CellPaintSSL_IN: Preprocessed Cell Painting MOA plate for CellPaintSSL zero-shot inference

Summary CellPaintSSL_IN is a preprocessed Cell Painting dataset built from a single JUMP‐MOA plate and prepared specifically for running the CellPaintSSL model in a strictly zero‐shot regime (no fine‐tuning). The dataset contains five‐channel Cell Painting images transformed into the input format required by the official CellPaintSSL implementation, together with a manifest file (`dataset.csv`) that links each image to well, site, and perturbation metadata. This dataset is derived from the same experimental plate used in our MOAProfiler_Feature_Extractor workflow, but it uses a different preprocessing and manifest format tailored to CellPaintSSL rather than MOAProfiler. Contents - `images/` – preprocessed five‐channel Cell Painting images in the format expected by CellPaintSSL.- `dataset.csv` – manifest linking each image to `plate`, `well`, `site`, `perturbation_id`, etc. All metadata and file naming conventions are compatible with the official CellPaintSSL codebase from Bayer (https://github.com/Bayer-Group/CellPaintSSL). How the data were generated Raw Cell Painting images from the JUMP–MOA plate were stored as separate 16-bit TIFF files per channel with filenames of the form BaseName-chX*.tiff. For each field of view (identified by BaseName), we collected the required five channels and built a 5×H×W uint16 stack in the channel order expected by our CellPaintSSL pipeline, corresponding to (mito, agp, rna, er, dna). For each channel, intensities were clipped to the 0.1–99.9th percentiles and then linearly rescaled to the full 16-bit range (0–65535). The five rescaled channels were stacked along a new axis and saved as a single multi-channel TIFF (BaseName.tiff). In addition, a global Otsu threshold was computed on the DNA channel (normalized to [0, 1]) and stored in the TIFF metadata as the field otsuth. The resulting CellPaintSSL_IN dataset therefore consists of 5-channel, per-field TIFF stacks (mito, agp, rna, er, dna) with standardized 16-bit intensity scaling and an accompanying per-image DNA Otsu threshold. Related resources - Code: https://github.com/lorakuznetsova/MOAProfiler_Feature_Extractor - MOAProfiler_IN dataset (preprocessed data for MOAProfiler): 10.5281/zenodo.17617785