pcr (version 1.2.2)

pcr_curve: Calculate the standard curve model

Description

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.

Usage

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

Arguments

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

Value

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.

Details

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.

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)

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)

# }