MultiAssayExperiment

Container class to represent and manage multi-omics genomic experiments. MultiAssayExperiment (MAE) simplifies the management of multiple experimental assays conducted on a shared set of specimens, follows Bioconductor's MAE R/Package.

Install

To get started, install the package from PyPI

pip install multiassayexperiment

Usage

An MAE contains three main entities,

Primary information (column_data): Bio-specimen/sample information. The column_data may provide information about patients, cell lines, or other biological units. Each row in this table represents an independent biological unit. It must contain an index that maps to the 'primary' in sample_map.
Experiments (experiments): Genomic data from each experiment. either a SingleCellExperiment, SummarizedExperiment, RangedSummarizedExperiment or any class that extends a SummarizedExperiment.
Sample Map (sample_map): Map biological units from column_data to the list of experiments. Must contain columns,
- assay provides the names of the different experiments performed on the biological units. All experiment names from experiments must be present in this column.
- primary contains the sample name. All names in this column must match with row labels from col_data.
- colname is the mapping of samples/cells within each experiment back to its biosample information in col_data.
Each sample in column_data may map to one or more columns per assay.

Let's start by first creating few experiments:

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"])

Finally, we can create an MultiAssayExperiment object:

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/.

BiocPy/MultiAssayExperiment

MultiAssayExperiment

Install

Usage

Note