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Flag concentration-dependent CYP mRNA down-regulation

induction_downregulation.Rd

[Experimental]

For each CYP isoform (and, by default, each hepatocyte donor), decide whether vehicle-normalized mRNA fold-change falls as precipitant concentration rises, rather than being induced.

Usage

induction_downregulation(x, fold_threshold = 0.5, by_donor = TRUE)

Arguments

x

An induction_experiment object. A FOLD column is required.

fold_threshold

Maximum fold-change at the highest concentration that still counts as suppression, as numeric. Defaults to 0.5.

by_donor

If TRUE (default), one row per isoform and donor. If FALSE, donors are pooled and there is one row per isoform.

Value

A data frame with one row per isoform, or per isoform and donor, and the columns:

  • OBJECT CYP isoform.

  • DONOR Hepatocyte donor (omitted if by_donor = FALSE).

  • n Number of concentrations used.

  • min_fold, max_fold Range of FOLD.

  • fold_maxc FOLD at the highest CONC.

  • rho Spearman correlation of CONC and FOLD.

  • downregulation TRUE if the curve meets both criteria above.

Details

Only SAMPLE == "test" rows with CONC > 0 and non-missing FOLD are used. A curve is flagged when both of the following hold:

  • fold-change at the highest tested concentration is below fold_threshold (default 0.5, i.e. ≤ 50% of vehicle);

  • Spearman's rank correlation of CONC with FOLD is negative, so fold-change tends to decrease as concentration increases.

FOLD is fold vs vehicle (1 = no change, < 1 = suppression). Percent of positive control (REL) is not used. Isolated FOLD < 1 points at low concentration, which occur in ordinary induction curves, do not trigger the flag.

The rank correlation uses order only, not a linear fit on the µM scale. No p-value is required; typical induction experiments have too few concentrations for a Spearman test to be informative.

Examples

induction_downregulation(examplinib_in_vitro_ind)
#> # A tibble: 6 × 8
#>   OBJECT DONOR     n min_fold max_fold fold_maxc   rho downregulation
#>   <chr>  <chr> <int>    <dbl>    <dbl>     <dbl> <dbl> <lgl>         
#> 1 CYP1A2 A         4    1.38      7.37      7.37 1     FALSE         
#> 2 CYP1A2 B         4    1.24      5.09      5.09 1     FALSE         
#> 3 CYP1A2 C         4    1.56      4.15      4.15 1     FALSE         
#> 4 CYP3A4 A         7    0.904     6.33      6.33 1     FALSE         
#> 5 CYP3A4 B         8    0.655     2.31      2.31 0.619 FALSE         
#> 6 CYP3A4 C         8    0.648     1.58      1.58 0.238 FALSE         
induction_downregulation(examplinib_in_vitro_ind1)
#> # A tibble: 9 × 8
#>   OBJECT DONOR     n min_fold max_fold fold_maxc   rho downregulation
#>   <chr>  <chr> <int>    <dbl>    <dbl>     <dbl> <dbl> <lgl>         
#> 1 CYP1A2 A         4    0.087    0.951     0.087  -1   TRUE          
#> 2 CYP1A2 B         4    0.188    1.54      0.188  -0.8 TRUE          
#> 3 CYP1A2 C         4    0.072    1.09      0.072  -1   TRUE          
#> 4 CYP2B6 A         4    0.188    1.07      0.188  -0.8 TRUE          
#> 5 CYP2B6 B         4    0.341    1.36      0.341  -0.8 TRUE          
#> 6 CYP2B6 C         4    0.107    0.978     0.107  -1   TRUE          
#> 7 CYP3A4 A         4    0.285    0.973     0.285  -0.2 TRUE          
#> 8 CYP3A4 B         4    0.293    1.79      0.293  -0.8 TRUE          
#> 9 CYP3A4 C         4    0.286    1.06      0.286  -0.8 TRUE