---
license: mit
language:
  - en
tags:
  - genomics
  - transcription-factors
  - yeast
  - saccharomyces-cerevisiae
pretty_name: "Yeast Dual Threshold Optimization Analysis"

features:
- applies_to:
  - dto
  fields:
  - name: pr_ranking_column
    dtype:
      class_label: ["log2fc", "pvalue"]
    description: >-
      Describes which column, effect (log2fc) or pvalue was used
      in ranking the perturbation response data. see scripts/dto_preparation.R
      for more info on each dataset
  - name: binding_rank_threshold
    dtype: float64
    description: >-
      Rank threshold used for the binding dataset in the DTO analysis. This
      represents the rank cutoff that maximizes overlap significance between
      binding and perturbation datasets.
    role: quantitative_measure
  - name: perturbation_rank_threshold
    dtype: float64
    description: >-
      Rank threshold used for the perturbation dataset in the DTO analysis.
      This represents the rank cutoff that maximizes overlap significance
      between binding and perturbation datasets.
    role: quantitative_measure
  - name: binding_set_size
    dtype: float64
    description: >-
      Number of targets in the binding dataset at the optimal rank threshold.
      This is the size of the set used to calculate overlap with the
      perturbation dataset.
    role: quantitative_measure
  - name: perturbation_set_size
    dtype: float64
    description: >-
      Number of targets in the perturbation dataset at the optimal rank
      threshold. This is the size of the set used to calculate overlap with
      the binding dataset.
    role: quantitative_measure
  - name: dto_fdr
    dtype: float64
    description: >-
      False discovery rate (FDR) for the dual threshold optimization test. Lower
      values indicate more significant overlap between binding and
      perturbation target sets. Missing values (NA) indicate insufficient
      data for DTO analysis.
    role: quantitative_measure
  - name: dto_empirical_pvalue
    dtype: float64
    description: >-
      Empirical p-value from permutation testing for the dual threshold
      optimization. This represents the probability of observing the observed
      overlap by chance. Missing values (NA) indicate insufficient data for
      DTO analysis.
    role: quantitative_measure

configs:
  - config_name: dto
    description: >-
      Dual threshold optimization analysis results comparing transcription factor
      binding experiments with gene expression perturbation experiments. The
      dataset is partitioned by binding dataset and perturbation dataset,
      allowing efficient queries for specific binding-perturbation comparisons.
    dataset_type: comparative
    data_files:
      - split: train
        path: dto/*/*/*.parquet
    dataset_info:
      features:
        - name: binding_id
          dtype: string
          description: >-
            Composite sample identifier for the binding experiment in format
            'repo_id;config_name;sample_id' (e.g.,
            'BrentLab/callingcards;annotated_feature_reprocess_intergenic_analysis;127')
          role: source_sample
        - name: perturbation_id
          dtype: string
          description: >-
            Composite sample identifier for the perturbation experiment in format
            'repo_id;config_name;sample_id' (e.g.,
            'BrentLab/hackett_2020;hackett_2020_analysis_set;1504')
          role: source_sample
        - name: binding_repo_dataset
          dtype: string
          description: >-
            Partition key for the binding dataset: the repository name and config
            name joined by a hyphen (e.g., 'rossi_2021-macs_kang').
        - name: perturbation_repo_dataset
          dtype: string
          description: >-
            Partition key for the perturbation dataset: the repository name and
            config name joined by a hyphen (e.g., 'hackett_2020-hackett_2020_analysis_set').
      partitioning:
        enabled: true
        partition_by: ["binding_repo_dataset", "perturbation_repo_dataset"]
        path_template: "dto/binding_repo_dataset={binding_repo_dataset}/perturbation_repo_dataset={perturbation_repo_dataset}/*.parquet"
---

# Yeast Comparative Analysis

Analyses that relate samples from different datasets in the BrentLab yeast
collection. Each row relates two or more samples, which are referenced by composite
identifiers of the form `repo_id;config_name;sample_id`, rather than describing the
samples of a single dataset. Every config here has `dataset_type: comparative`.

The repository currently holds one analysis, `dto`, and can hold others as additional
configs.

## dto: dual threshold optimization

[Dual threshold optimization](https://github.com/BrentLab/dual_threshold_optimization)
(DTO) compares a binding dataset with a perturbation response dataset for the same
regulator. For each pair of a binding sample and a perturbation sample it finds the
rank thresholds on the two target lists that maximize the significance of their
overlap, and reports the empirical p-value and false discovery rate of that overlap
(`dto_empirical_pvalue`, `dto_fdr`). `pr_ranking_column` records whether the
perturbation targets were ranked by effect (`log2fc`) or `pvalue`. See `scripts/` for
how the inputs are prepared and the results parsed.

The data is partitioned by binding dataset and perturbation dataset, one file per
pair:

```
dto/binding_repo_dataset=<repo>-<config>/perturbation_repo_dataset=<repo>-<config>/part-0.parquet
```

## Accessing Data

The examples below require
[labretriever](https://github.com/cmatKhan/labretriever#installation)
(`pip install labretriever`) and/or the
[HuggingFace Hub client](https://huggingface.co/docs/huggingface_hub/installation)
(`pip install huggingface_hub`).

### Accessing Data with labretriever

This repository is part of a collection configured as a unified database using
[labretriever.VirtualDB](https://cmatkhan.github.io/labretriever/virtual_db_configuration/).
Download the
[collection config](https://github.com/BrentLab/tfbpshiny/blob/main/tfbpshiny/brentlab_yeast_collection.yaml)
and use it to query the data directly in Python, or with an AI assistant using the
[labretriever plugin](https://cmatkhan.github.io/labretriever/mcp_server/#quick-install-claude-code-plugin).

The collection config's `links` block maps each composite identifier field to the
primary datasets it references. labretriever uses it to expose a `dto_expanded` view
that splits `binding_id` into the primary dataset (`binding_id_source`) and the sample
id (`binding_id_id`), and does the same for `perturbation_id`.

```python
from labretriever.virtual_db import VirtualDB
from labretriever.datacard import DataCard

# Metadata
card = DataCard("BrentLab/yeast_comparative_analysis")
print([c.config_name for c in card.configs])  # list available datasets

# path to the downloaded brentlab_yeast_collection.yaml
vdb = VirtualDB("/path/to/brentlab_yeast_collection.yaml")

print(vdb.get_dataset_description("dto"))
vdb.query("SELECT * FROM dto_expanded LIMIT 5")
```

### Direct parquet access

Use `DataCard.info()` to inspect available files, then download and query with
DuckDB. The `dto` data is partitioned, so download a single binding and perturbation
pair, or the whole directory.

```python
from huggingface_hub import snapshot_download
import duckdb

repo_path = snapshot_download(
    repo_id="BrentLab/yeast_comparative_analysis",
    repo_type="dataset",
    allow_patterns="dto/**",
)
conn = duckdb.connect()
# returns a pandas DataFrame with the first 5 rows; the partition keys are
# available as columns because of hive_partitioning
conn.execute(
    "SELECT * FROM read_parquet(?, hive_partitioning = true) LIMIT 5",
    [f"{repo_path}/dto/*/*/*.parquet"],
).df()
```

### Accessing using R

Clone the repository and read the partitioned dataset with
[arrow](https://arrow.apache.org/docs/r/):

```r
# install.packages("arrow")
arrow::open_dataset("dto") |> dplyr::collect()
```