deltaDeltaCt: Perform normalization and differential expression with given...

In jimrperkins/NormqPCR: Functions for normalisation of RT-qPCR data

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")

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 [e-mail]

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

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.

See Also

selectHKs, deltaCq

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)

jimrperkins/NormqPCR documentation built on March 6, 2020, 1:06 a.m.