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Compute per-period growth rates for time-course screens

Source: R/fit_SE.timecourse.R
compute_growth_rates.Rd

Stage 1 of the time-course pipeline: fits a growth rate per well within each time window, aggregates replicates, and normalises the rates to the control wells of a reference period. The returned table keeps every treatment, controls included, together with the raw rate and its standard deviation, so it can be reported on directly. Pass it through growth_rates_to_se to obtain the stage 2 input, or use fit_SE.timecourse to run both stages at once.

Usage

compute_growth_rates(
  se,
  periods,
  normalization_map,
  lfc_assay = NULL,
  rate_fn = NULL
)

Arguments

se

SummarizedExperiment with a log-fold-change assay.

periods

named list of numeric vectors of length 2; half-open time windows c(start_hour, end_hour), see fit_SE.timecourse.

normalization_map

named character vector mapping every period to its reference period or to "None", see fit_SE.timecourse.

lfc_assay

string or NULL; name of the log-fold-change assay. NULL uses the input_assay of the "time-course" fit profile (see get_fit_profile).

rate_fn

function or NULL; growth-rate estimator. It receives a data.table holding the timepoints of one well within one window — including the duration column and the log-fold-change column — and must return a single numeric value: the growth rate in doublings per day. NULL uses the slope of lm(LogFoldChange ~ Duration).

Value

A data.table with one row per (CellLine, Drug, Concentration, partner slots, period) — controls included — holding GrowthRate (doublings/day), sd_GrowthRate across replicate wells, rate_0 (the control baseline used, NA for periods mapped to "None" and when no control rows are present), NormalizedGrowthRate and normalization_type. All of these columns are always present, whatever the normalization map or the availability of controls.

Details

Growth rates are computed per (Barcode, WellRow, WellColumn, CellLine, Drug, Concentration, partner drug/concentration slots) group, so single-agent, doublet and triplet arms are never pooled. Control wells are detected via gDRutils::get_env_identifiers("untreated_tag") and a single baseline per (CellLine, period) is used for normalization.

See also

growth_rates_to_se, fit_SE.timecourse, apply_fit

Examples

if (FALSE) { # \dontrun{
periods <- list(early = c(0, 48), late = c(48, 96))
norm_map <- c(early = "None", late = "early")
growth_dt <- compute_growth_rates(se_tc, periods, norm_map)

# Stage 2 with any custom fit function:
se_fit <- apply_fit(
  growth_rates_to_se(growth_dt),
  fit_fn = my_fit_fn,
  data_type = "time-course-metrics",
  output_assay = "Metrics",
  merge = "replace",
  fit_source = "custom"
)
} # }