pcr (version 1.2.2)

pcr_standard: Calculate the standard curve

Description

Uses the \(C_T\) values from a serial dilution experiment to calculate the a curve for each gene and the log of the input amount

Usage

pcr_standard(df, amount, plot = FALSE)

Arguments

df

A data.frame of \(C_T\) values with genes in the columns and samples in rows rows. Each sample are replicates of a known input/dilution given by amount

amount

A numeric vector of the input amounts or dilutions. The length of this vector should equal the row number of df

plot

A logical (default FALSE) to indicate whether to return a data.frame or a plot

Value

When plot is FALSE returns a data.frame of 4 columns describing the line between the \(C_T\) of each gene and the log of amount

• gene The column names of df

• intercept The intercept of the line

• slope The slope of the line

• r_squared The squared correlation

When plot is TRUE returns a graph instead shows the average and standard deviation of of the \(C_T\) at different input amounts.

Details

Fortunately, regardless of the method used in the analysis of qPCR data, The quality assessment are done in a similar way. It requires an experiment similar to that of calculating the standard curve. Serial dilutions of the genes of interest and controls are used as input to the reaction and different calculations are made. Curves are required for each gene using the $C_T$ value and the log of the input amount. In this case, a separate slope and intercept are required for the calculation of the relative expression when applying the standard curve model.

References

Livak, Kenneth J, and Thomas D Schmittgen. 2001. <U+201C>Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method.<U+201D> Methods 25 (4). ELSEVIER. doi:10.1006/meth.2001.1262.

Examples

# NOT RUN {
# locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

# make amount/dilution variable
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# calculate the standard curve
pcr_standard(ct3,
             amount = amount)

# plot the standard curve
pcr_standard(ct3,
             amount = amount,
             plot = TRUE)

# }