Calculate delta delta cq (\(\Delta \Delta Cq\)) to globally normalize quantification cycle (log2-fold) data across sample_id.
Source:R/calculate_deltacq.R
calculate_deltadeltacq_bytargetid.RdBy 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.
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, \(2^(-\Delta \Delta Cq)\) |
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.
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")
#> # A tibble: 12 × 10
#> sample_id target_id tech_rep well_row well_col well delta_cq ref_delta_cq
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <dbl>
#> 1 S_1 T_1 1 A 1 A1 1 3
#> 2 S_1 T_1 2 A 2 A2 1 3
#> 3 S_1 T_1 3 A 3 A3 1 3
#> 4 S_norm T_1 1 A 4 A4 3 3
#> 5 S_norm T_1 2 A 5 A5 3 3
#> 6 S_norm T_1 3 A 6 A6 2 3
#> 7 S_1 T_2 1 B 1 B1 4 5
#> 8 S_1 T_2 2 B 2 B2 5 5
#> 9 S_1 T_2 3 B 3 B3 4 5
#> 10 S_norm T_2 1 B 4 B4 5 5
#> 11 S_norm T_2 2 B 5 B5 5 5
#> 12 S_norm T_2 3 B 6 B6 5 5
#> # ℹ 2 more variables: deltadelta_cq <dbl>, fold_change <dbl>
#----- use case 2: mean reference sample_id value
deltacq_tibble %>%
calculate_deltadeltacq_bytargetid(ref_sample_ids = "S_norm",
norm_function = mean)
#> # A tibble: 12 × 10
#> sample_id target_id tech_rep well_row well_col well delta_cq ref_delta_cq
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <dbl>
#> 1 S_1 T_1 1 A 1 A1 1 2.67
#> 2 S_1 T_1 2 A 2 A2 1 2.67
#> 3 S_1 T_1 3 A 3 A3 1 2.67
#> 4 S_norm T_1 1 A 4 A4 3 2.67
#> 5 S_norm T_1 2 A 5 A5 3 2.67
#> 6 S_norm T_1 3 A 6 A6 2 2.67
#> 7 S_1 T_2 1 B 1 B1 4 5
#> 8 S_1 T_2 2 B 2 B2 5 5
#> 9 S_1 T_2 3 B 3 B3 4 5
#> 10 S_norm T_2 1 B 4 B4 5 5
#> 11 S_norm T_2 2 B 5 B5 5 5
#> 12 S_norm T_2 3 B 6 B6 5 5
#> # ℹ 2 more variables: deltadelta_cq <dbl>, fold_change <dbl>