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[pre-commit.ci] pre-commit autoupdate #137

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a870dcd
[pre-commit.ci] pre-commit autoupdate
pre-commit-ci[bot] Aug 31, 2026
405c3b2
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 31, 2026
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4 changes: 2 additions & 2 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ repos:

- repo: https://github.com/astral-sh/ruff-pre-commit
# Ruff version.
rev: v0.16.2
rev: v0.16.5
hooks:
# Run the linter.
- id: ruff-check
Expand All @@ -55,7 +55,7 @@ repos:
# - id: codespell

- repo: https://github.com/PyCQA/bandit
rev: 1.7.9
rev: 1.9.4
hooks:
- id: bandit
args: ["-c", "pyproject.toml"]
Expand Down
194 changes: 106 additions & 88 deletions README.md
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Expand Up @@ -40,26 +40,31 @@ Genomic data often requires storing coordinates, annotations, and metadata toget

```python
# Gene annotation with nested structures
gene_annotations = BiocFrame({
"gene_id": ["GENE1", "GENE2", "GENE3"],
"symbol": ["BRCA1", "TP53", "EGFR"],
"location": BiocFrame({
"chromosome": ["chr17", "chr17", "chr7"],
"start": [43044295, 7668422, 55019017],
"end": [43125483, 7687550, 55211628],
"strand": ["-", "-", "+"],
}),
"transcripts": [
["NM_007294", "NM_007297", "NM_007300"],
["NM_000546"],
["NM_005228", "NM_201282"],
],
"pathways": [
["DNA repair", "Cell cycle"],
["Apoptosis", "Cell cycle", "DNA repair"],
["Cell growth", "Signal transduction"],
],
}, row_names=["ENSG00000012048", "ENSG00000141510", "ENSG00000146648"])
gene_annotations = BiocFrame(
{
"gene_id": ["GENE1", "GENE2", "GENE3"],
"symbol": ["BRCA1", "TP53", "EGFR"],
"location": BiocFrame(
{
"chromosome": ["chr17", "chr17", "chr7"],
"start": [43044295, 7668422, 55019017],
"end": [43125483, 7687550, 55211628],
"strand": ["-", "-", "+"],
}
),
"transcripts": [
["NM_007294", "NM_007297", "NM_007300"],
["NM_000546"],
["NM_005228", "NM_201282"],
],
"pathways": [
["DNA repair", "Cell cycle"],
["Apoptosis", "Cell cycle", "DNA repair"],
["Cell growth", "Signal transduction"],
],
},
row_names=["ENSG00000012048", "ENSG00000141510", "ENSG00000146648"],
)

print(gene_annotations)
```
Expand All @@ -70,27 +75,39 @@ When combining different types of omics data with varying structures:

```python
# Multi-omics data with different measurement types
multi_omics = BiocFrame({
"sample_id": ["S1", "S2", "S3"],
"rna_seq": np.array([
[100, 200, 150],
[300, 250, 180],
[120, 220, 160],
], dtype=np.float32),
"methylation": BiocFrame({
"cg0001": [0.85, 0.92, 0.78],
"cg0002": [0.45, 0.38, 0.52],
"cg0003": [0.12, 0.15, 0.10],
}),
"clinical": BiocFrame({
"age": [45, 52, 38],
"gender": ["M", "F", "F"],
"diagnosis": ["Type A", "Type B", "Type A"],
}),
}, column_data=BiocFrame({
"data_type": ["identifier", "expression", "epigenetic", "clinical"],
"source": ["lab", "sequencer", "array", "EHR"],
}))
multi_omics = BiocFrame(
{
"sample_id": ["S1", "S2", "S3"],
"rna_seq": np.array(
[
[100, 200, 150],
[300, 250, 180],
[120, 220, 160],
],
dtype=np.float32,
),
"methylation": BiocFrame(
{
"cg0001": [0.85, 0.92, 0.78],
"cg0002": [0.45, 0.38, 0.52],
"cg0003": [0.12, 0.15, 0.10],
}
),
"clinical": BiocFrame(
{
"age": [45, 52, 38],
"gender": ["M", "F", "F"],
"diagnosis": ["Type A", "Type B", "Type A"],
}
),
},
column_data=BiocFrame(
{
"data_type": ["identifier", "expression", "epigenetic", "clinical"],
"source": ["lab", "sequencer", "array", "EHR"],
}
),
)

print(multi_omics)
print("\nColumn metadata:")
Expand All @@ -103,31 +120,40 @@ For data with natural hierarchies (e.g., samples → patients → cohorts):

```python
# Hierarchical clinical trial data
clinical_trial = BiocFrame({
"patient_id": ["P001", "P002", "P003"],
"cohort": ["A", "A", "B"],
"samples": [
BiocFrame({
"sample_id": ["S001", "S002"],
"collection_date": ["2024-01-01", "2024-01-15"],
"vital_status": ["alive", "alive"],
}),
BiocFrame({
"sample_id": ["S003", "S004", "S005"],
"collection_date": ["2024-01-02", "2024-01-16", "2024-01-30"],
"vital_status": ["alive", "alive", "deceased"],
}),
BiocFrame({
"sample_id": ["S006"],
"collection_date": ["2024-01-03"],
"vital_status": ["alive"],
}),
],
}, metadata={
"trial_name": "PHASE_III_STUDY",
"start_date": "2024-01-01",
"status": "ongoing",
})
clinical_trial = BiocFrame(
{
"patient_id": ["P001", "P002", "P003"],
"cohort": ["A", "A", "B"],
"samples": [
BiocFrame(
{
"sample_id": ["S001", "S002"],
"collection_date": ["2024-01-01", "2024-01-15"],
"vital_status": ["alive", "alive"],
}
),
BiocFrame(
{
"sample_id": ["S003", "S004", "S005"],
"collection_date": ["2024-01-02", "2024-01-16", "2024-01-30"],
"vital_status": ["alive", "alive", "deceased"],
}
),
BiocFrame(
{
"sample_id": ["S006"],
"collection_date": ["2024-01-03"],
"vital_status": ["alive"],
}
),
],
},
metadata={
"trial_name": "PHASE_III_STUDY",
"start_date": "2024-01-01",
"status": "ongoing",
},
)

print(clinical_trial)
```
Expand Down Expand Up @@ -160,11 +186,7 @@ For example, we can nest a `BiocFrame` inside another `BiocFrame`:
obj = {
"ensembl": ["ENS00001", "ENS00002", "ENS00002"],
"symbol": ["MAP1A", "BIN1", "ESR1"],
"ranges": BiocFrame({
"chr": ["chr1", "chr2", "chr3"],
"start": [1000, 1100, 5000],
"end": [1100, 4000, 5500]
}),
"ranges": BiocFrame({"chr": ["chr1", "chr2", "chr3"], "start": [1000, 1100, 5000], "end": [1100, 4000, 5500]}),
}

bframe2 = BiocFrame(obj, row_names=["row1", "row2", "row3"])
Expand All @@ -188,7 +210,7 @@ print(bframe.shape)
print(bframe.get_column_names())
## ['ensembl', 'symbol']

print(bframe.column_names) # same as above
print(bframe.column_names) # same as above
## ['ensembl', 'symbol']
```

Expand Down Expand Up @@ -220,7 +242,7 @@ print(sliced)
## <list>
## [0] ENS00002

sliced = bframe[[0,2], ["symbol", "ensembl"]]
sliced = bframe[[0, 2], ["symbol", "ensembl"]]
print(sliced)
## BiocFrame with 2 rows and 2 columns
## symbol ensembl
Expand Down Expand Up @@ -280,9 +302,7 @@ print(modified)
Change the row or column names:

```python
modified = bframe.\
set_column_names(["FOO", "BAR"]).\
set_row_names(['alpha', 'bravo', 'charlie'])
modified = bframe.set_column_names(["FOO", "BAR"]).set_row_names(["alpha", "bravo", "charlie"])
print(modified)
## BiocFrame with 3 rows and 2 columns
## FOO BAR
Expand All @@ -295,9 +315,9 @@ print(modified)
We also support Bioconductor's metadata concepts, either along the columns or for the entire object:

```python
modified = bframe.\
set_metadata({ "author": "Jayaram Kancherla" }).\
set_column_data(BiocFrame({"column_source": ["Ensembl", "HGNC" ]}))
modified = bframe.set_metadata({"author": "Jayaram Kancherla"}).set_column_data(
BiocFrame({"column_source": ["Ensembl", "HGNC"]})
)
print(modified)
## BiocFrame with 3 rows and 2 columns
## ensembl symbol
Expand All @@ -317,8 +337,8 @@ We prefer not to do it this way as it can silently mutate ``BiocFrame`` instance
Nonetheless:

```python
testframe = BiocFrame({ "A": [1,2,3], "B": [4,5,6] })
testframe.column_names = ["column1", "column2" ]
testframe = BiocFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
testframe.column_names = ["column1", "column2"]
print(testframe)
## BiocFrame with 3 rows and 2 columns
## column1 column2
Expand All @@ -332,7 +352,7 @@ Similarly, we could set or replace columns directly:

```python
testframe["column2"] = ["A", "B", "C"]
testframe[1:3, ["column1","column2"]] = BiocFrame({"x":[4, 5], "y":["E", "F"]})
testframe[1:3, ["column1", "column2"]] = BiocFrame({"x": [4, 5], "y": ["E", "F"]})
## BiocFrame with 3 rows and 2 columns
## column1 column2
## <list> <list>
Expand Down Expand Up @@ -411,6 +431,7 @@ If this is not the case, e.g., with different columns across objects, we can use

```python
from biocframe import relaxed_combine_rows

modified2 = bframe2.set_column("foo", ["A", "B", "C", "D", "E"])
combined = relaxed_combine_rows(bframe1, modified2)
print(combined)
Expand All @@ -433,6 +454,7 @@ Similarly, if the rows are different, we can use **BiocFrame**'s `merge` functio

```python
from biocframe import merge

modified1 = bframe1.set_row_names(["A", "B", "C", "D", "E"])
modified3 = bframe3.set_row_names(["C", "D", "E", "F", "G"])
combined = merge([modified1, modified3], by=None, join="outer")
Expand All @@ -456,12 +478,8 @@ This conversion is easily achieved:

```python
from biocframe import BiocFrame
bframe = BiocFrame(
{
"foo": ["A", "B", "C", "D", "E"],
"bar": [True, False, True, False, True]
}
)

bframe = BiocFrame({"foo": ["A", "B", "C", "D", "E"], "bar": [True, False, True, False, True]})

pd = bframe.to_pandas()
print(pd)
Expand Down
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