R Analysis Pipeline for a
Drug Potency Screening Laboratory

RESEARCH QUESTION

What the Laboratory Needs From Its Analysis

This R analysis pipeline example follows a pharmacology group at a research institute that runs plate-based potency assays every week. Its analysis lives in an inherited script and in spreadsheets that each member edits differently, so the same plate can give two IC50 values. The group wants one documented workflow that it owns and can extend.

The need is a single set of functions, kept in a small internal package, that every member runs the same way, a pipeline that reruns only the steps affected when a new batch arrives, and a report for each batch that any member can regenerate. The scientific questions and the decisions about compounds stay with the group. The work is to make the analysis explicit, tested and shared.

STUDY DESIGN AND DATA

About 300 Plates a Year in Repeated Experiments

Repeated in vitro potency assays, about 300 plates a year across several compound series, each plate holding a dilution series per compound with vehicle controls and full-inhibition controls. Every compound is tested in several independent experiments run on different days. Records hold the plate layout, the raw signal per well, compound, concentration, experiment date and batch.

METHOD AND ASSUMPTIONS

Why the Experiment, Not the Well, Carries the Uncertainty

Wells on one plate share the day, the cells and the dilution series, so they are replicates within an experiment, and the independent experiment is the unit that carries the uncertainty. After layout checks on control wells and edge wells, each plate is normalized to its own vehicle and full-inhibition controls, and a four-parameter logistic curve is fitted for each compound and experiment. The top and bottom of the curve are fixed only where the controls justify it, and a compound whose curve does not reach a plateau within the tested range receives an interval or a bound, not a point estimate presented as exact.

Log IC50 values from independent experiments are combined in a mixed model with a random term for experiment day, which gives each compound's estimate and the comparisons between compounds with intervals that reflect variation between experiments. Where a curve is poorly determined, bootstrap or profile intervals replace the usual approximation. The package carries unit tests built on reference plates with known answers, and every run records the R version, the package versions and the random seed, so a number can be traced to the code that produced it.

OUTPUT AND INTERPRETATION

How a Batch Report Is Read

Each batch report shows the plate quality checks, the normalized data with the fitted curve for each compound and experiment, and a table of log IC50 per experiment, with the combined estimate for each compound and its 95% confidence interval, back-transformed to concentration for reading. Potency differences between compounds are shown as ratios with intervals.

A change log records the plates added in each batch and any change to the code since the last report, and rerunning the pipeline on the same plate data reproduces the same values. The report states which estimates rest on curves without a clear plateau. Which compounds move forward is the group's choice; the report supplies the potency estimates and how precise each one is.

REPORTING

What the Researcher Received

A methods text that names the R version, the packages and the model, written for the statistical checklist of the journals the group targets, and a short guide for adding a new assay type to the package. The scientific interpretation, the decisions about compounds and the conclusions remain with the research team.