Common workflows

Full code for each workflow lives in the Jupyter notebooks under Notebooks/tutorials/. This page keeps only a short summary and pointers.

1. Basic analysis

The standard end-to-end workflow - from loading images and extracting visual fingerprints (AI descriptions) to comparing images and generating reports - is covered in the core tutorial.

• Tutorial: 03 - Core phenotyping workflow

2. Drug screening and time-course

Analyzing the effects of different drug treatments or mapping changes over time requires using experimental info (metadata). Whether your data is organized hierarchically ({drug}/{concentration}/{image}.tif) or encoded in filenames (Treated_24h), you can handle it through the metadata pipeline.

• Guide: Adding experiment info (metadata)

3. Property-based analysis and explaining results

To connect abstract AI patterns to real biology, compute classical physical properties (like area, brightness, and texture) and link them to your visual fingerprints.

• Tutorial: 04 - Exploratory analysis and explainability
• Guide: Understanding properties, Explaining AI results

4. Finding groups and outliers

Discover hidden groups of images (clusters) within a single condition or find unusual cells (outliers) using clustering and similarity checks.

• Tutorial: 04 - Exploratory analysis and explainability

5. Temporal images (testing new data quickly)

To see how new images fit into your existing pattern visualizations (PCA/t-SNE) without starting over, use temporal processing.

pheno.process_temporal_images(
    wrapper,
    files=["new_data/test1.tif", "new_data/test2.tif"],
)

pheno.plot_pca(color_by="condition", hover_features=["source"])

pheno.clear_temporal_data()

6. Batch processing large datasets

For very large datasets, use “checkpointing” so results are saved to disk as they are calculated.

pheno.process_images(
    wrapper,
    checkpoint_path="results/my_large_dataset.h5",
    batch_size=128,
)

7. Plate / Batch correction

If your data comes from multiple plates or batches, you can correct for technical variation (plate effects) using control wells.

# Correct embeddings using 'Control' wells in each 'Plate'
pheno.correct_batches(
    batch_metadata_key="Plate",
    control_filters={"condition": "Control"},
    method="sphering",  # or "zscore"
    source="embeddings", # or "properties"
)

See Best practices - performance for memory sizing.

See also

Topic	Where
Installation and quick start	Getting started
Folder structure and metadata	Preparing your data, Adding experiment info
Visual fingerprints, channel modes, and results	Core concepts
Full API reference	API & data formats, Pipeline API
Property sets and custom functions	Select properties, Understanding properties
Reproducibility and optimization	Best practices