Unit tests

SummarizedExperiment

This package provides containers to represent genomic experimental data as 2-dimensional matrices, follows Bioconductor's SummarizedExperiment. In these matrices, the rows typically denote features or genomic regions of interest, while columns represent samples or cells.

The package currently includes representations for both SummarizedExperiment and RangedSummarizedExperiment. A distinction lies in the fact RangedSummarizedExperiment object provides an additional slot to store genomic regions for each feature and is expected to be GenomicRanges (more here).

Install

To get started, Install the package from PyPI,

pip install summarizedexperiment

Usage

A SummarizedExperiment contains three key attributes,

assays: A dictionary of matrices with assay names as keys, e.g. counts, logcounts etc.
row_data: Feature information e.g. genes, transcripts, exons, etc.
column_data: Sample information about the columns of the matrices.

First lets mock feature and 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 0.4.5

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 0.4.5
File	Size	Uploaded
summarizedexperiment-0.4.5.tar.gz	1.0 MB	Jun 14, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for summarizedexperiment 0.4.5
File	Interpreter	ABI	Platform	Reset
SummarizedExperiment-0.4.5-py3-none-any.whl 21.8 kB Jun 14, 2024	Python 3	none	any	Details

Total release size: 1.0 MB

Release files / summarizedexperiment-0.4.5.tar.gz

Download URL	summarizedexperiment-0.4.5.tar.gz
Size	1.0 MB
Tags	Source
SHA-256 checksum How to use checksums	`945f1b4f9dd49e860d7c707bf5f3a2affcf3d17fef1299a11c41802f0955425b`
BLAKE2b-256 checksum How to use checksums	`9c7581c8948684a0e5ae7cb7033316c36b93abbc7d82b852f416935422e835f5`
Upload date	Jun 14, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.0 CPython/3.9.19`

Release files / SummarizedExperiment-0.4.5-py3-none-any.whl

Download URL	SummarizedExperiment-0.4.5-py3-none-any.whl
Size	21.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`cf5c780c545b8482c78de9ca21a8c3ab728dde339516802cb4c35782d2ac481c`
BLAKE2b-256 checksum How to use checksums	`f5ac1dcc0125bd6116e37d3875c551ac440874285ea4deab5f80a12a9fb57160`
Upload date	Jun 14, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.0 CPython/3.9.19`