MorphoStat: A Statistics-Aware Pipeline for Morphological Profiling Analysis
Altobi, A.; Heo, D.
Show abstract
High-content imaging produces thousands of morphological measurements per cell. Interpreting these measurements requires normalization to remove plate effects, statistical tests selected on the basis of data distribution, and control over false discoveries across many features tested at once. MorphoStat is an open-source Python pipeline that applies this sequence of steps automatically. Given a CSV file from CellProfiler or a compatible imaging platform, it removes low-quality wells, normalizes each plate against DMSO controls using a MAD-scaled z-score, routes each feature to a parametric or nonparametric test based on a distributional check, applies Benjamini-Hochberg correction, and writes out results and publication-ready figures. On the BBBC021 benchmark (MCF-7 breast-cancer cells, 632 wells, 473 features), MorphoStat recovered 12 of 13 known mechanism-of-action classes in principal component space, confirming that the normalization and statistical routing work as intended. The tool is available at https://github.com/Almunthir334/morphostat (DOI: 10.5281/zenodo.20354069) under the MIT license.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Alternate dyes for image-based profiling assays 94%
- FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines 92%
- High Content Phenotypic Profiling in Oesophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery 91%
Similar papers in this journal
- Fully unsupervised deep mode of action learning for phenotyping high-content cellular images 91%
- Improving Deconvolution Methods in Biology through Open Innovation Competitions: An Application to the Connectivity Map 90%
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging 90%
Similar papers in this journal
- The Image Data Explorer: interactive exploration of image-derived data 92%
- HTSplotter: an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening 91%
- easyXpress: An R package to analyze and visualize high-throughput C. elegans microscopy data generated using CellProfiler 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.