diffHTS provides reusable methods for large-scale, two-condition high-throughput drug screening (HTS). It compares drug sensitivity between any two experimental conditions — for example irradiated versus non-irradiated cells, cancer versus normal cell lines, or treated versus untreated samples — across many plates and experiments, in a documented and tested toolkit.

Installation

Install from a local source checkout:

# install.packages("remotes")
remotes::install_local("diffHTS")
# or, from the built source tarball
install.packages("diffHTS_0.1.0.tar.gz", repos = NULL, type = "source")

Dose-response fitting is implemented in base R, so no external fitting package is needed. Some visualisation and I/O features use packages that are only suggested: the Bioconductor packages ComplexHeatmap and circlize (heatmaps), ggrepel (labelled scatter plots), readxl (Excel import) and rmarkdown (HTML reports).

Standard pipeline

diffHTS organises a complete HTS analysis into seven modules that take raw plate readouts all the way to an annotated, ranked hit list. It supports both single-concentration primary screens and gradient secondary (dose-response) confirmation screens. Each module pairs business functions with matching visualisation functions.

Module	Business functions	Visualisation
1. Import & pre-QC	`read_plate_layout()`, `apply_plate_layout()`, `read_hts_plate()`, `summarize_plate_setup()`, `baseline_subtract()`, `detect_outlier_wells()`, `calc_z_prime()`, `calc_robust_z_prime()`, `calc_z_factor()`, `calc_ssmd()`, `calc_sb_ratio()`, `calc_sn_ratio()`, `calc_cv()`, `calc_plate_qc()`, `calc_well_zscore()`, `filter_valid_plates()`	`plot_plate_heatmap_raw()`, `plot_plate_qc()`
2. Normalisation	`norm_by_control()`, `merge_plate_data()`	`plot_plate_heatmap_inhibition()`, `plot_inhibition_hist()`
3. Replicate consistency	`calc_replicate_cv()`, `calc_replicate_correlation()`, `filter_bad_replicate()`	`plot_replicate_scatter()`, `plot_cv_distribution()`
4. Primary hit selection	`select_primary_hit()`, `select_sigma_hits()`, `summarize_primary_hit()`, `summarize_sigma_hits()`	`plot_primary_inhibition_rank()`, `plot_hit_bar_count()`, `plot_sigma_hits()`
5. Dose-response & AUC	`import_dose_response()`, `fit_4pl_curve()`, `extract_drc_params()`, `calc_drc_auc()`, `filter_low_quality_curve()`	`plot_single_drc()`, `plot_batch_drc_overlay()`, `plot_ic50_auc_cor()`
6. AUC matrix & clustering	`build_auc_matrix()`, `cluster_auc_matrix()`, `extract_cluster_hit()`	`plot_auc_heatmap()`, `plot_cluster_tree()`
7. Ranking, annotation & report	`rank_hit_compound()`, `annotate_hit_info()`, `export_hit_table()`, `generate_hts_report()`	`plot_hit_stratify_bar()`, `plot_primary_secondary_cor()`

Utilities: plate_layout() / plate_layout_384() / plate_layout_1536(), convert_conc_log10(), check_control_label(), clean_compound_id().

The original two-condition (differential) analysis remains available: four_pl(), compute_auc(), fit_dose_response(), compute_delta_auc(), select_hits_cutoff(), select_hits_sigma(), calculate_qc_metrics(), plot_dose_response_curves(), plot_delta_auc_heatmap(), plot_condition_scatter() and the QC plots.

Quick start

library(diffHTS)
## --- Primary screen: raw signals -> hits ---------------------------------
## Author a plate map (which wells are NC / PC / blank / compound) yourself,
## then stamp it onto the instrument readings:
layout <- read_plate_layout(
  system.file("extdata", "plate_layout_example.csv", package = "diffHTS"),
  plate_id = "P01")
# raw <- apply_plate_layout(signal_export, layout)  # merge roles onto readings
raw  <- read_hts_plate(hts_primary_raw) # three 96-well plates in one object
summarize_plate_setup(raw)             # per-plate layout: controls & compounds
raw  <- baseline_subtract(raw)
qc   <- calc_plate_qc(raw)             # full QC panel: Z'/robust-Z'/SSMD/S:B/S:N/CV%
plot_plate_qc(qc, metric = "ssmd")     # one plot fn, choose any metric
good <- filter_valid_plates(raw)       # drop failed plates (P03)
norm <- norm_by_control(good)          # % inhibition vs NC/PC
hits <- select_primary_hit(norm, threshold = 50)
summarize_primary_hit(hits)
## --- Secondary screen: dose-response -> ranked hits ----------------------
dr   <- import_dose_response(hts_dose_response,
                             response_col = "viability",
                             group_cols   = "cell_line")
drc  <- calc_drc_auc(fit_4pl_curve(dr, group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
ranked <- rank_hit_compound(params)                 # Strong/Moderate/Weak
annotate_hit_info(ranked, hts_compound_meta)        # add target/MoA
plot_single_drc(drc, drc$meta$curve_id[1])          # one fitted curve
plot_auc_heatmap(build_auc_matrix(drc$meta, zscore = "row"))

Example data

hts_primary_raw — simulated 96-well primary screen with raw signals (plates P01/P02 good, P03 failed).
hts_dose_response — simulated gradient dose-response screen (10 compounds, 3 cell lines, 2 replicates).
hts_compound_meta — simulated compound annotation table.
screen_doseresponse — a small simulated two-condition dose-response screen.
screen_delta_auc — a small simulated differential-AUC table.
screen_plate_qc, screen_plate_layout — QC / plate-layout demo data.

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

Vignette

See vignette("diffHTS") for an end-to-end walkthrough.

License

GPL-3