cellreportR

cellreportR is a complete analysis and reporting pipeline for routine cell-culture laboratory diagnostics with microscopic evaluation. It picks up where segmentation leaves off: segmented single-cell data flows in, and structured statistical analyses, quality-controlled results, and publication- and audit-ready reports flow out.

Pipeline

Cell culture -> Treatment -> Staining -> Microscopy -> Segmentation
                                                    (segmantR / CellProfiler / QuPath)
                                                             |
                                                             v
                                                +---------------------+
                                                |     cellreportR     |
                                                |                     |
                                                |  Design & QC        |
                                                |  Normalization      |
                                                |  Hierarchical tests |
                                                |  Effect sizes + ROC |
                                                |  Dose-response      |
                                                |  Visualization      |
                                                |  Report generation  |
                                                |  Shiny dashboard    |
                                                +---------------------+
                                                             |
                                                             v
                                                Structured diagnostic report

Installation

# install.packages("pak")
pak::pak("cttir/cellreportR")

Quick example

library(cellreportR)

exp <- cr_example_experiment(seed = 42)
exp <- cr_qc_filter(exp, min_area = 50, max_area = 5000)

res <- cr_test_all(exp,
                   channel = "marker_1",
                   control_group = "Untreated",
                   level = "replicate")

cr_plot_effect_sizes(res)

Interactive analysis

cr_run_app()

The Shiny app provides a guided seven-tab workflow covering data import, QC, normalization, statistical analysis, dose-response fitting, interactive visualisation and report generation.

Documentation

• Getting Started
• Statistical Analysis
• Dose-Response
• Shiny App Guide

Citation

If you use cellreportR in your research, please cite the package:

citation("cellreportR")

Use of LLM tools

Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.

All scientific claims, methodological choices, analyses, interpretations, and conclusions are the author's own. No LLM-generated text was incorporated without review and revision, and every reference was verified against its DOI, arXiv ID, or ISBN.