pcr (version 1.2.2)

pcr_dct: Calculate the delta_ct model

Description

Uses the \(C_T\) values and a reference group to calculate the delta \(C_T\) model to estimate the relative fold change of a gene between groups

Usage

pcr_dct(
  df,
  group_var,
  reference_group,
  mode = "separate_tube",
  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_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

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

• calibrated The average \(C_T\) value of target genes after subtracting that of the reference_group

• fold_change The fold change of genes relative to a reference_group

• error The standard deviation of the fold_change

• lower The lower interval of the fold_change

• upper The upper interval of the fold_change

When plot is TRUE, returns a bar graph of the fold change 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 method is a variation of the double delta \(C_T\) model, pcr_ddct. It can be used to calculate the fold change of in one sample relative to the others. For example, it can be used to compare and choosing a control/reference genes.

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

# make a data.frame of two identical columns
pcr_hk <- data.frame(
  GAPDH1 = ct1$GAPDH,
  GAPDH2 = ct1$GAPDH
  )

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

# calculate caliberation
pcr_dct(pcr_hk,
        group_var = group_var,
        reference_group = 'brain')

# returns a plot
pcr_dct(pcr_hk,
        group_var = group_var,
        reference_group = 'brain',
        plot = TRUE)

# returns a plot with facets
pcr_dct(pcr_hk,
        group_var = group_var,
        reference_group = 'brain',
        plot = TRUE,
        facet = TRUE)

# }