calculate_deltadeltacq_bytargetid: Calculate delta delta cq (Delta Delta Cq) to globally...

In ewallace/tidyqpcr: Quantitative PCR Analysis with the Tidyverse

Calculate delta delta cq (\Delta \Delta Cq) to globally normalize quantification cycle (log2-fold) data across sample_id.

Description

By default, \Delta \Delta Cq is positive if a target is more highly detected in the relevant sample, compared to reference samples. This can be flipped by setting the parameter 'ddcq_positive' to 'FALSE'. In either case, The fold change, 2^{\Delta \Delta Cq}, is also reported.

Usage

calculate_deltadeltacq_bytargetid(
  deltacq_df,
  ref_sample_ids,
  norm_function = median,
  ddcq_positive = TRUE
)

Arguments

deltacq_df	a data frame containing columns 'sample_id', value_name (default 'delta_cq') and tid_name (default 'target_id'). Crucially, sample_id should be the same for different technical replicates measuring identical reactions in different wells of the plate, but differ for different biological and experimental replicates. Usually this will be a data frame that was output by 'calculate_deltacq_bysampleid'.
ref_sample_ids	reference sample_ids to normalize by
norm_function	Function to use to calculate the value to normalize by on given scale. Default is median, alternatively could use mean.
ddcq_positive	(default TRUE) output \Delta \Delta Cq as positive if a target is more highly detected in the relevant sample, compared to reference samples.

Details

This function does a global normalization, where all samples are compared to one or more reference samples specified in 'ref_sample_ids'. There are other experimental designs that require comparing samples in pairs or small groups, e.g. a time course comparing 'delta_cq' values against a reference strain at each time point. For those situations, instead we recommend adapting code from this function, changing the grouping variables used in to 'dplyr::group_by' to draw the contrasts appropriate for the experiment.

Value

data frame like cq_df with three additional columns:

ref_delta_cq	summary (median/mean) \Delta Cq value for target_id in reference sample ids
deltadelta_cq	the normalized value, \Delta \Delta Cq
fold_change	the normalized fold-change ratio, 2^(-\Delta \Delta Cq)

Examples

# create simple deltacq dataset with two samples, two targets and 3 reps
deltacq_tibble <- tibble(sample_id = rep(c("S_1","S_1","S_1", "S_norm", "S_norm", "S_norm"), 2),
                     target_id = rep(c("T_1",
                                       "T_2"), each = 6),
                     tech_rep = rep(1:3, 4),
                     well_row = rep(c("A",
                                      "B"), each = 6),
                     well_col = rep(1:6, 2),
                     well = paste0(well_row,well_col),
                     delta_cq = c(1, 1, 1, 3, 3, 2,
                                  4, 5, 4, 5, 5, 5))
                     
# calculate deltadeltacq using reference target_id called 'S_norm'

#----- use case 1: median reference sample_id value
deltacq_tibble |>
    calculate_deltadeltacq_bytargetid(ref_sample_ids = "S_norm")

#----- use case 2: mean reference sample_id value 
deltacq_tibble |>
    calculate_deltadeltacq_bytargetid(ref_sample_ids = "S_norm",
                                 norm_function = mean)

ewallace/tidyqpcr documentation built on June 5, 2024, 10:04 a.m.