create_synergy_dataset: Create a synergy dataset from raw data without a plate map

In DavidQuigley/HTDoseResponseCurve: Dose Response Curve Evaluation from Incucyte and Other High-Throughput Platforms

Description

A synergy dataset differs from a standard dataset in that each value is the result of combining two distinct treatments in two distinct concentrations. If there are measurements where only one treatment was present, the other treatment should be specified to have a concentration equal to zero.

Usage

create_synergy_dataset(sample_types, treatments_1, treatments_2,
  concentrations_1, concentrations_2, values, hours = 0,
  plate_id = "plate_1", negative_control = NA, summary_method = "mean")

Arguments

sample_types	vector of sample types
treatments_1	vector of treatment one
treatments_2	vector of treatment two
concentrations_1	vector of concentrations of treatment one
concentrations_2	vector of concentrations of treatment two
values	vector of measured response to treatments one and two
hours	time points for each observation. If a number, the same time point is assigned to all observations. If a vector, there should be one number for each observation. Defaults to 0.
plate_id	experiment identification string, useful if multiple datasets are later combined. Defaults to "plate_1"
negative_control	A designation for the negative controls in this dataset, if they exist. Value may be NA, a number, a string, or a data frame. NA: Use when there are no negative control measurements. The contents of the column named 'value_normalized' will be copied from the contents of the column named 'value'. Number: Use when each treatment has been labeled with a concentration (typically 0) that indicates the vehicle control. Each treatment must contain one or more observations with this concentration, and these observations will be the negative controls. string: Use when a single set of observations is a universal control. The treatment whose name matches the string is the universal negative control all of the data. data frame: Use when more than one negative control exists, and you have to map different treatments to a particular negative control. The data frame must have names 'drug' and 'vehicle', and the data frame will map match treatments in the 'drug' column to those in the 'vehicle' column.
summary_method	summarize replicate measures by either mean or median; must be one of "mean", "median". Defaults to "mean"

Value

a data frame where columns indicate the sample type, treatment 1, treatment 2, concentration of treatment 1, concentration of treatment 2, observed raw value, normalized value, name of the negative_control treatment, whether a particular row is a negative control for at least one other row, hours since the start time, and plate of origin.

Examples

dose_SCH=c(0.1, 0.5, 1, 2, 4)
eff_SCH = c(0.6701, 0.6289, 0.5577, 0.4550, 0.3755)
dose_4HPR = c(0.1, 0.5, 1, 2)
eff_4HPR = c(0.7666, 0.5833, 0.5706, 0.4934)
eff_comb = c(0.6539, 0.4919, 0.3551, 0.2341)
syn = data.frame( 
    treatment_1 = rep("SCH66336", 13),
    conc_1 = c( dose_SCH, rep(0, 4), dose_SCH[1:4]),
    treatment_2 = rep("4-HPR", 13),
    conc_2 = c( rep(0, 5), dose_4HPR, dose_4HPR ),
    values = c(eff_SCH, eff_4HPR, eff_comb ), stringsAsFactors=FALSE )
ds_lk = create_synergy_dataset( sample_types = rep("sample_1", 13), 
                                treatments_1 = syn$treatment_1,
                                treatments_2 = syn$treatment_2,
                                concentrations_1 = syn$conc_1,
                                concentrations_2 = syn$conc_2,
                                values = syn$values)

DavidQuigley/HTDoseResponseCurve documentation built on Jan. 23, 2021, 5:10 a.m.