Unit tests

SummarizedExperiment

Container to represent genomic experiments, follows Bioconductor's SummarizedExperiment.

Install

Package is published to PyPI,

pip install summarizedexperiment

Usage

Currently supports SummarizedExperiment & RangedSummarizedExperiment classes

First create necessary sample data

from random import random
import pandas as pd
import numpy as np
from biocframe import BiocFrame

nrows = 200
ncols = 6
counts = np.random.rand(nrows, ncols)
row_data = BiocFrame(
    {
        "seqnames": [
            "chr1",
            "chr2",
            "chr2",
            "chr2",
            "chr1",
            "chr1",
            "chr3",
            "chr3",
            "chr3",
            "chr3",
        ]
        * 20,
        "starts": range(100, 300),
        "ends": range(110, 310),
        "strand": ["-", "+", "+", "*", "*", "+", "+", "+", "-", "-"] * 20,
        "score": range(0, 200),
        "GC": [random() for _ in range(10)] * 20,
    }
)

col_data = pd.DataFrame(
    {
        "treatment": ["ChIP", "Input"] * 3,
    }
)

To create a SummarizedExperiment,

from summarizedexperiment import SummarizedExperiment

tse = SummarizedExperiment(
    assays={"counts": counts}, row_data=row_data, column_data=col_data,
    metadata={"seq_platform": "Illumina NovaSeq 6000"},
)

## output
class: SummarizedExperiment
dimensions: (200, 6)
assays(1): ['counts']
row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
row_names(0):
column_data columns(1): ['treatment']
column_names(0):
metadata(1): seq_platform

To create a RangedSummarizedExperiment

from summarizedexperiment import RangedSummarizedExperiment
from genomicranges import GenomicRanges

trse = RangedSummarizedExperiment(
    assays={"counts": counts}, row_data=row_data,
    row_ranges=GenomicRanges.from_pandas(row_data.to_pandas()), column_data=col_data
)

## output
class: RangedSummarizedExperiment
dimensions: (200, 6)
assays(1): ['counts']
row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
row_names(0):
column_data columns(1): ['treatment']
column_names(0):
metadata(0):

For more examples, checkout the documentation.

Note

This project has been set up using PyScaffold 4.5. For details and usage information on PyScaffold see https://pyscaffold.org/.

Release files for summarizedexperiment 0.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for summarizedexperiment 0.4.0
File	Size	Uploaded
SummarizedExperiment-0.4.0.tar.gz	1.0 MB	Dec 28, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for summarizedexperiment 0.4.0
File	Interpreter	ABI	Platform	Reset
SummarizedExperiment-0.4.0-py3-none-any.whl 20.8 kB Dec 28, 2023	Python 3	none	any	Details

Total release size: 1.0 MB

Release files / SummarizedExperiment-0.4.0.tar.gz

Download URL	SummarizedExperiment-0.4.0.tar.gz
Size	1.0 MB
Tags	Source
SHA-256 checksum How to use checksums	`7eff6be029ccda66d7e210ed7a7ff01ecfe47a2eedd13e598f75d08caa2070b2`
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Upload date	Dec 28, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.18`

Release files / SummarizedExperiment-0.4.0-py3-none-any.whl

Download URL	SummarizedExperiment-0.4.0-py3-none-any.whl
Size	20.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`22fba1d7d90137a3db310c19affc5c45b0205e44cc81baeaa51c8261d062a231`
BLAKE2b-256 checksum How to use checksums	`fd6c525837d5c0f44e9416026223ee6e262851bbcb7eb93539ce2adbffaecf85`
Upload date	Dec 28, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.18`