Workflow Modules#

Aggregate#

Aggregates single-cell profiles into aggregated profiles based on the given strata.

For example, users can configure Metadata_Well as their strata in order to aggregate single-cell data into the Well level.

Utilize’s pycytominer’s aggregate module: https://github.com/cytomining/pycytominer/blob/c90438fd7c11ad8b1689c21db16dab1a5280de6c/pycytominer/aggregate.py

Parameters (params)

inputs:

  • profile: single-cell morphology dataset.

  • barcode (optional): file containing unique barcodes that maps to a specific plate

  • metadata: metadata file associated with single-cell morphology dataset.

outputs:

  • profile : aggregated datsaset

  • cell-counts : csv file containg cell counts per well

Annotate#

Generates an annotated profile with given metadata and is stored in the results/ directory.

Utilizes pycytominer’s annotate module: https://github.com/cytomining/pycytominer/blob/master/pycytominer/annotate.py

parameters

inputs:

  • profiles: single-cell morphology or aggregate profiles.

  • barcode: file containing unique barcodes that maps to a specific plate.

  • metadata: metadata file associated with single-cell morphology dataset.

output:

  • profiles: annotated profiles.

Common#

common.smk is a workflow module that sets up the expected input and output paths for the main analytical workflow.

Cytotable Convert#

Converts single-cell morphology dataset to parquet format.

Utilizes CytoTable’s convert workflow module: https://github.com/cytomining/CytoTable/blob/main/cytotable/convert.py

paramters

inputs:

  • profiles: single-cell morphology profiles.

  • barcode: file containing unique barcodes that maps to a specific plate.

  • metadata: metadata file associated with single-cell morphology dataset.

outputs:

  • profiles: converted single-cell morphology dataset.

Feature Select#

Performs feature selection based on the given profiles.

PyCytominer contains different operations to conduct its feature selection: variance_threshold, correlation_threshold, drop_na_columns, drop_outliers, and noise_removal.

Utilizes pycytominer’s feature select module: https://github.com/cytomining/pycytominer/blob/master/pycytominer/feature_select.py

paramters

inputs:

  • profiles: single-cell morphology datasets.

  • barcode: file containing unique barcodes that maps to a specific plate.

  • metadata: metadata file associated with single-cell morphology dataset.

outputs:

  • profiles: selected features profiles. “””

Generate consensus#

Creates consensus profiles that reflects unique signatures associated with external factors.

Utilize’s pycytominer’s consensus module: https://github.com/cytomining/pycytominer/blob/master/pycytominer/consensus.py

parameters

inputs:

  • profiles: selected features profiles.

  • barcode: file containing unique barcodes that maps to a specific plate.

  • metadata: metadata file associated with single-cell morphology dataset.

output:

  • profiles: consensus profiles.

Normalize#

Normalizing single-cell or aggregate features. Current default normalization method is standardize. Other methods include: robustize, mad_robustize, and spherize.

Utlizes pycytominer’s normalization module: https://github.com/cytomining/pycytominer/blob/c90438fd7c11ad8b1689c21db16dab1a5280de6c/pycytominer/normalize.py

  • profiles: single-cell morphology or annotated profiles.

  • barcode: file containing unique barcodes that maps to a specific plate.

  • metadata: metadata file associated with single-cell morphology dataset.

output

  • profiles: normalized profiles.