Container class to represent and manage multi-omics genomic experiments. Follows Bioconductor's MAE R/Package.
Package is published to PyPI
pip install multiassayexperiment
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.
This project has been set up using PyScaffold 4.5. For details and usage information on PyScaffold see https://pyscaffold.org/.