Package 'growkar'

Import Growth Data into Canonical Tidy Format

Description

Import adapter that converts vendor plate-reader exports and user tables into the canonical sample/time/od columns expected by GrowthExperiment().

Usage

as_tidy_growth_data(
  data,
  sample_col = "sample",
  time_col = "time",
  od_col = "od",
  sample_sep = "_"
)

Arguments

data	A data frame, tibble, SummarizedExperiment, or object coercible to a tibble.
sample_col	Name of the sample column for long-form input.
time_col	Name of the time column.
od_col	Name of the optical density column.
sample_sep	Separator used to infer metadata columns from sample names in wide data or in long data when sample is present.

Details

This function is an import layer, not an analysis container. Within growkar the canonical container is SummarizedExperiment::SummarizedExperiment. Once data are in a SummarizedExperiment, tidy manipulation and display are better handled by the tidyomics stack: install tidySummarizedExperiment and use dplyr/tidyr/ggplot2 verbs directly on the object rather than round tripping through this function. See vignette("growkar-introduction", package = "growkar") for worked interoperability examples.

Wide input is expected to contain time in the first column and sample names in the remaining column names. Tidy input is expected to contain at least sample, time, and od. When replicate identifiers are encoded in sample names, use a consistent suffix such as ⁠_R1⁠ or ⁠_1⁠ so replicate metadata can be inferred reliably. Common machine-exported column labels such as Time [s], Time [h], Sample, Well, OD600, ⁠OD 600⁠, and ⁠Absorbance 600⁠ are detected automatically where possible, which helps when importing exports from Agilent microplate readers, BioTek Cytation instruments, LogPhase 600, and similar OD600-based plate-reader workflows.

Value

A tibble containing at least sample, time, and od. Additional metadata columns are preserved where available.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
head(tidy_growth)

se <- GrowthExperiment(yeast_growth_data)
head(as_tidy_growth_data(se))

---

Augment Observed Data with Fitted Values

Description

Return the observed data used for fitting together with the fitted values from a GrowthFit object.

Usage

augment_growth_fit(fit, ...)

## S4 method for signature 'GrowthFit'
augment_growth_fit(fit, ...)

Arguments

fit	A GrowthFit object returned by fit_growth_curve().
...	Arguments passed to methods.

Value

A tidy tibble containing the observed columns plus .fitted.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(dplyr::filter(tidy_growth, sample == sample_id))
head(augment_growth_fit(fit))

---

Compute Doubling Time

Description

Compute doubling time from a growth rate estimate.

Usage

compute_doubling_time(mu)

Arguments

mu	Numeric vector of specific growth-rate estimates. In growkar, mu is the slope of log(od) versus time.

Details

This is a low-level helper for converting one or more growth-rate estimates into doubling times. For a convenience summary across all samples, use summarize_growth_metrics().

Value

A numeric vector equal to log(2) / mu, with NA returned when mu <= 0.

Examples

compute_doubling_time(c(0.4, 0.7, NA, -0.1))

---

Compute Growth Rate

Description

Estimate per-sample growth rate from tidy growth data.

Usage

compute_growth_rate(
  data,
  method = c("rolling_window", "defined_interval", "rule_based"),
  interval = NULL,
  select_replicates = NULL,
  average_replicates = FALSE,
  window_size = 5,
  min_od = 0.02,
  first_timepoint = NULL
)

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format. All inputs are standardized internally to the canonical SummarizedExperiment representation before analysis.
method	Estimation method. One of "rolling_window", "defined_interval", or "rule_based".
interval	Interval definition for defined_interval. Supply either a numeric vector of length two to use the same interval for all samples, or a data frame containing sample-specific start and end times. Interval tables may use columns named sample, start_time, and end_time, or the first three columns may be sample, start time, and end time.
select_replicates	Optional character vector of replicate IDs to retain before estimation. When NULL, all replicates are retained.
average_replicates	Logical; if TRUE, average replicates before estimation when replicate metadata are available.
window_size	Rolling window size for rolling_window.
min_od	Minimum OD retained when fitting log-linear models.
first_timepoint	Reference time used by the "rule_based" method.

Details

The returned mu column is the specific growth rate, estimated as the slope of log(od) versus time over the selected interval or window.

Available methods:

• rolling_window: scans rolling windows across the time series and selects the window with the strongest positive log-linear slope.

• defined_interval: fits the growth rate over a user-supplied start and end time interval. Supply either one interval for all samples or a per-sample interval table.

• rule_based: starts from a reference OD, finds the nearest successive OD doublings, and estimates growth from the interval between those doubling anchors.

For the rule_based method, growkar first picks a reference OD at the earliest time point, or at first_timepoint when supplied. It then finds the observations nearest to approximately 2x and 4x that reference OD. The growth rate is estimated from the log-linear slope between those two doubling anchors. This provides a simple empirical approximation when users want a heuristic interval rather than a rolling search or manually defined bounds.

Value

A tibble with sample, mu, start_time, end_time, r_squared, and method. Here mu is the estimated specific growth rate. Additional diagnostic columns describe the number of points used and whether the estimate required degraded fallback behavior. Here degraded = TRUE indicates that the preferred estimation path could not be used cleanly, so the result comes from a reduced-window or fallback path and should be interpreted with extra caution.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
compute_growth_rate(
  dplyr::filter(tidy_growth, sample == sample_id),
  method = "rolling_window"
)
compute_growth_rate(
  tidy_growth,
  method = "defined_interval",
  interval = c(2, 6)
)

---

Detect Candidate Exponential-Phase Windows

Description

Identify rolling windows with the strongest evidence for exponential growth. For multi-sample input, detection is performed separately for each sample. When average_replicates = TRUE, replicate trajectories are averaged first and exponential-phase detection is then run on the averaged curves.

Usage

detect_exponential_phase(
  data,
  select_replicates = NULL,
  average_replicates = FALSE,
  window_size = 5,
  min_od = 0.02
)

Arguments

data	Growth data for one sample or multiple samples in tidy, wide, or SummarizedExperiment format.
select_replicates	Optional character vector of replicate IDs to retain before detection. When NULL, all replicates are retained.
average_replicates	Logical; if TRUE, average replicate trajectories before detection when replicate metadata are available.
window_size	Number of observations in each rolling window.
min_od	Minimum OD retained for log-linear fitting.

Value

A tibble with sample, start_time, end_time, slope, and r_squared, ranked by highest positive slope and then r_squared. The returned tibble also includes rank, n_points, selection_reason, and degraded metadata describing how the candidate windows were selected. For multi-sample input, windows are returned for each sample. Here degraded = TRUE indicates that detection required fallback behavior, such as reducing the requested window size or returning a placeholder result because too few usable observations were available.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
phase_tbl <- detect_exponential_phase(dplyr::filter(tidy_growth, sample == sample_id))
head(phase_tbl)
averaged_phase_tbl <- detect_exponential_phase(
  tidy_growth,
  average_replicates = TRUE
)
head(averaged_phase_tbl)

---

Extract Fitted Growth Parameters

Description

Extract fitted coefficients and model-derived quantities, such as the asymptote and the model-based doubling time, from a GrowthFit object.

Usage

extract_params(fit, ...)

## S4 method for signature 'GrowthFit'
extract_params(fit, ...)

Arguments

fit	A GrowthFit object returned by fit_growth_curve().
...	Arguments passed to methods.

Value

A tibble with one row containing sample, model, asymptote, r, t0, and doubling_time_model.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(dplyr::filter(tidy_growth, sample == sample_id))
extract_params(fit)

---

Fit a Parametric Growth Model

Description

Fit a logistic or Gompertz growth model to a single sample.

Usage

fit_growth_curve(data, model = c("logistic", "gompertz"))

Arguments

data	Growth curve data for one sample in tidy, wide, or SummarizedExperiment format.
model	Model type: "logistic" or "gompertz".

Value

A GrowthFit object. Failed fits return the same class with converged = FALSE and a status slot describing the failure, so that plate-wide fitting is not aborted by a single problematic sample.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(
  dplyr::filter(tidy_growth, sample == sample_id),
  model = "logistic"
)
fit

---

Fit and Store Growth Models

Description

Fit logistic or Gompertz growth models for tidy, wide, or SummarizedExperiment input. When data is a SummarizedExperiment, fitted model objects and extracted parameter summaries are stored in metadata(), and the updated object is returned.

Usage

fit_growth_models(
  data,
  model = c("logistic", "gompertz"),
  store_metadata = TRUE
)

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format.
model	Model type: "logistic" or "gompertz".
store_metadata	Logical; when TRUE and data is a SummarizedExperiment, store fitted models and parameter summaries in metadata().

Value

A tibble with fitted models for non-SummarizedExperiment input, or an updated SummarizedExperiment when data is a SummarizedExperiment.

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
se <- fit_growth_models(se, model = "logistic")
S4Vectors::metadata(se)$growth_model_parameters

---

Fit Growth Models Across a Plate

Description

Split tidy growth data by sample and fit a parametric model to each sample.

Usage

fit_growth_plate(data, model = c("logistic", "gompertz"))

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format.
model	Model type: "logistic" or "gompertz".

Value

A tibble with sample and a list-column of GrowthFit objects.

Examples

data(yeast_growth_data)
fits <- fit_growth_plate(yeast_growth_data, model = "logistic")
fits

---

Growth Assay Matrix

Description

Access the canonical OD assay from a SummarizedExperiment.

Usage

growth_assay(x)

Arguments

x	A SummarizedExperiment compatible with growkar.

Value

A numeric matrix with time points in rows and samples in columns.

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
growth_assay(se)

---

Compute and Store Growth Metrics

Description

Compute growth metrics for tidy, wide, or SummarizedExperiment input. When data is a SummarizedExperiment, the resulting metrics table is stored in metadata(data)$growth_metrics, and the updated object is returned.

Usage

growth_metrics(
  data,
  method = c("rolling_window", "defined_interval", "rule_based"),
  average_replicates = FALSE,
  select_replicates = NULL,
  comparison_col = NULL,
  compare_to = NULL,
  error = c("se", "sd"),
  pvalue_method = c("t_test", "wilcox"),
  store_metadata = TRUE,
  ...
)

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format.
method	Estimation method passed to compute_growth_rate().
average_replicates	Logical; if TRUE, average replicate trajectories before computing metrics. When select_replicates is NULL, all available replicates are averaged.
select_replicates	Optional character vector of replicate IDs to retain before summarizing. When NULL, all replicates are retained.
comparison_col	Optional column used to summarize replicate-level doubling times and compute group-wise statistics. Typical values are "condition" or "sample".
compare_to	Optional reference group in comparison_col used for doubling-time p-value comparisons.
error	Statistic used for doubling-time error bars and summary output. One of "se" or "sd".
pvalue_method	Statistical test used when compare_to is supplied. One of "t_test" or "wilcox".
store_metadata	Logical; when TRUE and data is a SummarizedExperiment, store the derived metrics and analysis parameters in metadata().
...	Additional arguments passed to compute_growth_rate().

Value

A tidy tibble for non-SummarizedExperiment input, or an updated SummarizedExperiment when data is a SummarizedExperiment.

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
se <- growth_metrics(se, method = "rolling_window", average_replicates = TRUE)
S4Vectors::metadata(se)$growth_metrics

---

Stored Growth Model Fits

Description

Retrieve fitted growth model objects stored in metadata().

Usage

growth_model_fits(x)

Arguments

x	A SummarizedExperiment compatible with growkar.

Value

A tibble of stored model fits, or NULL if no fits have been stored.

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
se <- fit_growth_models(se, model = "logistic")
growth_model_fits(se)

---

Construct a GrowthExperiment

Description

Build a GrowthExperiment object, the canonical growkar container, from tidy or wide growth curve data. GrowthExperiment extends SummarizedExperiment::SummarizedExperiment, so the result can be used directly by any Bioconductor package.

Usage

GrowthExperiment(
  data,
  metrics = NULL,
  sample_col = "sample",
  time_col = "time",
  od_col = "od",
  sample_sep = "_"
)

Arguments

data	Growth curve data in tidy or wide format, or a SummarizedExperiment.
metrics	Optional data frame of precomputed sample-level metrics to store in metadata(x)$growth_metrics. Metrics computed by growkar itself are normally attached with growth_metrics() instead.
sample_col	Name of the sample column for long-form input.
time_col	Name of the time column.
od_col	Name of the optical density column.
sample_sep	Separator used to infer metadata columns from sample names.

Details

The resulting object stores time points in rowData(x)$time, sample-level metadata in colData(x), optical density measurements in assay(x, "od"), and derived results in metadata(x).

Coercion methods are also provided, so as(x, "GrowthExperiment") works for a data.frame, tibble, matrix, or existing SummarizedExperiment, and as(x, "SummarizedExperiment") is available in the other direction. Use the constructor when you need to control column-name resolution; use as() for the default behaviour.

Value

A GrowthExperiment object with one assay named od.

See Also

GrowthExperiment

Examples

data(yeast_growth_data)

ge <- GrowthExperiment(yeast_growth_data)
ge

# Equivalent, using standard coercion.
as(yeast_growth_data, "GrowthExperiment")

---

Growth Assay Experiment

Description

GrowthExperiment is the data container used throughout growkar. It is an S4 class that directly extends SummarizedExperiment::SummarizedExperiment, adding a validity method that enforces the canonical growth-assay layout:

Details

• an assay named od holding optical density measurements, with timepoints in rows and samples in columns;

• a numeric time column in rowData();

• sample annotations in colData();

• derived results (growth metrics, exponential-phase windows, model fits) in metadata().

Because the class extends SummarizedExperiment, every method written for SummarizedExperiment — including subsetting, assay(), rowData(), colData(), metadata(), and the tidyomics verbs provided by tidySummarizedExperiment — applies unchanged, and as(x, "SummarizedExperiment") is always available for handing objects to other Bioconductor packages.

Construct objects with GrowthExperiment(), or coerce with as(x, "GrowthExperiment") from a data.frame, tibble, matrix, or existing SummarizedExperiment.

Value

A GrowthExperiment object.

See Also

GrowthExperiment(), validate_growth_experiment()

Examples

data(yeast_growth_data)

ge <- GrowthExperiment(yeast_growth_data)
ge

# Standard coercion in both directions.
se <- as(ge, "SummarizedExperiment")
identical(ge, as(se, "GrowthExperiment"))

# Inherited SummarizedExperiment methods apply unchanged.
SummarizedExperiment::assayNames(ge)
dim(ge[, 1:3])

---

Coerce to a GrowthExperiment

Description

Standard S4 coercion methods for GrowthExperiment. Coercion in the opposite direction, as(x, "SummarizedExperiment"), is inherited from the class hierarchy and always available.

Arguments

from	A data.frame, matrix, or SummarizedExperiment to coerce.

Value

A GrowthExperiment object.

Examples

data(yeast_growth_data)

ge <- as(yeast_growth_data, "GrowthExperiment")
ge

se <- as(ge, "SummarizedExperiment")
as(se, "GrowthExperiment")

---

Accessors for GrowthFit Objects

Description

Extract individual components of a GrowthFit object. These accessors are the supported interface; slots should not be accessed directly with @.

Usage

fit_sample(x, ...)

fit_model(x, ...)

fit_status(x, ...)

fit_converged(x, ...)

fit_data(x, ...)

## S4 method for signature 'GrowthFit'
fit_sample(x, ...)

## S4 method for signature 'GrowthFit'
fit_model(x, ...)

## S4 method for signature 'GrowthFit'
fit_status(x, ...)

## S4 method for signature 'GrowthFit'
fit_converged(x, ...)

## S4 method for signature 'GrowthFit'
fit_data(x, ...)

Arguments

x	A GrowthFit object.
...	Arguments passed to methods.

Value

• fit_sample(): a character scalar naming the fitted sample.

• fit_model(): a character scalar naming the fitted model.

• fit_status(): a character scalar describing the fit outcome.

• fit_converged(): a logical scalar.

• fit_data(): a tibble of the observed data used for fitting.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(dplyr::filter(tidy_growth, sample == sample_id))

fit_sample(fit)
fit_model(fit)
fit_status(fit)
fit_converged(fit)
head(fit_data(fit))

---

Model Components of a GrowthFit Object

Description

Methods for the standard modelling generics stats::coef(), stats::fitted(), stats::residuals(), and stats::nobs().

Usage

## S4 method for signature 'GrowthFit'
coef(object, ...)

## S4 method for signature 'GrowthFit'
fitted(object, ...)

## S4 method for signature 'GrowthFit'
residuals(object, ...)

## S4 method for signature 'GrowthFit'
nobs(object, ...)

Arguments

object	A GrowthFit object.
...	Unused, present for generic compatibility.

Value

• coef(): a named numeric vector of fitted coefficients.

• fitted(): a numeric vector of fitted optical densities.

• residuals(): a numeric vector of observed minus fitted values.

• nobs(): an integer count of observations used for fitting.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(dplyr::filter(tidy_growth, sample == sample_id))

coef(fit)
head(fitted(fit))
head(residuals(fit))
nobs(fit)

---

Compute and Store Exponential-Phase Windows

Description

Detect exponential-phase windows for tidy, wide, or SummarizedExperiment input. When data is a SummarizedExperiment, the detected windows are stored in metadata(data)$exponential_phase_windows, and the updated object is returned.

Usage

phase_windows(
  data,
  window_size = 4L,
  min_od = 0.02,
  average_replicates = FALSE,
  select_replicates = NULL,
  store_metadata = TRUE
)

Arguments

data	Growth data for one sample or multiple samples in tidy, wide, or SummarizedExperiment format.
window_size	Number of observations in each rolling window.
min_od	Minimum OD retained for log-linear fitting.
average_replicates	Logical; if TRUE, average replicate trajectories before detection when replicate metadata are available.
select_replicates	Optional character vector of replicate IDs to retain before detection. When NULL, all replicates are retained.
store_metadata	Logical; when TRUE and data is a SummarizedExperiment, store the detected windows and analysis parameters in metadata().

Value

A tibble for non-SummarizedExperiment input, or an updated SummarizedExperiment when data is a SummarizedExperiment.

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
se <- phase_windows(se, average_replicates = TRUE)
S4Vectors::metadata(se)$exponential_phase_windows

---

Plot Doubling Time Summary

Description

Plot replicate-based doubling-time summaries as a bar chart with error bars and optional bracketed p-value annotations.

Usage

plot_doubling_time(
  data,
  comparison_col = NULL,
  compare_to = NULL,
  exclude_groups = NULL,
  select_replicates = NULL,
  average_replicates = FALSE,
  method = c("rolling_window", "defined_interval", "rule_based"),
  error = c("se", "sd"),
  pvalue_method = c("t_test", "wilcox"),
  palette_name = "all_colors",
  custom_colors = NULL,
  ...
)

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format. Inputs are standardized internally to the canonical SummarizedExperiment representation before plotting.
comparison_col	Column used to group replicate-level doubling times. Typical values are "condition" or "sample".
compare_to	Optional reference group used for p-value comparisons.
exclude_groups	Optional character vector of groups in comparison_col to remove from the plotted summary.
select_replicates	Optional character vector of replicate IDs to retain before computing doubling times.
average_replicates	Logical; if TRUE, compute doubling time from averaged replicate trajectories and plot one bar per averaged sample.
method	Estimation method passed to summarize_growth_metrics().
error	Statistic used for error bars. One of "se" or "sd".
pvalue_method	Statistical test used when compare_to is supplied. One of "t_test" or "wilcox".
palette_name	Name of the qualitative palette used by select_palette().
custom_colors	Optional character vector of colours. When supplied, these user-defined colours are used instead of the selected palette.
...	Additional arguments passed to summarize_growth_metrics().

Value

A ggplot2 object. Requires the suggested package ggplot2.

Examples

data(yeast_growth_data)
plot_doubling_time(
  yeast_growth_data,
  comparison_col = "condition",
  compare_to = "Cg",
  palette_name = "Dark2"
)

---

Plot a Fitted Growth Curve

Description

Overlay observed points and fitted values from a parametric growth model.

Usage

plot_fitted_curve(
  fit,
  model = c("logistic", "gompertz"),
  select_replicates = NULL,
  average_replicates = FALSE,
  colour_col = "sample",
  facet_col = NULL,
  palette_name = "all_colors",
  custom_colors = NULL
)

Arguments

fit	A GrowthFit object, or growth curve data in tidy or wide format.
model	Model type used when fit is raw data rather than a GrowthFit object.
select_replicates	Optional character vector of replicate IDs to retain before fitting. When NULL, all replicates are retained.
average_replicates	Logical; if TRUE, average replicate trajectories before fitting and plotting.
colour_col	Name of the column mapped to colour when plotting from raw data.
facet_col	Optional name of a column used for faceting when plotting from raw data.
palette_name	Name of the qualitative palette used by select_palette(). Supported values include "all_colors" (combined palette), "Accent" (8), "Dark2" (8), "Paired" (12), "Pastel1" (9), "Pastel2" (8), "Set1" (9), "Set2" (8), and "Set3" (12).
custom_colors	Optional character vector of colours. When supplied, these user-defined colours are used instead of the selected palette.

Value

A ggplot2 object. Requires the suggested package ggplot2.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(dplyr::filter(tidy_growth, sample == sample_id))
plot_fitted_curve(fit)

---

Plot Growth Curves

Description

Plot observed growth curves from growth data standardized internally to a canonical SummarizedExperiment.

Usage

plot_growth_curve(
  data,
  average_replicates = FALSE,
  colour_col = "sample",
  facet_col = NULL,
  palette_name = "all_colors",
  custom_colors = NULL
)

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format.
average_replicates	Logical; if TRUE, average replicate trajectories before plotting when a replicate column is available.
colour_col	Name of the column mapped to colour.
facet_col	Optional name of a column used for faceting.
palette_name	Name of the qualitative palette used by select_palette(). Supported values include "all_colors" (combined palette), "Accent" (8), "Dark2" (8), "Paired" (12), "Pastel1" (9), "Pastel2" (8), "Set1" (9), "Set2" (8), and "Set3" (12).
custom_colors	Optional character vector of colours. When supplied, these user-defined colours are used instead of the selected palette.

Value

A ggplot2 object. Requires the suggested package ggplot2.

Examples

data(yeast_growth_data)
plot_growth_curve(
  yeast_growth_data,
  average_replicates = TRUE,
  colour_col = "condition",
  palette_name = "Dark2"
)

---

Plot Growth Curves as Facets

Description

Plot observed growth curves with replicates averaged and grouped into separate sample-family facets. For condition labels such as KN99(100) and CM2448(50), this produces one facet for KN99, one for CM2448, and so on, with the different conditions shown within each facet. By default, when labels include parenthesized dose values such as (0) or (100), those shared values are used for colour mapping across facets.

Usage

plot_growth_curve_facets(
  data,
  colour_col = NULL,
  palette_name = "all_colors",
  custom_colors = NULL
)

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format.
colour_col	Name of the column mapped to colour. When NULL, the plot uses a derived dose label from condition/sample when available and otherwise falls back to condition or sample.
palette_name	Name of the qualitative palette used by select_palette(). Supported values include "all_colors" (combined palette), "Accent" (8), "Dark2" (8), "Paired" (12), "Pastel1" (9), "Pastel2" (8), "Set1" (9), "Set2" (8), and "Set3" (12).
custom_colors	Optional character vector of colours. When supplied, these user-defined colours are used instead of the selected palette.

Value

A ggplot2 object. Requires the suggested package ggplot2.

Examples

data(yeast_growth_data)
plot_growth_curve_facets(
  yeast_growth_data,
  palette_name = "Dark2"
)

---

Sample Metadata

Description

Access sample-level metadata from a SummarizedExperiment.

Usage

sample_data(x)

Arguments

x	A SummarizedExperiment compatible with growkar.

Value

A tibble containing colData(x).

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
sample_data(se)

---

Display a GrowthFit Object

Description

Compact one-line summary of a fitted growth model.

Usage

## S4 method for signature 'GrowthFit'
show(object)

Arguments

object

A GrowthFit object.

Value

object, invisibly. Called for its side effect of printing.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(dplyr::filter(tidy_growth, sample == sample_id))
fit

---

Summarize Growth Metrics

Description

Compute growth rate and doubling time per sample.

Usage

summarize_growth_metrics(
  data,
  method = c("rolling_window", "defined_interval", "rule_based"),
  average_replicates = FALSE,
  select_replicates = NULL,
  comparison_col = NULL,
  compare_to = NULL,
  error = c("se", "sd"),
  pvalue_method = c("t_test", "wilcox"),
  ...
)

Arguments

data	Growth curve data in tidy, wide, or SummarizedExperiment format.
method	Estimation method passed to compute_growth_rate().
average_replicates	Logical; if TRUE, average replicate trajectories before computing metrics. When select_replicates is NULL, all available replicates are averaged.
select_replicates	Optional character vector of replicate IDs to retain before summarizing. When NULL, all replicates are retained.
comparison_col	Optional column used to summarize replicate-level doubling times and compute group-wise statistics. Typical values are "condition" or "sample".
compare_to	Optional reference group in comparison_col used for doubling-time p-value comparisons.
error	Statistic used for doubling-time error bars and summary output. One of "se" or "sd".
pvalue_method	Statistical test used when compare_to is supplied. One of "t_test" or "wilcox".
...	Additional arguments passed to compute_growth_rate().

Details

This is the convenience wrapper for obtaining both metrics across multiple samples in one tidy table. Internally, doubling time is derived from the estimated growth rate using compute_doubling_time(). Inputs are standardized internally to the canonical SummarizedExperiment representation before summary tables are derived.

Value

A tidy tibble with one row per sample by default. When comparison_col is supplied, returns one row per comparison group with replicate-level doubling-time summary statistics and optional p-values.

Examples

data(yeast_growth_data)
metrics <- summarize_growth_metrics(
  yeast_growth_data,
  method = "rolling_window",
  average_replicates = TRUE
)
head(metrics)

---

Summarize a GrowthFit Object

Description

Tabular summary of fit status and goodness-of-fit statistics.

Usage

## S4 method for signature 'GrowthFit'
summary(object, ...)

Arguments

object	A GrowthFit object.
...	Unused, present for generic compatibility.

Value

A one-row tibble with sample, model, converged, status, message, n_points, rss, aic, and bic.

Examples

data(yeast_growth_data)
tidy_growth <- as_tidy_growth_data(yeast_growth_data)
sample_id <- unique(tidy_growth$sample)[1]
fit <- fit_growth_curve(dplyr::filter(tidy_growth, sample == sample_id))
summary(fit)

---

Timepoint Metadata

Description

Access timepoint metadata from a SummarizedExperiment.

Usage

timepoints(x)

Arguments

x	A SummarizedExperiment compatible with growkar.

Value

A tibble containing rowData(x).

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
timepoints(se)

---

Validate Canonical Growth Data

Description

Validate tidy growth data before downstream analysis.

Usage

validate_growth_data(
  data,
  allow_negative_od = FALSE,
  require_increasing_time = TRUE,
  min_points_per_sample = 2L,
  warn_zero_od = FALSE,
  require_finite = TRUE
)

Arguments

data	A data frame containing at least sample, time, and od.
allow_negative_od	Logical; if FALSE, negative optical density values are rejected.
require_increasing_time	Logical; if TRUE, time must be strictly increasing within each sample.
min_points_per_sample	Minimum number of rows required within each sample.
warn_zero_od	Logical; if TRUE, emit a warning when zero OD values are present.
require_finite	Logical; if TRUE, reject non-finite numeric values in time or od.

Value

The validated tibble after coercion to tibble.

Examples

data(yeast_growth_data)
tidy_data <- as_tidy_growth_data(yeast_growth_data)
validate_growth_data(tidy_data)

---

Validate a Growth SummarizedExperiment

Description

Check that a SummarizedExperiment follows the canonical growkar layout.

Usage

validate_growth_experiment(x, require_finite = TRUE)

Arguments

x	A SummarizedExperiment.
require_finite	Logical; if TRUE, reject non-finite assay values and time values.

Details

The layout itself (an od assay, a numeric rowData(x)$time of matching length) is enforced by the GrowthExperiment validity method, which this function calls rather than re-checking. Only the finiteness requirement, which the class deliberately does not impose, is checked here.

Value

The validated SummarizedExperiment.

Examples

data(yeast_growth_data)
se <- GrowthExperiment(yeast_growth_data)
validate_growth_experiment(se)

---

Optical Density (OD600) of growing yeast cells

Description

A dataset containing the time-interval and OD600 of growing yeast cells recorded upto 24 hours.

Format

A data frame with 49 rows and 6 variables:

Time

Time, time-interval at which OD600 was recorded

Cg_R1, Cg_R2, Cg_R3

Optical Density (OD600) of yeast cells, at each time-interval

CgFlu_R1, CgFlu_R2, CgFlu_R3

Optical Density (OD600) of yeast cells treated with fluconazole, at each time-interval

YPD_R1, YPD_R2, YPD_R3

Optical Density (OD600) of medium without cells as control, at each time-interval

...

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