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
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
A numeric vector of the input amounts or dilutions. The length of this vector should equal the row number of df
A logical (default FALSE) to indicate whether to return a data.frame or a plot
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.
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.
Livak, Kenneth J, and Thomas D Schmittgen. 2001. “Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method.” Methods 25 (4). ELSEVIER. doi:10.1006/meth.2001.1262.
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# 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)
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