Quantitative real-time PCR is an important technique in medical and biomedical applications. The pcr
package provides a unified interface for quality assessing, analyzing and testing qPCR data for statistical significance. The aim of this document is to describe the different methods and modes used to relatively quantify gene expression of qPCR and their implementation in the pcr
package.
The pcr
is available on CRAN. To install it, use:
# install package CRAN
install.packages('pcr')
The development version of the package can be obtained through:
# install package from github (under development)
devtools::install_github('MahShaaban/pcr')
# load required libraries
library(pcr)
The following chunk of code locates a dataset of CT values of two genes from 12 different samples and performs a quick analysis to obtain the expression of a target gene c-myc normalized by a control GAPDH in the Kidney samples relative to the brain samples. pcr_analyze
provides different methods, the default one that is used here is 'delta_delta_ct' applies the popular Double Delta CT method.
# default mode delta_delta_ct
## locate and read raw ct data
fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- readr::read_csv(fl)
## add grouping variable
group_var <- rep(c('brain', 'kidney'), each = 6)
# calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(ct1,
group_var = group_var,
reference_gene = 'GAPDH',
reference_group = 'brain')
res
The output of pcr_analyze
is explained in the documentation of the function ?pcr_analyze
and the method it calls ?pcr_ddct
. Briefly, the input includes the CT value of c-myc normalized
to the control GAPDH, The calibrated
value of c-myc in the kidney relative to the brain samples and the final relative_expression
of c-myc. In addition, an error
term and a lower
and upper
intervals are provided.
The previous analysis makes a few assumptions. One of which is a perfect amplification efficiency of the PCR reaction. To assess the validity of this assumption, pcr_assess
provides a method called efficiency
. The input data.frame
is the CT values of c-myc and GAPDH at different input amounts/dilutions.
## locate and read data
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- readr::read_csv(fl)
## make a vector of RNA amounts
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)
## calculate amplification efficiency
res <- pcr_assess(ct3,
amount = amount,
reference_gene = 'GAPDH',
method = 'efficiency')
res
In the case of using the Double Delta C_T, the assumption of the amplification efficiency is critical for the reliability of the model. In particular, the slope and the R^2 of the line between the log input amount and Delta C_T or difference between the CT value of the target c-myc and GAPDH. Typically, The slope should be very small (less than 0.01). The slope
here is appropriate, so the assumption holds true.
?pcr_analyze
?pcr_test
browseVignettes("pcr")
Alternatively, the vignette can be found online, here.
citation("pcr")
For details about the methods and more examples, check out our PeerJ article.
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