pcr_curve: Calculate the standard curve model
In pcr: Analyzing Real-Time Quantitative PCR Data

Description Usage Arguments Details Value References Examples

Uses the C_T values and a reference gene and a group, in addition to the intercept and slope of each gene form a serial dilution experiment, to calculate the standard curve model and estimate the normalized relative expression of the target genes.

pcr_curve(
  df,
  group_var,
  reference_gene,
  reference_group,
  mode = "separate_tube",
  intercept,
  slope,
  plot = FALSE,
  ...
)

`df`	A data.frame of C_T values with genes in the columns and samples in rows rows
`group_var`	A character vector of a grouping variable. The length of this variable should equal the number of rows of df
`reference_gene`	A character string of the column name of a control gene
`reference_group`	A character string of the control group in group_var
`mode`	A character string of; 'separate_tube' (default) or 'same_tube'. This is to indicate whether the different genes were run in separate or the same PCR tube
`intercept`	A numeric vector of intercept and length equals the number of genes
`slope`	A numeric vector of slopes length equals the number of genes
`plot`	A logical (default is FALSE)
`...`	Arguments passed to customize plot

this model doesn't assume perfect amplification but rather actively use the amplification in calculating the relative expression. So when the amplification efficiency of all genes are 100% both methods should give similar results. The standard curve method is applied using two steps. First, serial dilutions of the mRNAs from the samples of interest are used as input to the PCR reaction. The linear trend of the log input amount and the resulting C_T values for each gene are used to calculate an intercept and a slope. Secondly, these intercepts and slopes are used to calculate the amounts of mRNA of the genes of interest and the control/reference in the samples of interest and the control sample/reference. These amounts are finally used to calculate the relative expression.

A data.frame of 7 columns

group The unique entries in group_var
gene The column names of df
normalized The normalized expression of target genes relative to a reference_gene
calibrated The calibrated expression of target genes relative to a reference_group
error The standard deviation of normalized relative expression
lower The lower interval of the normalized relative expression
upper The upper interval of the normalized relative expression

When plot is TRUE, returns a bar graph of the calibrated expression of the genes in the column and the groups in the column group. Error bars are drawn using the columns lower and upper. When more one gene are plotted the default in dodge bars. When the argument facet is TRUE a separate panel is drawn for each gene.

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.

# locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- read.csv(fl)

# make a vector of RNA amounts
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# calculate curve
standard_curve <- pcr_assess(ct3, amount = amount, method = 'standard_curve')
intercept <- standard_curve$intercept
slope <- standard_curve$slope

# make grouping variable
group <- rep(c('brain', 'kidney'), each = 6)

# apply the standard curve method
pcr_curve(ct1,
          group_var = group,
          reference_gene = 'GAPDH',
          reference_group = 'brain',
          intercept = intercept,
          slope = slope)

# returns a plot
pcr_curve(ct1,
          group_var = group,
          reference_gene = 'GAPDH',
          reference_group = 'brain',
          intercept = intercept,
          slope = slope,
          plot = TRUE)