Package {diffHTS}

diffHTS: Differential Drug Sensitivity Analysis for Two-Condition High-Throughput Screens

Description

The diffHTS package bundles reusable methods for analysing high-throughput drug screening (HTS) experiments that contrast two conditions, for example irradiated versus non-irradiated cells or cancer versus normal cell lines. The typical workflow is:

Details

1. Fit four-parameter logistic dose-response curves per drug and condition with fit_dose_response() and summarise potency with compute_auc().

2. Assess plate quality with calculate_qc_metrics() and flag_qc_plates().

3. Contrast the two conditions with compute_delta_auc() to obtain a differential AUC and its within-experiment z-score.

4. Nominate hits with select_hits_cutoff() or select_hits_sigma().

5. Visualise results with plot_delta_auc_heatmap(), plot_condition_scatter() and plot_qc_boxplot().

Two small demonstration datasets, screen_doseresponse and screen_delta_auc, are provided to run the examples end to end.

Author(s)

Maintainer: Liang Tang xphdtangliang@gmail.com

Authors:

• Liang Tang xphdtangliang@gmail.com

Annotate hits with compound metadata

Description

Joins external compound metadata (name, target, mechanism, library source) onto a hit table.

Usage

annotate_hit_info(hits, meta, by = "compound_id")

Arguments

Value

hits with the metadata columns merged in (left join), row order preserved.

See Also

rank_hit_compound(), export_hit_table()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
annotate_hit_info(rank_hit_compound(params), hts_compound_meta)

Stamp a plate-map layout onto measured readings

Description

Joins a user-defined layout from read_plate_layout() onto a table of raw signal readings, matching by well address (and plate identifier when both sides carry one), and returns a standard hts_raw object. This keeps the what was measured (signal) and the what each well is (role, compound, concentration) in separate files, exactly as a screening lab records them, and lets the same signal export be re-interpreted against different layouts.

Usage

apply_plate_layout(
  readings,
  layout,
  well_col = "well",
  signal_col = "signal",
  plate_col = "plate_id",
  plate_id = NULL
)

Arguments

Value

An hts_raw object with columns plate_id, well, row, col, well_type, compound_id, concentration and signal, ready for the rest of the pipeline.

See Also

read_plate_layout(), read_hts_plate()

Examples

f <- system.file("extdata", "plate_layout_example.csv", package = "diffHTS")
layout <- read_plate_layout(f, plate_id = "P01")
# Simulate a signal export for the same wells.
readings <- data.frame(well = layout$well,
                        signal = runif(nrow(layout), 1e4, 1e5))
raw <- apply_plate_layout(readings, layout, plate_id = "P01")
table(raw$well_type)

Subtract the blank background signal from a plate

Description

Removes the per-plate instrument background by subtracting the mean blank (no-cell) signal from every well, a standard first step for luminescence and fluorescence readouts. The original signal is preserved in signal_raw.

Usage

baseline_subtract(hts_raw, blank_label = "Blank", clip_zero = TRUE)

Arguments

Value

The input object with signal replaced by the background-subtracted value and a new signal_raw column holding the original signal.

See Also

read_hts_plate(), norm_by_control()

Examples

raw <- read_hts_plate(hts_primary_raw)
bs <- baseline_subtract(raw)
head(bs[, c("well_type", "signal_raw", "signal")])

Build a compound-by-sample AUC matrix

Description

Reshapes long AUC results into a numeric matrix with compounds as rows and samples (for example cell lines or batches) as columns, optionally standardised to Z-scores for comparability.

Usage

build_auc_matrix(
  data,
  row_col = "compound_id",
  col_col = "cell_line",
  value_col = "auc",
  zscore = c("none", "row", "column")
)

Arguments

Value

A numeric matrix (compounds x samples). The standardisation used is stored in attr(x, "zscore").

See Also

cluster_auc_matrix(), plot_auc_heatmap()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
build_auc_matrix(drc$meta)

Control coefficient of variation per plate

Description

The percent CV (100 * SD / mean) of each control population. What it is for: CV% is the direct measure of pipetting and reagent-handling reproducibility within a plate. Low, stable control CVs are a precondition for every other metric; a control drifting above ~15–20% flags a liquid- handling or edge-effect problem even when the Z'-factor still passes.

Usage

calc_cv(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC",
  cv_max = 20
)

Arguments

Value

A data frame with one row per plate: plate_id, cv_nc, cv_pc (percent) and a logical pass (both controls at or below cv_max).

See Also

calc_plate_qc(), plot_plate_qc()

Examples

calc_cv(read_hts_plate(hts_primary_raw))

Area under the dose-response curve

Description

Computes the area under each fitted curve by trapezoidal integration of the fitted 4PL over the tested concentration range (on the log10 scale), a robust single-number summary of overall potency.

Usage

calc_drc_auc(drc)

Arguments

Value

The hts_drc object with an auc column added to its meta, also returned invisibly as a data frame via drc$meta.

See Also

compute_auc(), extract_drc_params()

Examples

drc <- fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line")
calc_drc_auc(drc)$meta

Comprehensive per-plate quality assessment

Description

Computes the full panel of assay-quality metrics used to judge a high-throughput screening plate, in a single per-plate table. Alongside the classic Z'-factor this reports the median/MAD-based robust Z'-factor, the sample-aware Z-factor, SSMD, the signal-to-background (S/B) and signal-to-noise (S/N) ratios, and the coefficient of variation (CV%) of each control population. All of these can then be visualised through the single entry point plot_plate_qc().

Usage

calc_plate_qc(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC",
  sample_label = "compound",
  z_prime_min = 0.5
)

Arguments

Details

Each metric also has its own dedicated function, useful when you only need one number (and each with its own acceptance threshold): calc_z_prime(), calc_robust_z_prime(), calc_z_factor(), calc_ssmd(), calc_sb_ratio(), calc_sn_ratio() and calc_cv(). calc_plate_qc() uses the very same formulas so the numbers agree.

What each metric tells you, with the high-signal control taken as whichever of NC/PC has the larger mean and the low-signal control as the other:

• CV% = 100\,\sigma/\mu of each control — pipetting/reagent reproducibility; lower is better.

• S/B = \mu_{hi}/\mu_{lo} — raw dynamic range of the assay.

• S/N = (\mu_{hi}-\mu_{lo})/\sigma_{lo} — assay window relative to background noise.

• Z'-factor = 1 - 3(\sigma_{nc}+\sigma_{pc})/|\mu_{nc}-\mu_{pc}| — overall control-only assay quality (⁠>= 0.5⁠ excellent).

• Robust Z'-factor: as Z'-factor but with medians and MADs, resistant to outlier control wells.

• Z-factor = 1 - 3(\sigma_{s}+\sigma_{nc})/|\mu_{s}-\mu_{nc}| using the sample population s — is the window large versus the library spread (often low/negative in primary screens).

• SSMD = (\mu_{nc}-\mu_{pc})/\sqrt{\sigma_{nc}^2+\sigma_{pc}^2} — effect-size measure of control separation for hit cut-offs.

Value

A data frame with one row per plate and the columns plate_id, mean_nc, mean_pc, sd_nc, sd_pc, cv_nc, cv_pc, signal_window, sb (signal-to-background), sn (signal-to-noise), z_prime, robust_z_prime, z_factor, ssmd and a logical pass.

See Also

plot_plate_qc(), calc_z_prime(), calc_well_zscore()

Examples

raw <- read_hts_plate(hts_primary_raw)
calc_plate_qc(raw)

Pairwise replicate correlation between plates

Description

Computes the Pearson correlation (and its R^2) between the per-compound activity of every pair of replicate plates, the standard check that technical replicates agree.

Usage

calc_replicate_correlation(
  data,
  value = "viability",
  compound_col = "compound_id",
  plate_col = "plate_id"
)

Arguments

Value

A data frame with one row per plate pair: plate_x, plate_y, n, pearson_r and r_squared. The wide compound-by-plate matrix is attached as attr(x, "wide") for use by plot_replicate_scatter().

See Also

calc_replicate_cv(), plot_replicate_scatter()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
calc_replicate_correlation(norm)

Replicate coefficient of variation per compound

Description

Computes, for each compound, the coefficient of variation (CV, percent) across its technical replicates (repeated wells, whether on the same plate or across plates), a standard measure of assay reproducibility.

Usage

calc_replicate_cv(
  data,
  value = "viability",
  compound_col = "compound_id",
  group_cols = NULL
)

Arguments

Value

A data frame with one row per compound (and any group_cols): compound_id, the grouping columns, n, mean, sd and cv (percent).

See Also

calc_replicate_correlation(), filter_bad_replicate()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
cv <- calc_replicate_cv(norm)
head(cv[order(-cv$cv), ])

Robust Z'-factor per plate

Description

Median/MAD version of the Z'-factor. What it is for: it answers the same question as calc_z_prime() — how cleanly the positive and negative controls separate — but because it uses the median and the median absolute deviation instead of the mean and SD, a couple of splashed or crystallised control wells cannot single-handedly sink an otherwise good plate. Prefer it when control replicates occasionally contain outliers.

Usage

calc_robust_z_prime(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC",
  z_prime_min = 0.5
)

Arguments

Value

A data frame with one row per plate: plate_id, median_nc, median_pc, mad_nc, mad_pc, robust_z_prime and a logical pass.

See Also

calc_z_prime(), calc_plate_qc(), plot_plate_qc()

Examples

calc_robust_z_prime(read_hts_plate(hts_primary_raw))

Signal-to-background ratio per plate

Description

The ratio of the high-signal control mean to the low-signal control mean. What it is for: S/B is the quickest sanity check that the assay actually produced a usable dynamic range — that the untreated (high) and killed (low) controls are far enough apart on the raw scale. It ignores variability, so it complements but does not replace the Z'-factor; a common minimum is ⁠>= 2⁠.

Usage

calc_sb_ratio(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC",
  sb_min = 2
)

Arguments

Value

A data frame with one row per plate: plate_id, mean_nc, mean_pc, sb (high-mean / low-mean) and a logical pass.

See Also

calc_sn_ratio(), calc_plate_qc()

Examples

calc_sb_ratio(read_hts_plate(hts_primary_raw))

Signal-to-noise ratio per plate

Description

The assay window divided by the noise of the low-signal control. What it is for: S/N asks whether the separation between the two controls is large compared with the background scatter, i.e. whether a real change would rise above the noise floor. It is more informative than S/B because it accounts for variability; higher is better, with ⁠>= 3⁠ a reasonable floor.

Usage

calc_sn_ratio(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC",
  sn_min = 3
)

Arguments

Value

A data frame with one row per plate: plate_id, mean_nc, mean_pc, sn (window / low-control SD) and a logical pass.

See Also

calc_sb_ratio(), calc_plate_qc()

Examples

calc_sn_ratio(read_hts_plate(hts_primary_raw))

SSMD per plate (strictly standardised mean difference)

Description

The mean control difference divided by the pooled control spread. What it is for: SSMD is the effect-size measure of control separation, and it is the statistically principled companion to the Z'-factor for setting hit cut-offs. Its magnitude grades the assay — roughly ⁠|SSMD|⁠ ⁠>= 2⁠ is excellent, 1–2 good and ⁠< 1⁠ weak — independent of the number of control replicates.

Usage

calc_ssmd(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC",
  ssmd_min = 2
)

Arguments

Value

A data frame with one row per plate: plate_id, mean_nc, mean_pc, sd_nc, sd_pc, ssmd and a logical pass. The sign of ssmd follows mean_nc - mean_pc.

See Also

calc_z_prime(), calc_plate_qc()

Examples

calc_ssmd(read_hts_plate(hts_primary_raw))

Per-well standard and robust Z-scores

Description

Standardises every well's signal within its plate, adding both a classic Z-score (mean/SD based) and a robust Z-score (median/MAD based, which is the preferred choice in HTS because it is not distorted by the very hits the screen is looking for). Scores are computed against a per-plate reference population, by default the test-compound wells.

Usage

calc_well_zscore(
  hts_raw,
  signal_col = "signal",
  reference = c("compound", "negative", "all"),
  nc_label = "NC",
  sample_label = "compound"
)

Arguments

Value

The input object with two added columns: zscore (mean/SD based) and robust_zscore (median/MAD based, MAD scaled to the normal distribution).

See Also

calc_plate_qc(), plot_plate_qc()

Examples

raw <- read_hts_plate(hts_primary_raw)
scored <- calc_well_zscore(raw)
head(scored[, c("plate_id", "well", "zscore", "robust_zscore")])

Z-factor per plate (sample-aware)

Description

The screening-window quality factor computed from the test-compound population against the negative control. What it is for: unlike the Z'-factor (controls only), the Z-factor tells you whether the assay window is large enough relative to the spread of the actual library wells. Values above 0.5 indicate an excellent screen and 0–0.5 a marginal one; it is routinely low or negative in a single-point primary screen, because most library compounds are inactive and therefore sit at the negative-control level — that is expected, not a failure.

Usage

calc_z_factor(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  sample_label = "compound",
  z_factor_min = 0.5
)

Arguments

Value

A data frame with one row per plate: plate_id, mean_sample, mean_nc, sd_sample, sd_nc, z_factor and a logical pass.

See Also

calc_z_prime(), calc_plate_qc()

Examples

calc_z_factor(read_hts_plate(hts_primary_raw))

Compute per-plate Z' factors

Description

Calculates the Z' (Z-prime) factor for each plate from its negative- and positive-control populations and flags plates against an acceptance threshold.

Usage

calc_z_prime(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC",
  z_prime_min = 0.5
)

Arguments

Value

A data frame with one row per plate: plate_id, mean_nc, mean_pc, sd_nc, sd_pc, z_prime and a logical pass.

See Also

filter_valid_plates(), plot_plate_qc()

Examples

raw <- read_hts_plate(hts_primary_raw)
calc_z_prime(raw)

Plate-level quality-control metrics for high-throughput screens

Description

Computes the standard set of assay quality metrics for a single plate from per-control-type summary statistics: the Z-factor, Z-prime (Z'), the signal-to-background ratio, the signal-to-noise ratio and the strictly standardised mean difference (SSMD).

Usage

calculate_qc_metrics(df)

Arguments

Details

The metrics follow the usual HTS definitions, using the negative control and positive (kill) control populations:

Z\text{-}factor = 1 - \frac{3(sd_{neg} + sd_{sample})}{|m_{neg} - m_{sample}|}

Z' = 1 - \frac{3(sd_{neg} + sd_{pos})}{|m_{neg} - m_{pos}|}

with signal-to-background m_{neg}/m_{pos}, signal-to-noise (m_{pos} - m_{neg})/sd_{neg} and SSMD = (m_{pos} - m_{neg})/\sqrt{sd_{pos}^2 + sd_{neg}^2}.

Value

A one-row data frame with columns z_factor, z_prime, sb_value, sn_value and ssmd_value.

See Also

flag_qc_plates()

Examples

plate <- data.frame(
  experiment_type = c("negative_ctrl", "positive_cttl", "sample"),
  mean = c(1.00, 0.10, 0.60),
  sd = c(0.05, 0.03, 0.20)
)
calculate_qc_metrics(plate)

Validate control-well labelling

Description

Checks that a data frame contains the expected negative-, positive- and blank-control labels in its well-type column, so that normalisation and QC can be carried out. It is a light-weight guard to run right after import.

Usage

check_control_label(data, type_col = "well_type", required = c("NC", "PC"))

Arguments

Value

Invisibly, a list with ok (logical), present and missing (character vectors) and counts (a table of well-type frequencies). A warning is emitted when any required label is missing.

Examples

d <- data.frame(well_type = c("NC", "NC", "PC", "compound"))
check_control_label(d)

Standardise compound identifiers

Description

Cleans free-text compound identifiers into a consistent form: trims surrounding whitespace, collapses internal whitespace, and replaces illegal or separator characters with underscores so that IDs can be matched and used safely in file names.

Usage

clean_compound_id(x, to_upper = TRUE)

Arguments

Value

A character vector of cleaned identifiers.

Examples

clean_compound_id(c("  cpd 001 ", "Drug/A", "cpd-002!"))

Sanitise a string for use as a file name

Description

Removes newlines and characters that are illegal in file names on common operating systems, so that drug names or plate labels can be turned into safe output file names.

Usage

clean_filename(filename_str)

Arguments

Value

A character vector with newlines removed, / and : replaced by ⁠_⁠ and - respectively, and the characters ⁠* ? " < > |⁠ stripped.

Examples

clean_filename("Drug A/B: 10\u00b5M?")

Hierarchically cluster an AUC matrix

Description

Performs hierarchical clustering of the compounds (rows) and samples (columns) of an AUC matrix and cuts the compound tree into groups.

Usage

cluster_auc_matrix(mat, k = 3, distance = "euclidean", method = "complete")

Arguments

Value

A list with row_hclust, col_hclust (or NULL if too few columns), clusters (named integer vector of compound cluster ids) and k.

See Also

extract_cluster_hit(), plot_cluster_tree()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
cl <- cluster_auc_matrix(build_auc_matrix(drc$meta), k = 2)
table(cl$clusters)

Area under a four-parameter logistic dose-response curve

Description

Integrates a fitted four_pl() curve between two concentrations to obtain the area under the curve (AUC), a robust summary of drug potency used for between-condition comparisons.

Usage

compute_auc(min_concentration, max_concentration, top, ic50, hill, bottom)

Arguments

Value

A single numeric value: the integrated area under the curve. When integration fails (for example a degenerate fit) NA_real_ is returned with a warning.

See Also

four_pl(), fit_dose_response()

Examples

compute_auc(
  min_concentration = 0.01, max_concentration = 10,
  top = 1, ic50 = 1, hill = 1, bottom = 0
)

Differential (delta) AUC between two screening conditions

Description

Contrasts drug potency between two experimental conditions by reshaping a long per-drug/per-condition AUC table to one row per drug and computing the differential AUC together with its within-experiment z-score. This is the core scoring step for two-condition screens (for example irradiated versus non-irradiated, or treated versus control).

Usage

compute_delta_auc(
  data,
  drug_col = "drug_name",
  condition_col = "condition",
  auc_col = "auc",
  conditions = NULL
)

Arguments

Details

A negative delta_auc indicates the treatment condition sensitises cells to the drug relative to baseline (lower AUC = more killing). The z-score standardises delta_auc across all drugs within the experiment so that hits can be selected on a common scale (see select_hits_sigma()).

Value

A data frame with one row per drug containing the two per-condition AUC columns (named ⁠auc_<condition>⁠), delta_auc and zscore_delta_auc (the z-scored delta_auc across all drugs).

See Also

select_hits_cutoff(), select_hits_sigma()

Examples

auc_long <- data.frame(
  drug_name = rep(c("DrugA", "DrugB", "DrugC"), each = 2),
  condition = rep(c("Gy0", "Gy2"), times = 3),
  auc = c(5.0, 3.1, 4.2, 4.0, 6.0, 2.0)
)
compute_delta_auc(auc_long, conditions = c("Gy0", "Gy2"))

Convert concentrations to a log10 scale

Description

Transforms a concentration vector to log10, the scale on which dose-response curves are usually drawn and fitted. Non-positive values (for example the zero-dose controls) become NA.

Usage

convert_conc_log10(x, shift_min = FALSE)

Arguments

Value

A numeric vector of log10 concentrations.

Examples

convert_conc_log10(c(0.01, 0.1, 1, 10, 100))
convert_conc_log10(c(0.01, 0.1, 1, 10, 100), shift_min = TRUE)

Flag outlier wells by the IQR rule

Description

Detects anomalous wells (for example contaminated, bubble or crystallisation artefacts) using the interquartile-range rule within groups of comparable wells, and optionally removes them.

Usage

detect_outlier_wells(
  hts_raw,
  signal_col = "signal",
  group_cols = c("plate_id", "well_type"),
  k = 1.5,
  filter = FALSE
)

Arguments

Value

The input object with a logical outlier column added (or with outlier rows removed when filter = TRUE).

See Also

calc_z_prime(), filter_valid_plates()

Examples

raw <- read_hts_plate(hts_primary_raw)
flagged <- detect_outlier_wells(raw)
sum(flagged$outlier)

Export a hit summary table

Description

Writes a complete hit table (all QC and pharmacology columns) to a CSV file.

Usage

export_hit_table(hits, file, ...)

Arguments

Value

The file path, invisibly.

See Also

rank_hit_compound(), annotate_hit_info()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
export_hit_table(rank_hit_compound(params), tempfile(fileext = ".csv"))

Extract the most active compound cluster

Description

Returns the compounds belonging to the cluster with the most extreme mean activity, that is the lowest mean AUC (strongest potency) by default.

Usage

extract_cluster_hit(mat, cluster_result, direction = c("low", "high"))

Arguments

Value

A character vector of compound ids in the selected cluster.

See Also

cluster_auc_matrix()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
m <- build_auc_matrix(drc$meta)
extract_cluster_hit(m, cluster_auc_matrix(m, k = 2))

Extract dose-response parameters

Description

Pulls the fitted potency and efficacy parameters from every curve of an hts_drc object.

Usage

extract_drc_params(drc)

Arguments

Value

A data frame with one row per curve: keys plus ic50, emax (top), bottom, hill, rsq and pass. Curves that failed to fit yield NA parameters.

See Also

fit_4pl_curve(), calc_drc_auc()

Examples

drc <- fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line")
head(extract_drc_params(drc))

Drop compounds with poor replicate reproducibility

Description

Removes compounds whose replicate coefficient of variation exceeds a threshold. When a correlation table is supplied it also checks that the replicate plates are globally concordant, warning if any pair falls below the R^2 threshold.

Usage

filter_bad_replicate(cv, cv_max = 15, cor_result = NULL, r2_min = 0.8)

Arguments

Value

The subset of cv for compounds passing the CV threshold, with the removed compound ids stored in attr(x, "removed").

See Also

calc_replicate_cv(), calc_replicate_correlation()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
cv <- calc_replicate_cv(norm)
good <- filter_bad_replicate(cv, cv_max = 20)
attr(good, "removed")

Drop low-quality dose-response curves

Description

Removes curves that failed to fit or whose R^2 falls below the threshold, leaving only trustworthy dose-response models.

Usage

filter_low_quality_curve(drc, rsq_min = NULL)

Arguments

Value

The filtered hts_drc object; removed curve ids are stored in attr(drc, "removed").

See Also

fit_4pl_curve()

Examples

drc <- fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line")
clean <- filter_low_quality_curve(drc)
nrow(clean$meta)

Keep only plates that pass the Z' threshold

Description

Removes whole plates whose Z' factor falls below the acceptance threshold, returning a clean data set for downstream normalisation.

Usage

filter_valid_plates(
  hts_raw,
  z_prime_min = 0.5,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC"
)

Arguments

Value

The hts_raw object restricted to passing plates, with the Z' summary table stored in attr(x, "zprime") and the dropped plate ids in attr(x, "dropped_plates").

See Also

calc_z_prime()

Examples

raw <- read_hts_plate(hts_primary_raw)
clean <- filter_valid_plates(raw)
attr(clean, "dropped_plates")

Fit four-parameter logistic curves per compound

Description

Fits a 4PL dose-response model to every compound (optionally split by grouping columns such as cell line), reusing the package's self-contained fitter, and records the goodness of fit R^2. No external fitting package is required.

Usage

fit_4pl_curve(
  data,
  response = "response",
  concentration = "concentration",
  compound_col = "compound_id",
  group_cols = NULL,
  rsq_min = 0.85
)

Arguments

Value

An hts_drc object: a list with meta (one row per curve: keys, n, rsq, converged, pass), fits (named list of dsr_4pl models) and data (the input). group_cols is stored as an attribute.

See Also

extract_drc_params(), calc_drc_auc(), plot_single_drc()

Examples

dr <- import_dose_response(hts_dose_response, response_col = "viability",
  group_cols = "cell_line")
drc <- fit_4pl_curve(dr, group_cols = "cell_line")
head(drc$meta)

Fit a four-parameter logistic dose-response model

Description

Fits a 4PL curve for a single drug under one condition by non-linear least squares, entirely in base R (no external fitting package required). Robust starting values are derived from the data and refined with a bounded L-BFGS-B optimisation over several initial IC50 guesses, falling back to a derivative-free Nelder-Mead search if needed. The model uses the same parameterisation as four_pl(), which matches the dr4pl package convention (UpperLimit, IC50, Slope, LowerLimit).

Usage

fit_dose_response(data, response, concentration, bounds = NULL)

## S3 method for class 'dsr_4pl'
coef(object, ...)

## S3 method for class 'dsr_4pl'
predict(object, newdata = NULL, ...)

## S3 method for class 'dsr_4pl'
fitted(object, ...)

## S3 method for class 'dsr_4pl'
residuals(object, ...)

## S3 method for class 'dsr_4pl'
print(x, ...)

Arguments

Details

The fitted parameters can be passed directly to compute_auc() to obtain a potency summary, for example do.call(compute_auc, c(list(min(x), max(x)), as.list(coef(fit)))).

Value

An object of class dsr_4pl: a list with elements coefficients (named numeric vector top, ic50, hill, bottom), fitted, residuals, convergence (logical), sse, data and call. The companion methods coef(), predict(), fitted(), residuals() and print() are provided for this class.

Functions

• coef(dsr_4pl): Extract the fitted 4PL coefficients (top, ic50, hill, bottom).

• predict(dsr_4pl): Predict responses from a fitted model.

• fitted(dsr_4pl): Extract fitted values.

• residuals(dsr_4pl): Extract residuals.

• print(dsr_4pl): Print a short summary of the fitted model.

See Also

four_pl(), compute_auc()

Examples

one <- subset(
  screen_doseresponse,
  drug_name == "DrugA" & condition == "Gy0" & experiment_type == "sample"
)
fit <- fit_dose_response(one, "normalized_cell_count", "concentration")
coef(fit)
compute_auc(
  min(one$concentration), max(one$concentration),
  top = coef(fit)[["top"]], ic50 = coef(fit)[["ic50"]],
  hill = coef(fit)[["hill"]], bottom = coef(fit)[["bottom"]]
)

Flag plates that fail quality-control thresholds

Description

Adds pass/fail flags to a table of plate quality metrics such as the one produced by calculate_qc_metrics(), using conventional acceptance thresholds.

Usage

flag_qc_plates(qc, z_prime_min = 0.4, sb_min = 3, cv_max = 10)

Arguments

Value

The input data frame with added character flag columns z_primer_flag, sb_flag and (when applicable) cv_flag, each taking the value "Bad" or "Normal".

See Also

calculate_qc_metrics()

Examples

qc <- data.frame(
  plateID = c("P01", "P02"),
  z_prime = c(0.62, 0.20),
  sb_value = c(5.1, 2.4),
  cv_negative_ctrl = c(6, 14)
)
flag_qc_plates(qc)

Four-parameter logistic (4PL) dose-response function

Description

Evaluates the classic four-parameter logistic model used to describe drug dose-response curves in high-throughput screens.

Usage

four_pl(x, top, ic50, hill, bottom)

Arguments

Details

The model is

f(x) = \mathrm{bottom} + \frac{\mathrm{top} - \mathrm{bottom}}{1 + (x / \mathrm{ic50})^{\mathrm{hill}}}

Value

A numeric vector of predicted responses, the same length as x.

See Also

compute_auc(), fit_dose_response()

Examples

four_pl(x = c(0.01, 0.1, 1, 10), top = 1, ic50 = 1, hill = 1, bottom = 0)

Generate an HTML screening report

Description

Renders a standard HTML analysis report from the bundled R Markdown template, embedding the supplied QC, dose-response and heatmap results. Requires the suggested rmarkdown package.

Usage

generate_hts_report(
  params = list(),
  output_file = tempfile(fileext = ".html"),
  template = NULL,
  quiet = TRUE
)

Arguments

Value

The path to the rendered report, invisibly.

See Also

export_hit_table()

Examples

## Not run: 
drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
generate_hts_report(list(hits = rank_hit_compound(params)))

## End(Not run)

Example compound metadata

Description

A small, simulated compound annotation table used to demonstrate hit annotation. Keyed by compound_id to join onto screening results.

Usage

hts_compound_meta

Format

A data frame with one row per compound and the following columns:

compound_id

Compound identifier such as "CPD001" (character).

compound_name

Human-readable compound name (character).

target

Nominal molecular target (character).

moa

Mechanism of action (character).

library_source

Source library the compound came from (character).

Details

The data are simulated (see data-raw/make_datasets.R) and do not correspond to any real compound.

Example gradient dose-response secondary screen

Description

A small, simulated dose-response confirmation screen: ten compounds tested across a concentration gradient in three cell lines with two replicates each. Used to demonstrate the dose-response fitting, AUC and clustering modules.

Usage

hts_dose_response

Format

A data frame with one row per measurement and the following columns:

compound_id

Compound identifier such as "CPD001" (character).

cell_line

Cell line, "A549", "H1299" or "MRC5" (character).

concentration

Compound concentration (micromolar) (numeric).

replicate

Technical replicate index, 1 or 2 (integer).

viability

Percent viability relative to vehicle control (numeric).

Details

The data are simulated (see data-raw/make_datasets.R); the cell line "MRC5" is made comparatively resistant.

Colourblind-safe qualitative palette for diffHTS

Description

Returns the package's low-saturation, colourblind-safe qualitative palette (an Okabe-Ito ordering). Use it whenever a plot maps a discrete variable to colour so that figures stay consistent and accessible.

Usage

hts_pal(n = NULL)

Arguments

Value

A character vector of hex colours.

See Also

theme_hts()

Examples

hts_pal(3)

Example single 384-well screening plate (realistic layout)

Description

A single simulated 384-well plate ("EXP87P01") whose layout mirrors a real cell-based high-throughput screen (see ⁠EXP87/⁠). The two outermost rings of wells (rows A, B, O, P and columns 1, 2, 23, 24) are kept free of compound to avoid edge-evaporation artefacts. They still hold medium but no live cells, so the instrument reads them at the same low, "dead" level as the kill control; they are stored as "edgewell" wells (not omitted). The controls sit inset from the true plate edge in the two innermost flanking columns (3 and 22), arranged in a rotationally balanced diagonal pattern: column 3 holds the positive (kill) control in its top half (rows C-H) and the negative (DMSO) control in its bottom half (rows I-N), while column 22 mirrors it (negative top, positive bottom). This diagonal split averages out spatial/edge signal gradients. Columns 4-21 across the interior rows C-N hold the test compounds. Used to demonstrate plate-layout heatmaps with control circling on a 384-well plate.

Usage

hts_plate_384

Format

A data frame with one row per well (all 384) and the following columns:

plate_id

Plate identifier, "EXP87P01" (character).

well

Well address such as "C03" (character).

row

Plate row letter, "A"-"P" (character).

col

Plate column number, 1-24 (integer).

well_type

Well role: "PC" (positive control), "NC" (negative control), "compound" or "edgewell" (empty buffer) (character).

compound_id

Compound identifier for test wells, NA for controls and edge wells (character).

concentration

Compound concentration (micromolar); NA for controls and edge wells (numeric).

signal

Raw luminescence read-out (numeric).

Details

The data are simulated (see data-raw/make_datasets.R) and do not correspond to any real compound. All 384 wells are stored; the empty edge wells carry only a low background/no-cell reading.

Example single-concentration primary screen (raw signals)

Description

A small, simulated primary high-throughput screen in 96-well plate format with raw readout signals, used to demonstrate the pre-QC, normalisation, replicate and primary-hit modules. The layout follows good HTS practice: the outer ring of wells (row A, row H and columns 1 and 12) is a compound-free evaporation buffer stored as "Blank" wells, the two inset columns 2 and 11 carry the controls in a rotationally balanced diagonal pattern (positive controls top-left/bottom-right, negative controls top-right/bottom-left), and the 48 compounds fill the interior block (rows B-G, columns 3-10). Plates "P01" and "P02" are good replicates (Z-prime around 0.8) while "P03" is a failed plate.

Usage

hts_primary_raw

Format

A data frame with one row per well and the following columns:

plate_id

Plate identifier, "P01", "P02" or "P03" (character).

well

Well identifier such as "A1" (character).

row

Plate row letter, "A"-"H" (character).

col

Plate column number, 1-12 (integer).

well_type

Well role: "NC" (negative), "PC" (positive), "Blank" or "compound" (character).

compound_id

Compound identifier such as "CPD001", NA for control and blank wells (character).

concentration

Compound concentration (micromolar), NA for controls (numeric).

signal

Raw instrument signal (numeric).

Details

The data are simulated (see data-raw/make_datasets.R) and do not correspond to any real compound or cell line.

Import gradient dose-response data

Description

Standardises a secondary (dose-response) screening table into the columns expected by the fitting engine, matching each compound to its concentration gradient and response, ready for fit_4pl_curve().

Usage

import_dose_response(
  data,
  compound_col = "compound_id",
  concentration_col = "concentration",
  response_col = "viability",
  group_cols = NULL
)

Arguments

Value

An hts_dose object (data frame) with columns compound_id, concentration, response, any group_cols, and canonical class.

See Also

fit_4pl_curve()

Examples

dr <- import_dose_response(hts_dose_response, response_col = "viability",
  group_cols = "cell_line")
head(dr)

Combine normalised plates into one data set

Description

Row-binds several normalised plates (or a list of them) into a single hts_norm object holding the activity of the whole screen, keeping only the columns shared by all inputs.

Usage

merge_plate_data(...)

Arguments

Value

A combined hts_norm object.

See Also

norm_by_control()

Examples

n1 <- norm_by_control(read_hts_plate(
  hts_primary_raw[hts_primary_raw$plate_id == "P01", ]))
n2 <- norm_by_control(read_hts_plate(
  hts_primary_raw[hts_primary_raw$plate_id == "P02", ]))
merged <- merge_plate_data(n1, n2)
table(merged$plate_id)

Normalise wells to plate controls

Description

Converts raw or background-subtracted signals to percent inhibition and percent viability using each plate's own negative (NC) and positive (PC) controls, so that readouts are comparable across plates. Inhibition is 100 \times (m_{NC} - x) / (m_{NC} - m_{PC}) and viability is its complement relative to the control window.

Usage

norm_by_control(
  hts_raw,
  signal_col = "signal",
  nc_label = "NC",
  pc_label = "PC"
)

Arguments

Value

An hts_norm object: the input data with added numeric columns inhibition (percent) and viability (percent). Multiple plates are normalised independently.

See Also

merge_plate_data(), plot_plate_heatmap_inhibition()

Examples

raw <- baseline_subtract(read_hts_plate(hts_primary_raw))
norm <- norm_by_control(raw)
summary(norm$inhibition)

Standard microplate coordinate map

Description

Builds the full row/column coordinate table for a standard microplate, ordered by row then column. This is useful for joining plate readouts onto a complete layout so that empty wells are represented explicitly.

Usage

plate_layout(format = 96)

plate_layout_384()

plate_layout_1536()

Arguments

Value

A data frame with one row per well and the columns well (for example "A1"), row (row letter), col (column number) and row_index (numeric row position, A = 1).

See Also

plate_layout_384(), plate_layout_1536()

Examples

head(plate_layout(96))
nrow(plate_layout(384))

AUC clustering heatmap

Description

Draws a clustered heatmap of the AUC matrix. Uses ComplexHeatmap (with circlize for the colour scale) when available for a publication-quality figure with a diverging navy-white-firebrick palette matching plot_delta_auc_heatmap(), otherwise falls back to base stats::heatmap().

Usage

plot_auc_heatmap(
  mat,
  title = "AUC heatmap",
  cluster_rows = TRUE,
  cluster_columns = TRUE
)

Arguments

Value

Invisibly, the drawn object. Called for its side effect (a plot on the active graphics device).

See Also

build_auc_matrix(), plot_cluster_tree()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
plot_auc_heatmap(build_auc_matrix(drc$meta, zscore = "row"))

Overlay multiple dose-response curves

Description

Overlays the fitted curves of several compounds for side-by-side comparison of activity.

Usage

plot_batch_drc_overlay(drc, curve_ids = NULL)

Arguments

Value

A ggplot2::ggplot object.

See Also

plot_single_drc()

Examples

drc <- fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line")
plot_batch_drc_overlay(drc, drc$meta$curve_id[1:4])

Plot the compound clustering dendrogram

Description

Draws the hierarchical clustering tree of compounds from an AUC matrix.

Usage

plot_cluster_tree(cluster_result, main = "Compound clustering")

Arguments

Value

Invisibly NULL; called for its side effect (a base-graphics plot).

See Also

cluster_auc_matrix()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
m <- build_auc_matrix(drc$meta)
plot_cluster_tree(cluster_auc_matrix(m, k = 2))

Scatter plot comparing two conditions or cell lines

Description

Plots a per-drug scatter of a score (for example delta AUC) under two conditions or cell lines, colouring points by quadrant and labelling the most extreme drugs in each quadrant. This is useful for spotting drugs that behave differently between, say, a cancer and a normal cell line.

Usage

plot_condition_scatter(data, x, y, drug_col = "drug_name", label_n = 5)

Arguments

Details

Point labelling uses ggrepel when available and falls back to ggplot2::geom_text() otherwise. The axes are centred on the origin with a fixed aspect ratio so the four quadrants are equal in size.

Value

A ggplot2::ggplot object.

See Also

plot_delta_auc_heatmap()

Examples

df <- data.frame(
  drug_name = paste0("Drug", 1:8),
  MRC5 = c(-1, 0.5, -0.2, 1.2, -0.8, 0.3, -1.5, 0.9),
  A549 = c(-1.2, 0.1, 0.7, -0.9, -1.1, 1.0, -0.4, 0.6)
)
plot_condition_scatter(df, x = "MRC5", y = "A549")

Scatter plot of control readouts across plates

Description

Draws, for each plate, the individual negative- and positive-control well readouts (for example normalised cell counts) so that plate-to-plate control behaviour and separation can be inspected at a glance. This is the standard QC view used to confirm that negative controls stay high and positive controls stay low across an entire screen.

Usage

plot_control_scatter(
  data,
  plate_col = "plateID",
  neg_cols = grep("^neg_ctrl", names(data), value = TRUE),
  pos_cols = grep("^pos_ctrl", names(data), value = TRUE),
  colors = c(`Negative control` = "#0072B2", `Positive control` = "#D55E00"),
  y_title = "Normalized cell count"
)

Arguments

Value

A ggplot2::ggplot object: control readouts (points, dodged and jittered) per plate, coloured by control type, with the y-axis expanded to include zero.

See Also

plot_qc_boxplot(), plot_qc_circular_heatmap()

Examples

plot_control_scatter(screen_plate_qc)

Histogram of replicate CV values

Description

Shows the distribution of per-compound replicate CVs across the library, with the acceptance threshold marked.

Usage

plot_cv_distribution(cv, cv_max = 15, bins = 30)

Arguments

Value

A ggplot2::ggplot object.

See Also

calc_replicate_cv(), filter_bad_replicate()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
plot_cv_distribution(calc_replicate_cv(norm))

Heatmap of differential AUC across conditions or cell lines

Description

Draws a diverging heatmap of differential (delta) AUC values with drugs on one axis and conditions/cell lines on the other, using ComplexHeatmap. Colours are centred at zero so that sensitising (negative) and antagonising (positive) responses are visually separated.

Usage

plot_delta_auc_heatmap(
  data,
  drug_col = "drug_name",
  value_cols = NULL,
  cell_types = NULL,
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  name = "delta AUC"
)

Arguments

Details

Requires the suggested packages ComplexHeatmap and circlize.

Value

A ComplexHeatmap::Heatmap object, which is drawn when printed. Rows with missing values are dropped before plotting.

See Also

plot_condition_scatter()

Examples

if (requireNamespace("ComplexHeatmap", quietly = TRUE) &&
    requireNamespace("circlize", quietly = TRUE)) {
  ht <- plot_delta_auc_heatmap(
    screen_delta_auc,
    value_cols = c("A549", "H1299", "H1975", "MRC5")
  )
}

Plot dose-response curves for two conditions

Description

Fits a four-parameter logistic model to each condition of a single drug and overlays the observed responses with the smooth fitted curves, coloured by condition. This is the key visual for comparing drug sensitivity between two conditions (for example irradiated versus non-irradiated cells), making a potency shift between conditions immediately apparent.

Usage

plot_dose_response_curves(
  data,
  drug = NULL,
  response = "normalized_cell_count",
  concentration = "concentration",
  condition = "condition",
  drug_col = "drug_name",
  n_points = 200,
  colors = c("#0072B2", "#D55E00", "#009E73", "#CC79A7"),
  shapes = c(19, 15, 17, 18),
  ref_line = 0.5,
  legend_title = "Condition"
)

Arguments

Details

The styling reproduces the plots used throughout the screening pipeline: a logarithmic concentration axis, distinct colour and point shape per condition, a horizontal 50 % reference line, and the fitted IC50/AUC of each condition reported in the subtitle.

Only concentrations that are finite and strictly positive are used (a log scale cannot show zero or negative doses). A condition with fewer than four usable observations is plotted as points only, with a warning, and no curve is fitted for it. Fitting is performed with fit_dose_response(), so no external fitting package is required. The figure uses the package's modern-academic theme_hts() look with a colourblind-safe condition palette.

Value

A ggplot2::ggplot object with a base-10 logarithmic concentration axis, observed points and fitted 4PL curves coloured by condition.

See Also

fit_dose_response(), four_pl(), compute_auc()

Examples

one <- subset(
  screen_doseresponse,
  drug_name == "DrugA" & experiment_type == "sample"
)
plot_dose_response_curves(one, drug = "DrugA")

Hit-count bar chart

Description

Bar chart of the number of hit versus non-hit compounds from a primary screen.

Usage

plot_hit_bar_count(hits)

Arguments

Value

A ggplot2::ggplot object.

See Also

summarize_primary_hit()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
plot_hit_bar_count(select_primary_hit(norm))

Stratified hit-count bar chart

Description

Bar chart of the number of Strong, Moderate and Weak hits.

Usage

plot_hit_stratify_bar(ranked)

Arguments

Value

A ggplot2::ggplot object.

See Also

rank_hit_compound()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
plot_hit_stratify_bar(rank_hit_compound(params))

IC50 versus AUC scatter

Description

Scatter plot of IC50 against AUC across curves, a compact view for triaging strong, moderate and weak compounds. IC50 marks the potency (the concentration for half-maximal effect) while AUC integrates potency and efficacy over the whole tested range; plotting them together shows whether the two agree and flags compounds that are potent but only weakly efficacious (or vice versa). A regression trend line and the Pearson correlation (computed on the log10 IC50, matching the log x-axis) are drawn on the figure by default.

Usage

plot_ic50_auc_cor(
  params,
  auc_col = "auc",
  ic50_col = "ic50",
  add_trend = TRUE,
  annotate_cor = TRUE
)

Arguments

Value

A ggplot2::ggplot object.

See Also

extract_drc_params(), calc_drc_auc()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
plot_ic50_auc_cor(params)

Histogram of compound inhibition on a plate

Description

Shows the distribution of percent inhibition across the compound wells of a plate (controls excluded), the standard view for judging overall hit density.

Usage

plot_inhibition_hist(
  hts_norm,
  plate = NULL,
  value = "inhibition",
  compound_label = "compound",
  bins = 30
)

Arguments

Value

A ggplot2::ggplot object.

See Also

norm_by_control()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
plot_inhibition_hist(norm, plate = "P01")

Plate-layout heatmap of well readouts

Description

Draws a microplate as a grid of wells (rows lettered top-to-bottom, columns numbered left-to-right) coloured by a per-well readout such as normalised cell count, so that drug-treated wells and their positive/negative controls can be inspected in their physical plate positions. Missing wells are shown as empty cells, making layout errors and edge effects easy to spot. This abstracts the per-plate heatmap.2() plate maps used in the screening pipeline, but is built on ggplot2 and needs no external heatmap package.

Usage

plot_plate_heatmap(
  data,
  fill = "normalized_cell_count",
  well_col = NULL,
  row_col = "plate_rows",
  col_col = "plate_columns",
  label = NULL,
  control_col = NULL,
  nc_values = c("NC", "negative_ctrl"),
  pc_values = c("PC", "positive_ctrl"),
  circle_size = NULL,
  plate = NULL,
  plate_col = "plateID",
  plate_type = NULL,
  title = NULL,
  na_value = "grey90"
)

Arguments

Details

Duplicate wells (for example replicates sharing a position) are summarised by their mean for numeric fill and concatenated for categorical fill/label.

Value

A ggplot2::ggplot object: one tile per well, coloured by fill, with row A at the top and column 1 at the top-left, drawn with a fixed aspect ratio. When control_col is supplied the negative- and positive-control wells are outlined with coloured circles.

See Also

plot_qc_boxplot(), plot_control_scatter()

Examples

# A single 96-well plate coloured by normalised cell count.
plot_plate_heatmap(subset(screen_plate_layout, plateID == "EXP99_Gy0"))

# Show the well-type layout instead, both plates side by side.
plot_plate_heatmap(screen_plate_layout, fill = "experiment_type")

# Circle the negative and positive controls on a raw 96-well plate.
plot_plate_heatmap(
  subset(hts_primary_raw, plate_id == "P01"),
  fill = "signal", well_col = "well", plate_col = "plate_id",
  control_col = "well_type"
)

# A realistic 384-well plate (the outer 2-well ring is a compound-free buffer
# that reads at the low, no-cell level; controls sit on a diagonal in the two
# inset columns 3 and 22).
plot_plate_heatmap(hts_plate_384, fill = "signal", well_col = "well",
  control_col = "well_type", plate_col = "plate_id", plate_type = 384)

Percent-inhibition plate heatmap

Description

Draws a plate's normalised percent-inhibition values in plate layout. A thin wrapper around plot_plate_heatmap() for hts_norm objects.

Usage

plot_plate_heatmap_inhibition(hts_norm, plate = NULL, value = "inhibition")

Arguments

Value

A ggplot2::ggplot object.

See Also

plot_plate_heatmap(), plot_inhibition_hist()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
plot_plate_heatmap_inhibition(norm, plate = "P01")

Raw-signal plate heatmap

Description

Draws a single plate's raw (or background-subtracted) readouts in plate layout, optionally marking flagged outlier wells. A thin wrapper around plot_plate_heatmap() for hts_raw objects.

Usage

plot_plate_heatmap_raw(
  hts_raw,
  plate = NULL,
  signal_col = "signal",
  mark_outliers = TRUE
)

Arguments

Value

A ggplot2::ggplot object.

See Also

plot_plate_heatmap(), plot_plate_qc()

Examples

raw <- detect_outlier_wells(read_hts_plate(hts_primary_raw))
plot_plate_heatmap_raw(raw, plate = "P01")

Visualise any plate quality-control metric

Description

A single entry point for plotting the plate-quality metrics produced by calc_plate_qc() (and the per-well scores from calc_well_zscore()). Choose which assessment to draw through the metric argument; the acceptance threshold, axis label and pass/fail colouring are set automatically for each metric but can be overridden.

Usage

plot_plate_qc(qc, metric = "z_prime", threshold = NULL, plate_col = "plate_id")

Arguments

Value

A ggplot2::ggplot object.

See Also

calc_plate_qc(), calc_well_zscore(), plot_plate_heatmap_raw()

Examples

raw <- read_hts_plate(hts_primary_raw)
qc <- calc_plate_qc(raw)
plot_plate_qc(qc, metric = "z_prime")
plot_plate_qc(qc, metric = "ssmd")
plot_plate_qc(qc, metric = "cv")
plot_plate_qc(calc_well_zscore(raw), metric = "robust_zscore")

Ranked primary-activity curve

Description

Draws the whole library ranked by activity, with the hit cut-off marked, the standard view of how many compounds clear the primary threshold.

Usage

plot_primary_inhibition_rank(hits)

Arguments

Value

A ggplot2::ggplot object.

See Also

select_primary_hit()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
plot_primary_inhibition_rank(select_primary_hit(norm))

Primary versus secondary activity correlation

Description

Scatter of primary-screen activity against secondary-screen AUC for the same compounds, the standard check that primary hits reproduce in the dose-response confirmation screen.

Usage

plot_primary_secondary_cor(
  primary,
  secondary,
  by = "compound_id",
  primary_col = "activity",
  secondary_col = "auc"
)

Arguments

Value

A ggplot2::ggplot object.

See Also

select_primary_hit(), calc_drc_auc()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
primary <- select_primary_hit(norm)
drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
sec <- aggregate(auc ~ compound_id, drc$meta, mean)
plot_primary_secondary_cor(primary, sec)

Quality-control boxplot of control wells across plates

Description

Draws grouped boxplots of a per-well readout across plates, split by control type, to inspect assay consistency and separation between negative and positive controls.

Usage

plot_qc_boxplot(
  data,
  plate_col = "plateID",
  value_col = "normalized_cell_count",
  group_col = "experiment_type",
  y_title = "Normalized cell count"
)

Arguments

Value

A ggplot2::ggplot object.

See Also

calculate_qc_metrics()

Examples

plot_qc_boxplot(screen_doseresponse)

Circular heatmap of control readouts across plates

Description

Draws a large circular (circlize) heatmap of the control-well readouts for every plate, grouped into sectors by experiment. Each ring is one plate and each cell one control well, so systematic control problems across a whole screen become visible at once. This mirrors the summary circos.heatmap used in the pipeline to display negative- and positive-control cell counts.

Usage

plot_qc_circular_heatmap(
  data,
  value_cols = grep("_ctrl_", names(data), value = TRUE),
  plate_col = "plateID",
  split_col = "experimentID",
  col_breaks = c(0, 0.5, 1),
  col_colors = c("navy", "white", "firebrick3"),
  name = "Norm cell counts",
  show_sector_labels = TRUE
)

Arguments

Details

Requires the suggested package circlize; when ComplexHeatmap is also installed a colour legend is added.

Value

Invisibly NULL. Called for the side effect of drawing to the active graphics device.

See Also

plot_control_scatter(), plot_delta_auc_heatmap()

Examples

if (requireNamespace("circlize", quietly = TRUE)) {
  plot_qc_circular_heatmap(screen_plate_qc)
}

Circular plot of a per-plate QC metric

Description

Draws a circular (circlize) scatter of a QC metric with one sector per experiment and one point per plate, coloured by whether the plate passes a threshold. This reproduces the whole-screen Z'-factor overview used in the pipeline, where many plates across several experiments are compared on a single ring.

Usage

plot_qc_metric_circular(
  data,
  metric = "z_prime",
  sector_col = "experimentID",
  x_col = "plateNumber",
  label_col = "plateID",
  threshold = 0.5,
  transform = TRUE,
  colors = c(Bad = "#D55E00", Normal = "#0072B2"),
  bar_lwd = 4,
  x_margin = 0.8,
  show_labels = TRUE,
  center_label = NULL
)

Arguments

Details

Requires the suggested package circlize. Each experiment is a sector and each plate a thin vertical bar drawn at its own plateNumber position, rising from a zero baseline to the metric value, so the bars never overlap. An angular x_margin keeps the first and last bars clear of the sector edges, the plate identifier is printed radially just outside each bar, and a short Y-axis on the first sector plus a dashed reference line at threshold in every sector give the scale. The design scales to large campaigns of roughly ten plates per experiment.

Value

Invisibly NULL. Called for the side effect of drawing to the active graphics device.

See Also

plot_qc_metric_trend(), plot_qc_circular_heatmap()

Examples

if (requireNamespace("circlize", quietly = TRUE)) {
  plot_qc_metric_circular(screen_plate_qc, metric = "z_prime")
}

Trend plot of a QC metric across plates

Description

Plots a single QC metric (for example Z', Z-factor or negative-control CV) for every plate in acquisition order, colouring each plate by a pass/fail flag and marking the overall mean. This mirrors the per-metric QC review plots used in the screening pipeline and makes drifting or failing plates easy to spot.

Usage

plot_qc_metric_trend(
  data,
  metric = "z_prime",
  plate_col = "plateID",
  flag_col = NULL,
  threshold = 0.5,
  direction = c("min", "max"),
  colors = c(Bad = "#D55E00", Normal = "#0072B2")
)

Arguments

Value

A ggplot2::ggplot object: one bar per plate in plate order, coloured by flag, with a dashed line at the mean and a solid line at threshold.

See Also

calculate_qc_metrics(), flag_qc_plates()

Examples

plot_qc_metric_trend(screen_plate_qc, metric = "z_prime")

Replicate scatter plot(s) with regression fit and correlation

Description

Plots the activity of replicate plates against one another in the house style used across the screening reports: points coloured by well type (compounds, negative/positive controls, blanks) at 50% opacity, a linear regression line with a shaded 95% confidence band, and a Pearson R (with p-value) annotated on each panel. When more than two replicate plates are present and no specific pair is requested, every pairwise combination (replicate 1 vs 2, 1 vs 3, 2 vs 3, ...) is drawn together in a single faceted figure, mirroring the combn()-based reps_corr_plot() workflow in the source analyses.

Usage

plot_replicate_scatter(
  data,
  plate_x = NULL,
  plate_y = NULL,
  value = "viability",
  plate_col = "plate_id",
  key_col = NULL,
  compound_col = "compound_id",
  color_col = NULL,
  palette = .well_type_palette,
  line_color = "lightblue",
  band_color = "lightgray",
  show_identity = TRUE
)

Arguments

Value

A single ggplot2::ggplot object. With three or more plates it is faceted, one panel per replicate pair. The annotated R and p-value are computed over every point shown in the panel.

See Also

calc_replicate_correlation()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
# All pairwise replicate comparisons at once, points coloured by well type:
plot_replicate_scatter(norm)
# A single chosen pair:
plot_replicate_scatter(norm, plate_x = "P01", plate_y = "P02")

Distribution plot of sigma-based hits

Description

Histogram of the per-compound activity with the library centre and the 1, 2 and 3 sigma cut-offs marked, bars coloured by how many sigma each compound clears. Optionally overlays the fitted normal curve so the departure from normality (the hit tail) stands out.

Usage

plot_sigma_hits(hits, bins = 30, show_normal = TRUE)

Arguments

Value

A ggplot2::ggplot object.

See Also

select_sigma_hits(), summarize_sigma_hits()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
plot_sigma_hits(select_sigma_hits(norm))

Plot a single dose-response curve

Description

Plots one compound's observed points and fitted 4PL curve on a log10 concentration axis, annotating IC50, AUC and R^2.

Usage

plot_single_drc(drc, curve_id = NULL, color = "#0072B2")

Arguments

Value

A ggplot2::ggplot object.

See Also

plot_batch_drc_overlay()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
plot_single_drc(drc, drc$meta$curve_id[1])

Rank and stratify hit compounds

Description

Combines potency, efficacy and quality metrics into a single activity score and assigns each compound to a Strong, Moderate or Weak tier, the final prioritisation step of the pipeline. Lower AUC and IC50 and higher Emax count as more active; lower replicate CV and higher fit R^2 raise confidence.

Usage

rank_hit_compound(
  data,
  auc_col = "auc",
  ic50_col = "ic50",
  emax_col = "emax",
  cv_col = "cv",
  rsq_col = "rsq",
  weights = NULL,
  probs = c(1/3, 2/3)
)

Arguments

Value

data with added score (numeric) and tier (ordered factor Weak < Moderate < Strong), sorted by descending score.

See Also

annotate_hit_info(), plot_hit_stratify_bar()

Examples

drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
ranked <- rank_hit_compound(params)
table(ranked$tier)

Import a plate readout file into a standard HTS object

Description

Reads an instrument export (csv, txt/tsv or Excel) or an in-memory data frame of well-level readouts and standardises it into an hts_raw object with a fixed set of columns, ready for the rest of the pipeline. Well coordinates are parsed from the well identifier and control wells are canonicalised to the labels "NC", "PC" and "Blank".

Usage

read_hts_plate(
  file,
  plate_id = NULL,
  well_col = "well",
  signal_col = "signal",
  compound_col = "compound_id",
  conc_col = "concentration",
  type_col = "well_type",
  plate_col = "plate_id",
  nc_label = "NC",
  pc_label = "PC",
  blank_label = "Blank",
  sep = "\t",
  sheet = 1
)

Arguments

Details

Reading Excel files requires the suggested package readxl.

Value

An hts_raw object (a data frame subclass) with columns plate_id, well, row, col, well_type, compound_id, concentration and signal.

See Also

baseline_subtract(), calc_z_prime(), norm_by_control()

Examples

raw <- read_hts_plate(hts_primary_raw)
table(raw$well_type)

Read a user-defined plate-map layout file

Description

Reads a plate map that you author yourself in Excel or CSV to declare, for every well, whether it holds a negative control, a positive control, a blank (empty/edge) well or a test compound. Nothing about the layout is fixed by the package: you are free to place the controls, blanks and compounds in whichever wells your assay uses, and apply_plate_layout() then stamps those roles onto the measured readings.

Usage

read_plate_layout(
  file,
  plate_id = NULL,
  nc_labels = c("NC", "DMSO", "vehicle", "negative_ctrl", "neg_ctrl"),
  pc_labels = c("PC", "kill_ctrl", "positive_ctrl", "pos_ctrl"),
  blank_labels = c("Blank", "edgewell", "empty", "blank", "na", ""),
  concentration_file = NULL,
  sheet = 1,
  sep = "\t"
)

Arguments

Details

The expected file is a grid that mirrors the physical plate: the first column holds the row letters (A, B, ...), the remaining column headers are the plate column numbers (1, 2, ... 24), and each cell contains the label for that well. Labels are matched case-insensitively against nc_labels, pc_labels and blank_labels; any other non-empty label is treated as a compound identifier. An optional second grid of the same shape supplies per-well concentrations.

Value

An hts_layout data frame with one row per well and the columns plate_id, well, row, col, well_type ("NC", "PC", "Blank" or "compound"), compound_id and concentration.

See Also

apply_plate_layout(), read_hts_plate(), check_control_label()

Examples

f <- system.file("extdata", "plate_layout_example.csv", package = "diffHTS")
layout <- read_plate_layout(f, plate_id = "P01")
table(layout$well_type)

Example differential-AUC hit table

Description

A small, simulated table of differential (delta) AUC values with one row per drug and one column per cell line, used to demonstrate hit selection and heatmap/scatter visualisation across several cancer cell lines and a normal (MRC5) cell line.

Usage

screen_delta_auc

Format

A data frame with one row per drug and the following columns:

drug_name

Drug identifier (character).

A549

Delta AUC in the A549 lung cancer cell line (numeric).

H1299

Delta AUC in the H1299 lung cancer cell line (numeric).

H1975

Delta AUC in the H1975 lung cancer cell line (numeric).

H2030

Delta AUC in the H2030 lung cancer cell line (numeric).

MRC5

Delta AUC in the MRC5 normal lung fibroblast cell line (numeric).

Details

The data are simulated (see data-raw/make_datasets.R) and do not correspond to any real compound. Negative values indicate treatment-induced sensitisation.

See Also

screen_doseresponse

Example two-condition dose-response screen

Description

A small, simulated high-throughput drug-screening dataset in long (tidy) format, mimicking a two-condition experiment in which each drug is tested across a concentration series under a baseline ("Gy0") and a treatment ("Gy2") condition, alongside negative and positive control wells. It is sized to run the package examples and vignette quickly.

Usage

screen_doseresponse

Format

A data frame with one row per well and the following columns:

plateID

Plate identifier (character).

drug_name

Drug identifier, or a control label such as "DMSO" (negative) or "kill_ctrl" (positive).

experiment_type

Well role: "sample", "negative_ctrl" or "positive_cttl".

condition

Experimental condition, "Gy0" (baseline) or "Gy2" (treatment).

concentration

Drug concentration (micromolar).

normalized_cell_count

Viability normalised to the negative control (roughly 0-1).

Details

The data are simulated (see data-raw/make_datasets.R) and do not correspond to any real compound or cell line.

See Also

screen_delta_auc

Example 96-well plate layout with well positions

Description

A small, simulated 96-well plate (8 rows by 12 columns) assayed under two conditions ("Gy0" and "Gy2"), in long (one row per well) format. The layout follows good HTS practice: the outer ring (row A, row H, columns 1 and 12) is an empty evaporation buffer, the two flanking columns (2 and 11) carry the positive (kill) and negative (DMSO) controls in a rotationally balanced diagonal pattern, and the interior block (rows B-G, columns 3-10) holds an 8-point dose series of one drug per row. It is used to demonstrate the plate-layout heatmap plot_plate_heatmap().

Usage

screen_plate_layout

Format

A data frame with one row per well and the following columns:

plateID

Plate identifier such as "EXP99_Gy0" (character).

condition

Experimental condition, "Gy0" or "Gy2".

well

Well identifier such as "A1" (character).

plate_rows

Plate row letter, "A"-"H" (character).

plate_columns

Plate column number, 1-12 (integer).

drug_name

Drug identifier, or a control label ("DMSO", "kill_ctrl"), or NA for empty buffer wells.

experiment_type

Well role: "sample", "negative_ctrl", "positive_ctrl" or "empty" (evaporation-buffer ring).

concentration

Drug concentration (micromolar), NA for controls and empty wells.

normalized_cell_count

Viability normalised to the negative control; empty buffer wells hold no live cells and read near the kill control.

Details

The data are simulated (see data-raw/make_datasets.R) and do not correspond to any real compound or cell line.

See Also

plot_plate_heatmap()

Example per-plate quality-control summary

Description

A small, simulated per-plate quality-control (QC) table spanning several experiments (cell lines), each with a few plates. Every plate carries its negative- and positive-control replicate readouts together with the QC metrics derived from them, so the table can drive the control scatter, the circular control heatmap and the circular Z'-factor plot. A couple of plates are deliberately noisy so that passing and failing plates are both present.

Usage

screen_plate_qc

Format

A data frame with one row per plate and the following columns:

plateID

Plate identifier such as "EXP76_01" (character).

experimentID

Experiment identifier (character), the plate prefix.

cell_line

Cell line assayed on the experiment (character).

plateNumber

Plate order within the experiment (integer).

z_factor

Assay Z-factor from sample and negative-control wells.

z_prime

Z-prime factor from negative- and positive-control wells.

sb_value

Signal-to-background ratio.

sn_value

Signal-to-noise value.

ssmd_value

Strictly standardised mean difference.

cv_negative_ctrl

Coefficient of variation of the negative controls (percent).

neg_ctrl_1, neg_ctrl_2, neg_ctrl_3, neg_ctrl_4, neg_ctrl_5, neg_ctrl_6

Negative-control well readouts.

pos_ctrl_1, pos_ctrl_2, pos_ctrl_3, pos_ctrl_4, pos_ctrl_5, pos_ctrl_6

Positive-control well readouts.

Details

The data are simulated (see data-raw/make_datasets.R) and do not correspond to any real compound or cell line.

See Also

plot_control_scatter(), plot_qc_metric_circular(), plot_qc_circular_heatmap()

Select hits by an absolute cut-off across conditions

Description

Nominates drugs whose score is below a fixed cut-off in a required number of the supplied score columns. This implements the cut-off based hit-selection strategy used to intersect differential responses across several cancer cell lines.

Usage

select_hits_cutoff(
  data,
  score_cols,
  cutoff = 0,
  min_pass = length(score_cols),
  drug_col = "drug_name"
)

Arguments

Value

The subset of data (rows) meeting the criterion, with an added integer column n_pass giving the number of score columns below cutoff. Rows containing missing scores in the tested columns are dropped.

See Also

select_hits_sigma()

Examples

df <- data.frame(
  drug_name = c("DrugA", "DrugB", "DrugC"),
  A549 = c(-0.5, 0.2, -0.9),
  H1299 = c(-0.3, -0.1, -0.8)
)
select_hits_cutoff(df, score_cols = c("A549", "H1299"), cutoff = 0,
                   min_pass = 2)

Select hits by a sigma (z-score) threshold

Description

Nominates drugs whose standardised score exceeds a given number of standard deviations from the mean, in the desired direction. This implements the sigma-based (for example 2-sigma or 3-sigma) hit-selection strategy applied to z-scored differential AUC values.

Usage

select_hits_sigma(
  data,
  score_col = "zscore_delta_auc",
  n_sigma = 3,
  direction = c("less", "greater"),
  standardize = FALSE,
  drug_col = "drug_name"
)

Arguments

Value

The subset of data whose score is beyond the sigma threshold in the requested direction, ordered by score.

See Also

select_hits_cutoff(), compute_delta_auc()

Examples

df <- data.frame(
  drug_name = paste0("Drug", 1:6),
  zscore_delta_auc = c(-3.4, -1.2, 0.1, 0.5, 1.1, -2.8)
)
select_hits_sigma(df, n_sigma = 2, direction = "less")

Select primary-screen hits

Description

Flags active compounds from single-concentration primary-screen data using either a fixed activity threshold or a percentile rank of the whole library.

Usage

select_primary_hit(
  data,
  value = "inhibition",
  method = c("threshold", "percentile"),
  threshold = 50,
  percentile = 0.95,
  compound_col = "compound_id"
)

Arguments

Value

A data frame with one row per compound: compound_id, activity, is_hit. The applied cut-off is stored in attr(x, "cutoff") and the method in attr(x, "method").

See Also

summarize_primary_hit(), plot_primary_inhibition_rank()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
hits <- select_primary_hit(norm, threshold = 50)
sum(hits$is_hit)

Select primary-screen hits by distance from the library distribution

Description

Treats the per-compound activity of the whole library as an approximately normal "inactive" distribution and flags compounds sitting far out in the tail. Each compound receives a z-score (how many standard deviations it lies from the library centre) and is binned by whether it clears 1, 2 or 3 sigma. By default the centre and spread are estimated robustly (median and MAD), the HTS-standard choice: a handful of genuine hits would otherwise inflate a plain mean/SD and mask themselves (with too wide an SD nothing reaches 3 sigma).

Usage

select_sigma_hits(
  data,
  value = "inhibition",
  method = c("robust", "sd"),
  n_sigma = 3,
  direction = c("greater", "less", "two.sided"),
  compound_col = "compound_id"
)

Arguments

Value

A data frame with one row per compound: compound_id, activity, sigma (signed z-score), sigma_level (0-3, how many sigma it clears in the tested direction), band (an ordered-factor label) and is_hit (sigma_level >= n_sigma). The distribution center, scale, method, n_sigma and direction are stored as attributes.

See Also

summarize_sigma_hits(), plot_sigma_hits(), select_primary_hit()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
hits <- select_sigma_hits(norm, n_sigma = 3)
sum(hits$is_hit)

Summarise how each plate in a run is set up

Description

Reports, for every plate in an hts_raw object, how the plate is laid out: its physical format, the number of wells of each role (negative/positive controls, blanks and compounds), how many distinct compounds it carries, and which columns the controls and compounds occupy. The positions are read back from your own layout (see read_plate_layout()) rather than assumed by the package: because a screening run normally spans several plates at once, this is the quickest way to confirm that every plate carries the layout you designed before QC and normalisation.

Usage

summarize_plate_setup(
  hts_raw,
  plate_col = "plate_id",
  type_col = "well_type",
  compound_col = "compound_id",
  col_col = "col",
  nc_label = "NC",
  pc_label = "PC",
  blank_label = "Blank",
  compound_label = "compound"
)

Arguments

Value

A data frame with one row per plate and the columns plate_id, n_wells, plate_format, n_rows, n_cols, n_nc, n_pc, n_blank, n_compound, n_unique_compound, nc_columns, pc_columns, blank_columns and compound_columns (control/compound positions given as compact column ranges such as "3-11").

See Also

read_hts_plate(), check_control_label(), calc_z_prime()

Examples

raw <- read_hts_plate(hts_primary_raw)
# One row per plate; the run holds three 96-well plates here.
summarize_plate_setup(raw)

Summarise primary-screen hits

Description

Reports the number of compounds screened, the number of hits and the hit rate.

Usage

summarize_primary_hit(hits)

Arguments

Value

A one-row data frame with n_compounds, n_hits, hit_rate (fraction) and cutoff.

See Also

select_primary_hit()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
summarize_primary_hit(select_primary_hit(norm))

Summarise sigma-based hits

Description

Counts how many compounds clear 1, 2 and 3 sigma from the library centre and compares each count to what a perfectly normal, hit-free distribution would predict, so enrichment of the tail over chance is obvious.

Usage

summarize_sigma_hits(hits)

Arguments

Value

A data frame with one row per sigma level (1, 2, 3): sigma, n_beyond (compounds clearing it), frac_beyond, expected_frac (normal-theory tail probability) and expected_n (expected count).

See Also

select_sigma_hits(), plot_sigma_hits()

Examples

norm <- norm_by_control(baseline_subtract(read_hts_plate(hts_primary_raw)))
summarize_sigma_hits(select_sigma_hits(norm))

Modern academic ggplot2 theme for diffHTS figures

Description

A minimal, publication-ready theme: pure-white background, an L-shaped axis (left and bottom only), light dashed horizontal major grid lines with no minor grid, restrained dark-grey typography and generous margins. Pair it with hts_pal() for discrete colours or a viridis/muted-blue gradient for continuous ones.

Usage

theme_hts(
  base_size = 12,
  base_family = "",
  legend = "bottom",
  grid = c("y", "x", "both", "none")
)

Arguments

Value

A ggplot2::theme object that can be added to any ggplot.

See Also

hts_pal(), plot_plate_qc()

Examples

library(ggplot2)
ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_boxplot() +
  theme_hts()

Convert a Windows path to a forward-slash path

Description

Replaces backslashes with forward slashes so that Windows-style paths can be used consistently across platforms.

Usage

windows_to_linux_path(path)

Arguments

Value

A character vector of the same length with backslashes replaced by forward slashes.

Examples

windows_to_linux_path("C:\\data\\EXP99\\raw.txt")