData1, Effmethod = "LRE")
  res4
  effs(res4)
  se.effs(res4)

Perform normalization with a given housekeeping gene

Description

Normalise qPCR eset using a given housekeeping gene as control, then perform differential expression analysis using the delta delta Ct method

Usage

deltaCt(qPCRBatch, ...)
## S4 method for signature 'qPCRBatch'
deltaCt(qPCRBatch, hkgs, combineHkgs=FALSE, calc="arith")
deltaCq(qPCRBatch, hkgs, combineHkgs=FALSE, calc="arith")
deltaCt(qPCRBatch, ...)
## S4 method for signature 'qPCRBatch'
deltaCt(qPCRBatch, hkgs, combineHkgs=FALSE, calc="arith")
deltaCq(qPCRBatch, hkgs, combineHkgs=FALSE, calc="arith")

Arguments

`qPCRBatch`	qPCR-specific expression set, containing qPCR data.
`...`	Extra arguments, detailed below
`hkgs`	String containing the name of the name of the housekeeping gene which will be used to normalise the rest of the genes.
`combineHkgs`	Logical - if TRUE, then as long as more than one housekeeper given for argument hkgs, it will combine the housekeepers by finding the geometric mean. Housekeepers can be found using geNorm or NormFinder algorithms.
`calc`	use arithmetic or geometric mean.

Details

Takes expression set of qPCR values and normalises them using a housekeeping gene. Returns a qPCRBatch with exprs set of the same dimensions but with the given hkg value subtracted.

Value

qPCRBatch with exprs set of the same dimensions but with the given hkg value subtracted.

Author(s)

James Perkins [email protected]

References

Kenneth Livak, Thomase Schmittgen (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2^DDCt Method. Methods 25, 402-408, 2001 http://www.ncbi.nlm.nih.gov/pubmed/11846609

Examples

  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  hkgs<-"Actb-Rn00667869_m1"
  qPCRBatch.norm <- deltaCq(qPCRBatch =  qPCRBatch.taqman, hkgs = hkgs, calc="arith")
  head(exprs(qPCRBatch.norm))
path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  hkgs<-"Actb-Rn00667869_m1"
  qPCRBatch.norm <- deltaCq(qPCRBatch =  qPCRBatch.taqman, hkgs = hkgs, calc="arith")
  head(exprs(qPCRBatch.norm))

Perform normalization and differential expression with given housekeeping gene

Description

Normalise qPCRBatch RT-qPCR data using housekeeping genes as control, then perform differential expression analysis using the delta delta Cq method.

Usage

  deltaDeltaCt(qPCRBatch,...)
  ## S4 method for signature 'qPCRBatch'
deltaDeltaCt(qPCRBatch, maxNACase=0, maxNAControl=0, hkgs, contrastM, 
             case, control, paired=TRUE, hkgCalc="arith", statCalc="arith")
  deltaDeltaCq(qPCRBatch, maxNACase=0, maxNAControl=0, hkgs, contrastM,
             case, control, paired=TRUE, hkgCalc="arith", statCalc="arith")
deltaDeltaCt(qPCRBatch,...)
  ## S4 method for signature 'qPCRBatch'
deltaDeltaCt(qPCRBatch, maxNACase=0, maxNAControl=0, hkgs, contrastM, 
             case, control, paired=TRUE, hkgCalc="arith", statCalc="arith")
  deltaDeltaCq(qPCRBatch, maxNACase=0, maxNAControl=0, hkgs, contrastM,
             case, control, paired=TRUE, hkgCalc="arith", statCalc="arith")

Arguments

`qPCRBatch`	qPCR-specific expression set, containing qPCR data.
`...`	Extra arguments, detailed below
`maxNACase`	Maximum number of NA values allowed before a detector's reading is discarded for samples designated as case.
`maxNAControl`	Maximum number of NA values allowed before a detector's reading is discarded for samples designated as control.
`hkgs`	String containing the name of th name of the housekeeping gene which will be used to normalise the rest of the genes.
`contrastM`	A binary matrix which designates case and control samples.
`case`	The name of the column in contrastM that corresponds to the case samples.
`control`	The name of the column in contrastM that corresponds to the control samples.
`paired`	Logical - if TRUE the detectors and housekeepers in the same sample will be paired for calculating standard deviation, effectively meaning we will be calculating standard deviation of the differences. If FALSE, there will be no pairing, and standard deviation will be pooled between the detector and housekeepers.
`hkgCalc`	String - either "arith" or "geom", details how the different housekeeper genes should be combined - either by using the arithmetic or geometric mean.
`statCalc`	String - either "arith" or "geom", details how genes should be combined - either by using the arithmetic or geometric mean.

Details

Takes expression set of qPCR values and normalises them using different housekeeping genes. Returns seperate sets of values for each housekeeping gene given.

Value

matrix with columns containing the detector ids, 2^delta Cq values for the sample of interest and the callibrator sample, alongside their respective standard deviations, the 2^delta delta Cq values and the minimum and maximum values (ie the values that are 1 sd away )

Author(s)

James Perkins [email protected]

References

Examples

  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  hkg <- "Actb-Rn00667869_m1"

  contM <- cbind(c(0,0,1,1,0,0,1,1),c(1,1,0,0,1,1,0,0))
  colnames(contM) <- c("interestingPhenotype","wildTypePhenotype")
  rownames(contM) <- sampleNames(qPCRBatch.taqman)

  ddCq.taqman <- deltaDeltaCq(qPCRBatch = qPCRBatch.taqman, maxNACase=1, 
                              maxNAControl=1, hkg=hkg, contrastM=contM, 
                              case="interestingPhenotype", 
                              control="wildTypePhenotype", 
                              statCalc="geom", hkgCalc="arith")
  head(ddCq.taqman)
path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  hkg <- "Actb-Rn00667869_m1"

  contM <- cbind(c(0,0,1,1,0,0,1,1),c(1,1,0,0,1,1,0,0))
  colnames(contM) <- c("interestingPhenotype","wildTypePhenotype")
  rownames(contM) <- sampleNames(qPCRBatch.taqman)

  ddCq.taqman <- deltaDeltaCq(qPCRBatch = qPCRBatch.taqman, maxNACase=1, 
                              maxNAControl=1, hkg=hkg, contrastM=contM, 
                              case="interestingPhenotype", 
                              control="wildTypePhenotype", 
                              statCalc="geom", hkgCalc="arith")
  head(ddCq.taqman)

Data set of Vandesompele et al (2002)

Description

This data set was used in Vandesompele et al (2002) to demonstrate normalization of real-time quantitative RT-PCR data by geometric averaging of housekeeping genes.

Usage

data(geNorm)data(geNorm)

Format

A qPCRBatch object which contains an expression matrix with 85 observations on the following 10 variables which stand for expression data of ten potential reference/housekeeping genes

ACTB: actin, beta
B2M: beta-2-microglobulin
GAPD: glyceraldehyde-3-phosphate dehydrogenase
HMBS: hydroxymethylbilane synthase
HPRT1: hypoxanthine phosphoribosyltransferase 1
RPL13A: ribosomal protein L13a
SDHA: succinate dehydrogenase complex subunit A
TBP: TATA box binding protein
UBC: ubiquitin C
YWHAZ: tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide

Details

The row names of this data set indicate the various human tissues which were investigated.

BM: 9 normal bone-marrow samples
POOL: 9 normal human tissues from pooled organs (heart, brain, fetal brain, lung, trachea, kidney, mammary gland, small intestine and uterus)
FIB: 20 short-term cultured normal fibroblast samples from different individuals
LEU: 13 normal leukocyte samples
NB: 34 neuroblastoma cell lines (independently prepared in different labs from different patients)

Source

The data set was obtained from http://genomebiology.com/content/supplementary/gb-2002-3-7-research0034-s1.txt

References

Examples

  data(geNorm)
  str(exprs(geNorm.qPCRBatch))
  sampleNames(geNorm.qPCRBatch)
data(geNorm)
  str(exprs(geNorm.qPCRBatch))
  sampleNames(geNorm.qPCRBatch)

Geometric Mean

Description

Computation of the geometric mean.

Usage

geomMean(x, na.rm = TRUE)
geomMean(x, na.rm = TRUE)

Arguments

`x`	numeric vector of non-negative Reals
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.

Details

The computation of the geometric mean is done via prod(x)^(1/length(x)).

Value

geometric mean

Note

A first version of this function appeared in package SLqPCR.

Author(s)

Matthias Kohl [email protected]

References

Examples

x <- rlnorm(100)
geomMean(x)
x <- rlnorm(100)
geomMean(x)

Make all Cq values NA

Description

Make all Cq values for a given detector NA when the number of NAs for that detector is above a given threshold

Usage

makeAllNAs(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
makeAllNAs(qPCRBatch, contrastM, sampleMaxM)
makeAllNAs(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
makeAllNAs(qPCRBatch, contrastM, sampleMaxM)

Arguments

`qPCRBatch`	Expression set containing qPCR data.
`...`	Extra arguments, detailed below
`contrastM`	Contrast Matrix like that used in `limma`. Columns represent the different samples types, rows are the different samples, with a 1 or 0 in the matrix indicating which sample types the different samples belong to.
`sampleMaxM`	Sample Max Matrix. Columns represent the different sample types. There is one value per column, which represents the max number of NAs allowed for that sample type.

Details

Make all NAs when number of NAs above a given threshold

Value

qPCRBatch object with a new exprs slot, everything else equal

Author(s)

James Perkins [email protected]

References

Examples

  # read in the data
  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  exprs(qPCRBatch.taqman)["Ccl20.Rn00570287_m1",] # values before

  # make contrastM
  a <- c(0,0,1,1,0,0,1,1) # one for each sample type, with 1 representing
  b <- c(1,1,0,0,1,1,0,0) # position of sample type in the samplenames vector
  contM <- cbind(a,b)
  colnames(contM) <- c("case","control") # then give the names of each sample type
  rownames(contM) <- sampleNames(qPCRBatch.taqman) # and the rows of the matrix
  contM

  # make sampleMaxM
  sMaxM <- t(as.matrix(c(3,3))) # now make the sample max matrix
  colnames(sMaxM) <- c("case","control") # make sure these line up with samples
  sMaxM

  # function
  qPCRBatch.taqman.replaced <- makeAllNAs(qPCRBatch.taqman, contM, sMaxM)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]
# read in the data
  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  exprs(qPCRBatch.taqman)["Ccl20.Rn00570287_m1",] # values before

  # make contrastM
  a <- c(0,0,1,1,0,0,1,1) # one for each sample type, with 1 representing
  b <- c(1,1,0,0,1,1,0,0) # position of sample type in the samplenames vector
  contM <- cbind(a,b)
  colnames(contM) <- c("case","control") # then give the names of each sample type
  rownames(contM) <- sampleNames(qPCRBatch.taqman) # and the rows of the matrix
  contM

  # make sampleMaxM
  sMaxM <- t(as.matrix(c(3,3))) # now make the sample max matrix
  colnames(sMaxM) <- c("case","control") # make sure these line up with samples
  sMaxM

  # function
  qPCRBatch.taqman.replaced <- makeAllNAs(qPCRBatch.taqman, contM, sMaxM)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]

Make all Cq values NA

Description

Make all Cq values for a given detector NA when the number of NAs for that detector is above a given threshold

Usage

makeAllNewVal(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
makeAllNewVal(qPCRBatch, contrastM, sampleMaxM, newVal)
makeAllNewVal(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
makeAllNewVal(qPCRBatch, contrastM, sampleMaxM, newVal)

Arguments

`qPCRBatch`	Expression set containing qPCR data.
`...`	Extra arguments, detailed below
`contrastM`	Contrast Matrix like that used in `limma`. Columns represent the different samples types, rows are the different samples, with a 1 or 0 in the matrix indicating which sample types the different samples belong to.
`sampleMaxM`	Sample Max Matrix. Columns represent the different sample types. There is one value per column, which represents the max number of NAs allowed for that sample type.
`newVal`	New value to give the values in the group where the NAs are above the threshold.

Details

Make all a given value when number of NAs above a given threshold, with different thresholds for the different sample classes, using sMaxM and contM to provide this information, as detailed below.

Value

qPCRBatch object with a new exprs slot, everything else equal

Author(s)

James Perkins [email protected]

References

Examples

  # read in the data
  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  exprs(qPCRBatch.taqman)["Ccl20.Rn00570287_m1",] # values before

  # make contrastM
  a <- c(0,0,1,1,0,0,1,1) # one for each sample type, with 1 representing
  b <- c(1,1,0,0,1,1,0,0) # position of sample type in the samplenames vector
  contM <- cbind(a,b)
  colnames(contM) <- c("case","control") # then give the names of each sample type
  rownames(contM) <- sampleNames(qPCRBatch.taqman) # and the rows of the matrix
  contM

  # make sampleMaxM
  sMaxM <- t(as.matrix(c(3,3))) # now make the sample max matrix
  colnames(sMaxM) <- c("case","control") # make sure these line up with samples
  sMaxM

  # function
  qPCRBatch.taqman.replaced <- makeAllNewVal(qPCRBatch.taqman, contM, sMaxM)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]
# read in the data
  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  exprs(qPCRBatch.taqman)["Ccl20.Rn00570287_m1",] # values before

  # make contrastM
  a <- c(0,0,1,1,0,0,1,1) # one for each sample type, with 1 representing
  b <- c(1,1,0,0,1,1,0,0) # position of sample type in the samplenames vector
  contM <- cbind(a,b)
  colnames(contM) <- c("case","control") # then give the names of each sample type
  rownames(contM) <- sampleNames(qPCRBatch.taqman) # and the rows of the matrix
  contM

  # make sampleMaxM
  sMaxM <- t(as.matrix(c(3,3))) # now make the sample max matrix
  colnames(sMaxM) <- c("case","control") # make sure these line up with samples
  sMaxM

  # function
  qPCRBatch.taqman.replaced <- makeAllNewVal(qPCRBatch.taqman, contM, sMaxM)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]

Replace Cq values with new value

Description

Replace Cq values above a given threshold with a new value

Usage

replaceAboveCutOff(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
replaceAboveCutOff(qPCRBatch, newVal=NA, cutOff=38)
replaceAboveCutOff(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
replaceAboveCutOff(qPCRBatch, newVal=NA, cutOff=38)

Arguments

`qPCRBatch`	Expression set containing qPCR data.
`...`	Extra arguments, detailed below
`newVal`	The new value with which to replace the values above the cutoff
`cutOff`	the minimal threshold above which the values will be replaced

Details

Replaces values in the exprs slot of the qPCRBatch object that are above a threshold value with a new number

Value

qPCRBatch object with a new exprs slot

Author(s)

James Perkins [email protected]

References

Examples

  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  exprs(qPCRBatch.taqman)["Ccl20.Rn00570287_m1",]
  qPCRBatch.taqman.replaced <- replaceAboveCutOff(qPCRBatch.taqman, newVal = NA, cutOff = 35)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]
path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  exprs(qPCRBatch.taqman)["Ccl20.Rn00570287_m1",]
  qPCRBatch.taqman.replaced <- replaceAboveCutOff(qPCRBatch.taqman, newVal = NA, cutOff = 35)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]

Replace NAs with a given value

Description

Replace NAs with a given value

Usage

replaceNAs(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
replaceNAs(qPCRBatch, newNA)
replaceNAs(qPCRBatch, ...)

## S4 method for signature 'qPCRBatch'
replaceNAs(qPCRBatch, newNA)

Arguments

`qPCRBatch`	Expression set containing qPCR data.
`...`	Extra arguments, detailed below
`newNA`	The new value to replace the NAs with

Details

Replaces NA values in the exprs slot of the qPCRBatch object with a given number

Value

qPCRBatch object with a new exprs slot

Author(s)

James Perkins [email protected]

References

Examples

  path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  qPCRBatch.taqman.replaced <- replaceNAs(qPCRBatch.taqman, newNA = 40)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]
path <- system.file("exData", package = "NormqPCR")
  taqman.example <- file.path(path, "example.txt")
  qPCRBatch.taqman <- read.taqman(taqman.example)
  qPCRBatch.taqman.replaced <- replaceNAs(qPCRBatch.taqman, newNA = 40)
  exprs(qPCRBatch.taqman.replaced)["Ccl20.Rn00570287_m1",]

Selection of reference/housekeeping genes

Description

This function can be used to determine a set of reference/housekeeping (HK) genes for gene expression experiments

Usage

selectHKs(qPCRBatch, ...)

## S4 method for signature 'matrix'
selectHKs(qPCRBatch, group, method = "geNorm", minNrHKs = 2, log = TRUE, Symbols, 
        trace = TRUE, na.rm = TRUE)

## S4 method for signature 'qPCRBatch'
selectHKs(qPCRBatch, group, method = "geNorm", minNrHKs = 2, log = TRUE, Symbols, 
	      trace = TRUE, na.rm = TRUE)
selectHKs(qPCRBatch, ...)

## S4 method for signature 'matrix'
selectHKs(qPCRBatch, group, method = "geNorm", minNrHKs = 2, log = TRUE, Symbols, 
        trace = TRUE, na.rm = TRUE)

## S4 method for signature 'qPCRBatch'
selectHKs(qPCRBatch, group, method = "geNorm", minNrHKs = 2, log = TRUE, Symbols, 
	      trace = TRUE, na.rm = TRUE)

Arguments

`qPCRBatch`	matrix or qPCRBatch, containing the data (expression matrix) in the exprs slot
`...`	Extra arguments, detailed below
`group`	optional factor not used by all methods, hence may be missing
`method`	method to compute most stable genes
`minNrHKs`	minimum number of HK genes that should be considered
`log`	logical: is data on log-scale
`Symbols`	gene symbols
`trace`	logical, print additional information
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.

Details

This function can be used to determine a set of reference/housekeeping (HK) genes for gene expression experiments. The default method "geNorm" was proposed by Vandesompele et al. (2002).

Currently, the geNorm method by Vandesompele et al. (2002) and the NormFinder method of Andersen et al. (2004) are implemented.

Vandesompele et al. (2002) propose a cut-off value of 0.15 for the pairwise variation. Below this value the inclusion of an additional housekeeping gene is not required.

Value

If method = "geNorm" a list with the following components is returned

`ranking`	ranking of genes from best to worst where the two most stable genes cannot be ranked
`variation`	pairwise variation during stepwise selection
`meanM`	average expression stability M

If method = "NormFinder" a list with the following components is returned

`ranking`	ranking of genes from best to worst where the two most stable genes cannot be ranked
`rho`	stability measure rho of Andersen et al. (2004)

Author(s)

Matthias Kohl [email protected]

References

Examples

  data(geNorm)
  tissue <- as.factor(c(rep("BM", 9), rep("FIB", 20), rep("LEU", 13),
                        rep("NB", 34), rep("POOL", 9)))
  res.BM <- selectHKs(geNorm.qPCRBatch[,tissue == "BM"], method = "geNorm", 
                      Symbols = featureNames(geNorm.qPCRBatch), minNrHK = 2, 
                      log = FALSE)
data(geNorm)
  tissue <- as.factor(c(rep("BM", 9), rep("FIB", 20), rep("LEU", 13),
                        rep("NB", 34), rep("POOL", 9)))
  res.BM <- selectHKs(geNorm.qPCRBatch[,tissue == "BM"], method = "geNorm", 
                      Symbols = featureNames(geNorm.qPCRBatch), minNrHK = 2, 
                      log = FALSE)

Gene expression stability value M

Description

Computation of the gene expression stability value M for real-time quantitativ RT-PCR data. For more details we refer to Vandesompele et al. (2002).

Usage

stabMeasureM(x, log = TRUE, na.rm = TRUE)
stabMeasureM(x, log = TRUE, na.rm = TRUE)

Arguments

`x`	matrix or data.frame containing real-time quantitative RT-PCR data
`log`	logical: is data on log-scale
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.

Details

The gene expression stability value M is defined as the average pairwise normalization factor; i.e., one needs to specify data from at least two genes. For more details see Vandesompele et al. (2002). Note this dispatches on a transposed expression matrix, not a qPCRBatch object since it is only called from within the selectHKs method.

Value

numeric vector with gene expression stability values

Author(s)

Matthias Kohl [email protected]

References

Examples

  data(geNorm)
  tissue <- as.factor(c(rep("BM", 9),  rep("FIB", 20), rep("LEU", 13),
                    rep("NB", 34), rep("POOL", 9)))
  res.BM <- selectHKs(geNorm.qPCRBatch[,tissue == "BM"], method = "geNorm", 
                    Symbols = featureNames(geNorm.qPCRBatch), minNrHK = 2, log = FALSE)
data(geNorm)
  tissue <- as.factor(c(rep("BM", 9),  rep("FIB", 20), rep("LEU", 13),
                    rep("NB", 34), rep("POOL", 9)))
  res.BM <- selectHKs(geNorm.qPCRBatch[,tissue == "BM"], method = "geNorm", 
                    Symbols = featureNames(geNorm.qPCRBatch), minNrHK = 2, log = FALSE)

Gene expression stability value rho

Description

Computation of the gene expression stability value rho for real-time quantitativ RT-PCR data. For more details we refer to Andersen et al. (2004).

Usage

  stabMeasureRho(x,...)

  ## S4 method for signature 'x'
stabMeasureRho(x, group, log = TRUE, na.rm = TRUE, returnAll = FALSE)
stabMeasureRho(x,...)

  ## S4 method for signature 'x'
stabMeasureRho(x, group, log = TRUE, na.rm = TRUE, returnAll = FALSE)

Arguments

`x`	matrix containing real-time quantitative RT-PCR data, or qPCRBatch object
`...`	Extra arguments, detailed below
`group`	grouping factor, either a factor vector or a phenoData column called "Group"
`log`	logical: is data on log-scale
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`returnAll`	logical, return additional information.

Details

The gene expression stability value rho is computed. For more details see Andersen et al. (2004).

Value

numeric vector with gene expression stability values

If returnAll == TRUE a list with the following components is returned

`rho`	stability measure rho of Andersen et al. (2004)
`d`	used by `selectHKs`
`v`	used by `selectHKs`

Author(s)

Matthias Kohl [email protected]

References

Examples

  data(Colon)
  Class <- pData(Colon)[,"Classification"]
  res.Colon <- stabMeasureRho(Colon, group = Class, log = FALSE)
  sort(res.Colon) # cf. Table 3 in Andersen et al (2004)
  
  data(Bladder)
  Grade <- pData(Bladder)[,"Grade"]
  res.Bladder <- stabMeasureRho(Bladder, group = Grade, log = FALSE)
  sort(res.Bladder) # cf. Table 3 in Andersen et al (2004)
data(Colon)
  Class <- pData(Colon)[,"Classification"]
  res.Colon <- stabMeasureRho(Colon, group = Class, log = FALSE)
  sort(res.Colon) # cf. Table 3 in Andersen et al (2004)
  
  data(Bladder)
  Grade <- pData(Bladder)[,"Grade"]
  res.Bladder <- stabMeasureRho(Bladder, group = Grade, log = FALSE)
  sort(res.Bladder) # cf. Table 3 in Andersen et al (2004)

Package 'NormqPCR'

Help Index

Functions for normalisation of RT-qPCR data.

Description

Details

Author(s)

References

Examples

Bladder dataset of Andersen et al (2004)

Description

Usage

Format

Details

Source

References

Examples

Dataset of Andersen et al (2004)

Description

Usage

Format

Details

Source

References

Examples

Colon dataset of Andersen et al (2004)

Description

Usage

Format

Details

Source

References

Examples

Combines Technical Replicates

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Combines Technical Replicates

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Compute Normalized Relative Quantities

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Compute Cq value and amplification efficiency

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Perform normalization with a given housekeeping gene

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also