metacell package

Subpackages

• metacell.bin package
  • Submodules
  • metacell.bin.metacell_call module
  • metacell.bin.metacell_import module
  • metacell.bin.metacell_make module
  • Module contents
• metacell.dash package
  • Submodules
  • metacell.dash.app module
  • metacell.dash.main module
  • metacell.dash.plots module
  • Module contents
• metacell.dynamake package
  • Submodules
  • metacell.dynamake.analysis module
  • metacell.dynamake.common module
  • metacell.dynamake.group module
  • metacell.dynamake.importation module
  • metacell.dynamake.view module
  • Module contents
• metacell.storage package
  • Submodules
  • metacell.storage.genes module
  • metacell.storage.hca module
  • metacell.storage.helpers module
  • metacell.storage.imports module
  • metacell.storage.matrices module
  • metacell.storage.metadata module
  • metacell.storage.profiles module
  • metacell.storage.views module
  • Module contents

Submodules

metacell.analyze module

Perform analysis on metacell data.

metacell.few_profiles module

Directly group a small number of profiles.

metacell.many_profiles module

Group a large number of profiles using divide-and-conquer.

metacell.parameters module

Provide general purpose parameters and utilities.

metacell.pick module

Pick specific genes and/or profiles from a data frame.

metacell.tracks module

Track the trajectory of some profiles through the grouping process.

metacell.utilities module

Generic utilities.

metacell.utilities.combine_uuids(uuids: List[uuid.UUID]) → uuid.UUID

Use md5sum to combine multiple UUIDs into a single UUID.

metacell.utilities.file_uuid(path: str) → uuid.UUID

Compute a checksum of a disk file.

metacell.utilities.nans_count(array: tgutils.numpy.ArrayFloat32) → int

Count the number of NaN values in an array.

metacell.utilities.non_nans_count(array: tgutils.numpy.ArrayFloat32) → int

Count the number of non-NaN values in an array.

metacell.utilities.only_lowest_ranks(array: tgutils.numpy.ArrayFloat32, keep_count: int) → tgutils.numpy.ArrayFloat32

Return only the lowest few values in an array.

metacell.utilities.rank_array(array: tgutils.numpy.ArrayFloat32) → tgutils.numpy.ArrayFloat32

Convert an array of values to an array of ranks.

The result is still an array of float where NaN values get a NaN rank.

metacell.utilities.sliding_window_functions(values: tgutils.numpy.ArrayFloat32, order_by: tgutils.numpy.ArrayFloat32, window_size: int, functions: List[str]) → Dict[str, tgutils.numpy.ArrayFloat32]

Compute some function(s) on a sliding window.

The values are first sorted by the content of a second array, then the sliding window is applied (repeating the 1st and last elements as needed), the function(s) are applied, and the results are unsorted back into the proper positions.

Currently supported functions are median, mean, std and var.

metacell.utilities.sum_profiles_loop(expected: int) → tgutils.application.Loop

Create a logged loop for summing profiles.

metacell.version module

Version is generated by setup.py.

metacell.visualizations module

Visualizations of computed metacell data.

Module contents

Main Metacell module.