Calculate delta cq (\(\Delta Cq\)) to normalize quantification cycle (log2-fold) data within sample_id.
Source:R/calculate_deltacq.R
calculate_deltacq_bysampleid.RdThis function implements relative quantification by the delta Cq method. For each sample, the Cq values of all targets (e.g. genes, probes, primer sets) are compared to one or more reference target ids specified in `ref_target_ids`.
Arguments
- cq_df
a data frame containing columns `sample_id`, value_name (default `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. See tidyqpcr vignettes for examples.
- ref_target_ids
names of targets to normalize by, i.e. reference genes, hydrolysis probes, or primer sets. This can be one reference target id, a selection of multiple target ids, or even all measured target ids. In the case of all of them, the delta Cq value would be calculated relative to the median (or other `norm_function`) of all measured targets.
- norm_function
Function to use to calculate the value to normalize by on given scale. Default is median, alternatively could use mean.
Value
data frame like cq_df with three additional columns:
| ref_cq | summary (median/mean) cq value for reference target ids |
| delta_cq | normalized value, \(\Delta Cq\) |
| rel_abund | normalized ratio, \(2^(-\Delta Cq)\) |
Examples
# create simple cq dataset with two samples, two targets and 3 reps
cq_tibble <- tibble(sample_id = rep(c("S_1","S_1","S_1", "S_2", "S_2", "S_2"), 2),
target_id = rep(c("T_1",
"T_norm"), 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),
cq = c(10, 10, 10, 12, 12, 11,
9, 9, 9, 9, 9, 9))
# calculate deltacq using reference target_id called 'T_norm'
#----- use case 1: median reference target_id value
cq_tibble %>%
calculate_deltacq_bysampleid(ref_target_ids = "T_norm")
#> # A tibble: 12 × 10
#> sample_id target_id tech_rep well_row well_col well cq ref_cq delta_cq
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <dbl> <dbl>
#> 1 S_1 T_1 1 A 1 A1 10 9 1
#> 2 S_1 T_1 2 A 2 A2 10 9 1
#> 3 S_1 T_1 3 A 3 A3 10 9 1
#> 4 S_1 T_norm 1 B 1 B1 9 9 0
#> 5 S_1 T_norm 2 B 2 B2 9 9 0
#> 6 S_1 T_norm 3 B 3 B3 9 9 0
#> 7 S_2 T_1 1 A 4 A4 12 9 3
#> 8 S_2 T_1 2 A 5 A5 12 9 3
#> 9 S_2 T_1 3 A 6 A6 11 9 2
#> 10 S_2 T_norm 1 B 4 B4 9 9 0
#> 11 S_2 T_norm 2 B 5 B5 9 9 0
#> 12 S_2 T_norm 3 B 6 B6 9 9 0
#> # ℹ 1 more variable: rel_abund <dbl>
#----- use case 2: mean reference target_id value
cq_tibble %>%
calculate_deltacq_bysampleid(ref_target_ids = "T_norm",
norm_function = mean)
#> # A tibble: 12 × 10
#> sample_id target_id tech_rep well_row well_col well cq ref_cq delta_cq
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <dbl> <dbl>
#> 1 S_1 T_1 1 A 1 A1 10 9 1
#> 2 S_1 T_1 2 A 2 A2 10 9 1
#> 3 S_1 T_1 3 A 3 A3 10 9 1
#> 4 S_1 T_norm 1 B 1 B1 9 9 0
#> 5 S_1 T_norm 2 B 2 B2 9 9 0
#> 6 S_1 T_norm 3 B 3 B3 9 9 0
#> 7 S_2 T_1 1 A 4 A4 12 9 3
#> 8 S_2 T_1 2 A 5 A5 12 9 3
#> 9 S_2 T_1 3 A 6 A6 11 9 2
#> 10 S_2 T_norm 1 B 4 B4 9 9 0
#> 11 S_2 T_norm 2 B 5 B5 9 9 0
#> 12 S_2 T_norm 3 B 6 B6 9 9 0
#> # ℹ 1 more variable: rel_abund <dbl>