In DrugComb/TidyComb: Preparing drug combination data for DrugComb

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(TidyComb)
library(reshape2)
library(dplyr)

1. Relative Inhibition (RI) for single drug

The Relative Inhibition (RI) score is the proportion of the area under the log10-scaled dose-response curve, to the maximum area or the maximum possible inhibition that one drug can achieve at the same dose range. $$ AUC = \int_{c_1}^{c_2} y_{min} + \frac{y_{max} - y_{min}}{1 + 10 ^ {\lambda(log_{10}{IC_{50} - x'}dx')}} \ RI = 100 \times \frac{AUC-inh_{min}(c_2 - c_1)}{(1-inh_{min}){c_2 - c_1}} $$

, where $x' = log_{10}x$; $[c_1,c_2]$ is the concentration range in which the drug was tested; $inh_{min}$ is the minimum inhibition rate the drug can achieve at the dose range [c~1~, c~2~]. (0 is set by default in function)

Input data should be a data frame which contains two columns:

• dose: concentration at which drug was tested.
• response: percentage inhibition of cell growth at each drug concentrations.

df <- data.frame(dose = c(0, 0.1954, 0.7812, 3.125, 12.5, 50),
                response = c(2.95, 3.76, 18.13, 28.69, 46.66, 58.82))
sens <- CalculateSens(df)
print(sens)

Calculate 95% confidence interval of RI by simulation. The iteration is set as 100 times by default

pred <- CalculateSens(df, pred = TRUE)$pred
RIConfidenceInterval(pred)

2. Drug combination analysis

1. Function for single drug response matrix

The input data is a matrix which contains the drug combination reaponse value. Its column names are doses of drug added along columns. Its row name are doses of drug added along rows.

Note: the matrix should be sorted by:

1. The concentrations along the column increase from left to right;
2. The concentrations along the row increase from top to bottom.

data <- read.csv(system.file("template.csv", package = "TidyComb"),
                 stringsAsFactors = FALSE)
response.mat <- reshape2::acast(conc_r~conc_c, value.var = "inhibition",
                               data = data[data$block_id == 1, ])
response.mat

The CalculateMat function with summary.only = TRUE setting will return a data frame which contains:

1. synergy scores calculated with model: ZIP, Bliss, Loewe, HSA
2. Relative IC50 and RI for two drugs added in the combination block.
3. CSS and S score for drug combination sensitivity and synergy, respectively.

CalculateMat(response.mat, summary.only = TRUE)

By setting summary.only = FALSE, you can get 3 more tables: synergy: percentage inhibitions and synergy scores for each wells in the matrix surface: smoothed response or synergy surface for the combination * curve: model used to fit two single drug dose response curves and fitted parameters.

res <- CalculateMat(response.mat)
res

2. Function for multiple drug combinations

The input data must be a data frame which contains following columns:

• block_id: It is the unique identifier for each drug response (or combination) block.
• drug_row: It is the name (or ID) of the drug which was added along the rows of drug combination testing matrix.
• drug_col: It is the name (or ID) of the drug which was added along the columns of drug combination testing matrix.
• cell_line_name: It is the name (or ID) of cell line on which drug combination response was tested.
• conc_r_unit: The unit of concentrations used when adding drug_row. For example "uM".
• conc_c_unit: The unit of concentrations used when adding drug_col. For example "nM".
• conc_r: The concentrations of drug_row was added in the drug combination test.
• conc_c: The concentrations of drug_col was added in the drug combination test.
• inhibition: The percentage cell growth inhibition at each combinations.

data

CalculateTemplate(data, summary.only = TRUE)

3. Function for parallel calculation

Function ParCalculateTemplate allows you to do the calculation parallelly on multiple cores.

res <- ParCalculateTemplate(data, cores = 4)
res

DrugComb/TidyComb documentation built on June 22, 2022, 2:49 a.m.