https://github.com/biocpy/multiassayexperiment
Container class to represent and manage multi-omics genomic experiments
https://github.com/biocpy/multiassayexperiment
multiassayexperiment
Last synced: 10 months ago
JSON representation
Container class to represent and manage multi-omics genomic experiments
- Host: GitHub
- URL: https://github.com/biocpy/multiassayexperiment
- Owner: BiocPy
- License: mit
- Created: 2022-06-16T23:08:24.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2025-04-21T16:29:13.000Z (10 months ago)
- Last Synced: 2025-04-21T17:35:37.199Z (10 months ago)
- Topics: multiassayexperiment
- Language: Python
- Homepage: https://biocpy.github.io/MultiAssayExperiment/
- Size: 4.16 MB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
Awesome Lists containing this project
README
[](https://pyscaffold.org/)
[](https://pypi.org/project/MultiAssayExperiment/)
# 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](https://bioconductor.org/packages/release/bioc/html/MultiAssayExperiment.html).
## Install
To get started, install the package from [PyPI](https://pypi.org/project/multiassayexperiment/)
```shell
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:
```python
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:
```python
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](https://biocpy.github.io/MultiAssayExperiment/).
## Note
This project has been set up using PyScaffold 4.5. For details and usage
information on PyScaffold see https://pyscaffold.org/.