Tutorials

Start here if you are new to CytoTable. We’ve split material by audience:

• Image analysts (no engineering background required): follow the narrative tutorials below. They include downloadable data, exact commands, and what to expect. Please also feel free to reference the example notebooks.

• Engineers / power users: see any documentation, including the example notebooks, for tuning and integration details, or use the quick recipe below.

Who this helps (and doesn’t)

• Helps: image analysts who want to get CellProfiler/DeepProfiler/InCarta outputs into Parquet with minimal coding; people comfortable running a few commands.

• Not ideal: raw image ingestion or pipeline authoring (use CellProfiler/DeepProfiler upstream); workflows needing a GUI-only experience.

• Effort: install, copy/paste a few commands, validate outputs in minutes.

Tutorials (start here)

• Tutorial: CellProfiler SQLite or CSV to Parquet
  • What you will accomplish
  • Setup (copy-paste)
  • Inputs and outputs
  • Before you start
  • Step 1: choose your input type
  • Step 2: run the conversion (minimal Python)
  • Step 3: check that the outputs look right
  • What success looks like
• Tutorial: NPZ embeddings + metadata to Parquet
  • What you will accomplish
  • Setup (copy-paste)
  • Inputs and outputs
  • Step 1: define your paths
  • Step 2: run the conversion
  • Step 3: validate the output
  • What success looks like
• Tutorial: Merging multiple plates with Tablenumber
  • What you will accomplish
  • Setup (copy-paste)
  • Inputs and outputs
  • Step 1: define your paths
  • Step 2: run the conversion with tablenumber
  • Step 3: validate plate separation
  • Scenario callouts (“if your data looks like this…”)

Quick recipe: CellProfiler CSV to Parquet

This short recipe is for people comfortable with Python/CLI and parallels our older tutorial. If you prefer a guided, narrative walkthrough with downloadable inputs and expected outputs, use the tutorial above.

CellProfiler exports compartment CSVs (for example, “Cells.csv”, “Cytoplasm.csv”). CytoTable converts this data to Parquet from local or object-storage locations.

Files with similar names nested within sub-folders are concatenated by default (for example, folder/sub_a/cells.csv and folder/sub_b/cells.csv become a single folder.cells.parquet unless concat=False).

The dest_path parameter is used for intermediary work and must be a new file or directory path. It will be a directory when join=False and a single file when join=True.

from cytotable import convert

# Local CSVs with CellProfiler preset
convert(
    source_path="./tests/data/cellprofiler/ExampleHuman",
    source_datatype="csv",
    dest_path="ExampleHuman.parquet",
    dest_datatype="parquet",
    preset="cellprofiler_csv",
)

# S3 CSVs (unsigned) with CellProfiler preset
convert(
    source_path="s3://s3path",
    source_datatype="csv",
    dest_path="s3_local_result",
    dest_datatype="parquet",
    concat=True,
    preset="cellprofiler_csv",
    no_sign_request=True,
)