MultiAssayExperiment

Container class to represent and manage multi-omics genomic experiments. Follows Bioconductor's MAE R/Package.

Install

Package is published to PyPI

pip install multiassayexperiment

Usage

First create mock sample data

from random import random

import numpy as np
from biocframe import BiocFrame
from genomicranges import GenomicRanges
from iranges import IRanges

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

col_data_sce = BiocFrame({"treatment": ["ChIP", "Input"] * 3},
    row_names=[f"sce_{i}" for i in range(6)],
)

col_data_se = BiocFrame({"treatment": ["ChIP", "Input"] * 3},
    row_names=[f"se_{i}" for i in range(6)],
)

sample_map = BiocFrame({
    "assay": ["sce", "se"] * 6,
    "primary": ["sample1", "sample2"] * 6,
    "colname": ["sce_0", "se_0", "sce_1", "se_1", "sce_2", "se_2", "sce_3", "se_3", "sce_4", "se_4", "sce_5", "se_5"]
})

sample_data = BiocFrame({"samples": ["sample1", "sample2"]}, row_names= ["sample1", "sample2"])

Now we can create an instance of an MAE -

from multiassayexperiment import MultiAssayExperiment
from singlecellexperiment import SingleCellExperiment
from summarizedexperiment import SummarizedExperiment

tsce = SingleCellExperiment(
    assays={"counts": counts}, row_data=gr.to_pandas(), column_data=col_data_sce
)

tse2 = SummarizedExperiment(
    assays={"counts": counts.copy()},
    row_data=gr.to_pandas().copy(),
    column_data=col_data_se.copy(),
)

mae = MultiAssayExperiment(
    experiments={"sce": tsce, "se": tse2},
    column_data=sample_data,
    sample_map=sample_map,
    metadata={"could be": "anything"},
)

## output
class: MultiAssayExperiment containing 2 experiments
[0] sce: SingleCellExperiment with 200 rows and 6 columns
[1] se: SummarizedExperiment with 200 rows and 6 columns
column_data columns(1): ['samples']
sample_map columns(3): ['assay', 'primary', 'colname']
metadata(1): could be

For more use cases, 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/.