Skip to main content

Save and load summarized experiments in the dolomite framework!

Project description

Project generated with PyScaffold PyPI-Server Unit tests

Save and load SummarizedExperiments in Python

Introduction

The dolomite-se package is the Python counterpart to the alabaster.se R package, providing methods for saving/reading SummarizedExperiment or RangeSummarizedExperiment objects within the dolomite framework. All components of the SummarizedExperiment - assays, row data and column data - are saved to their respective file representations, which can be loaded in a new R/Python environment for cross-language analyses.

Quick start

Let's mock up a SummarizedExperiment:

import summarizedexperiment
import biocframe
import numpy

se = summarizedexperiment.SummarizedExperiment(
    assays={ "counts": numpy.random.rand(1000, 200) },
    row_data=biocframe.BiocFrame(
        { "foo": numpy.random.rand(1000) }, 
        row_names = ["gene" + str(i) for i in range(1000)]
    ),
    column_data=biocframe.BiocFrame(
        { "whee": numpy.random.rand(200) },
        row_names = ["cell" + str(i) for i in range(200)]
    )
)

Now we can save it:

from dolomite_base import save_object
import dolomite_se
import os
from tempfile import mkdtemp

path = os.path.join(mkdtemp(), "test")
save_object(se, path)

And load it again, e,g., in a new session:

from dolomite_base import read_object

roundtrip = read_object(path)
## Class SummarizedExperiment with 1000 features and 200 samples
##   assays: ['counts']
##   row_data: ['foo']
##   column_data: ['whee']

This also works for RangeSummarizedExperiment objects storing row_ranges to the specified path.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dolomite_se-0.2.0.tar.gz (23.7 kB view details)

Uploaded Source

Built Distribution

dolomite_se-0.2.0-py3-none-any.whl (8.8 kB view details)

Uploaded Python 3

File details

Details for the file dolomite_se-0.2.0.tar.gz.

File metadata

  • Download URL: dolomite_se-0.2.0.tar.gz
  • Upload date:
  • Size: 23.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for dolomite_se-0.2.0.tar.gz
Algorithm Hash digest
SHA256 7a662fc2e561f35775c4259e47a5a2371e14f0a6ac65c8a0e30774d148ab28ba
MD5 09625b431ff69f91fa267990690a4113
BLAKE2b-256 8ad5a9dae35205a0b0f86958e65fd62aa96f4027743972de0cadb6f1849dbac7

See more details on using hashes here.

File details

Details for the file dolomite_se-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: dolomite_se-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 8.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for dolomite_se-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 37ca3809cf1f0435353e83c0405f6a01b7245b2a8cd93508a277d54052c7d52f
MD5 cfa3d36c5ad62d216447b59237ade99d
BLAKE2b-256 6871e847016605753627f1e3114ad0ec5bfbad2d56ea2cc3f0288f4c4d94fac4

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page