README.md

---
license: mit
language:
- en
tags:
- genomics
- yeast
- transcription
- perturbation
- response
- overexpression
pretty_name: Hackett, 2020 Overexpression
size_categories:
- 1M<n<10M
experimental_conditions:
  temperature_celsius: 30
  cultivation_method: chemostat
  media:
    name: minimal
    carbon_source:
      - compound: D-glucose
        # Saldanha et al 2004: 10 g/l
        concentration_percent: 1
doi: https://doi.org/10.15252/msb.20199174
citation: >-
  Hackett, SR, Baltz, EA, Coram, M, Wranik, BJ, Kim, et al. 2020. Learning causal networks using inducible transcription factors and transcriptome-wide time series. Molecular Systems Biology.

features:
- applies_to:
  - hackett_2020
  - hackett_2020_analysis_set
  fields:
  - name: regulator_locus_tag
    dtype: string
    description: >-
      induced transcriptional regulator systematic ID.
      See hf/BrentLab/yeast_genome_resources
    role: regulator_identifier
  - name: regulator_symbol
    dtype: string
    description: >-
      induced transcriptional regulator common name. If no common name exists,
      then the `regulator_locus_tag` is used.
    role: regulator_identifier
- applies_to:
  - hackett_2020
  - zev_gev
  - hackett_2020_analysis_set
  fields:
  - name: target_locus_tag
    dtype: string
    description: >-
      The systematic ID of the feature to which the effect/pvalue is assigned.
      See hf/BrentLab/yeast_genome_resources
    role: target_identifier
  - name: target_symbol
    dtype: string
    description: >-
      The common name of the feature to which the effect/pvalue is assigned.
      If there is no common name, the `target_locus_tag` is used.
    role: target_identifier
  - name: time
    dtype:
      class_label:
        names: [0, 2, 5, 7, 8, 10, 12, 15, 20, 30, 45, 60, 90, 100, 120, 180, 290]
    description: time point (minutes)
    role: experimental_condition
  - name: mechanism
    dtype:
      class_label:
        names: ["GEV", "ZEV"]
    description: Synthetic TF induction system (GEV or ZEV)
    role: experimental_condition
    definitions:
      GEV:
        perturbation_method:
          type: inducible_overexpression
          system: GEV
          inducer: beta-estradiol
          description: "Galactose-inducible estrogen receptor-VP16 fusion system"
      ZEV:
        perturbation_method:
          type: inducible_overexpression
          system: ZEV
          inducer: beta-estradiol
          description: "Z3 (synthetic zinc finger)-estrogen receptor-VP16 fusion system"
  - name: restriction
    dtype:
      class_label:
        names: ["M", "N", "P"]
    description: >-
      nutrient limitation, one of P (phosphate limitation (20 mg/l).),
      N (Nitrogen‐limited cultures were maintained at 40 mg/l ammonium sulfate) or
      M (Not defined in the paper or on the Calico website)
    role: experimental_condition
    definitions:
      P:
        media:
          nitrogen_source:
            - compound: ammonium_sulfate
              # Saldanha et al 2004: 5 g/l
              concentration_percent: 0.5
          phosphate_source:
            - compound: potassium_phosphate_monobasic
              # Hackett et al 2020: 20 mg/l
              concentration_percent: 0.002
      N:
        media:
          nitrogen_source:
            - compound: ammonium_sulfate
              # Hackett et al 2020: 40 mg/l
              concentration_percent: 0.004
      M:
        description: "Not defined in the paper or on the Calico website"
  - name: date
    dtype: string
    description: date performed
    role: experimental_condition
  - name: strain
    dtype: string
    description: strain name
    role: experimental_condition
  - name: green_median
    dtype: float
    description: median of green (reference) channel fluorescence
    role: quantitative_measure
  - name: red_median
    dtype: float
    description: median of red (experimental) channel fluorescence
    role: quantitative_measure
  - name: log2_ratio
    dtype: float
    description: log2(red / green) subtracting value at time zero
    role: quantitative_measure
  - name: log2_cleaned_ratio
    dtype: float
    description: Non-specific stress response and prominent outliers removed
    role: quantitative_measure
  - name: log2_noise_model
    dtype: float
    description: estimated noise standard deviation
    role: quantitative_measure
  - name: log2_cleaned_ratio_zth2d
    dtype: float
    description: >-
      cleaned timecourses hard-thresholded based on
      multiple observations (or last observation) passing the noise model
    role: quantitative_measure
  - name: log2_selected_timecourses
    dtype: float
    description: >-
      cleaned timecourses hard-thresholded based on single observations
      passing noise model and impulse evaluation of biological feasibility
    role: quantitative_measure
  - name: log2_shrunken_timecourses
    dtype: float
    description: >-
      selected timecourses with observation-level shrinkage based on
      local FDR (false discovery rate). Most users of the data will want
      to use this column.
    role: quantitative_measure
  - name: responsive
    dtype: bool
    description: >-
      This labels targets, for a given regulator, with abs(log2_shrunken_timecourses) `>` 0

configs:
  - config_name: hackett_2020
    description: >-
      Microarray expression data comparing cells without estradiol inducer, which express a TF at a very low level and post-induction, which express the TF at a high level by 15-30 minutes post induction. Contains many time points. Cells were grown in minimal medium with glucose in continuous-flow chemostats. In most experiments growth was limited by phosphate limitation.
    dataset_type: annotated_features
    metadata_fields: ["sample_id", "regulator_locus_tag", "regulator_symbol", "time", "mechanism", "restriction", "date", "strain"]
    data_files:
      - split: train
        path: hackett_2020.parquet
    dataset_info:
      features:
        - name: sample_id
          dtype: integer
          description: >-
            unique identifier for a specific sample. The sample ID identifies a unique
            (regulator_locus_tag, time, mechanism, restriction, date, strain) tuple.
        - name: db_id
          dtype: integer
          description: >-
            an old unique identifer, for use internally only. Deprecated and will be removed eventually.
            Do not use in analysis. db_id = 0, for GEV and Z3EV, means that those samples are not
            included in the original DB.

  - config_name: hackett_2020_analysis_set
    description: >-
      This dataset filters the full data such that a single strain is chosen for each
      regulator. Where a ZEV with phosphate restriction is available, that is chosen,
      otherwise a GEV with phosphate restriction is chosen, and if that is not
      available, then the first available sample is chosen. There are 4 regulators,
      GCN4, RDS2, SWI1, MAC1, which have multiple replicates of the same conditions.
      For the time being, these regulators are entirely removed.
      See `scripts/adding_analysis_set.R`
    default: true
    dataset_type: annotated_features
    metadata_fields: ["sample_id", "regulator_locus_tag", "regulator_symbol", "time", "mechanism", "restriction", "date", "strain"]
    data_files:
      - split: train
        path: hackett_2020_analysis_set.parquet
    dataset_info:
      features:
        - name: sample_id
          dtype: integer
          description: >-
            unique identifier for a specific sample. The sample ID identifies a unique
            (regulator_locus_tag, time, mechanism, restriction, date, strain) tuple.
        - name: db_id
          dtype: integer
          description: >-
            an old unique identifer, for use internally only. Deprecated and will be removed eventually.
            Do not use in analysis. db_id = 0, for GEV and Z3EV, means that those samples are not
            included in the original DB.

  - config_name: zev_gev
    description: These are the Z3EV and GEV control strains (no specifically tagged TF)
    dataset_type: annotated_features
    metadata_fields: ["sample_id", "time", "mechanism", "restriction", "date", "strain"]
    data_files:
      - split: train
        path: zev_gev_strains.parquet
    dataset_info:
      features:
        - name: sample_id
          dtype: integer
          description: >-
            unique identifier for a specific sample. The sample ID identifies a unique
            (regulator_locus_tag, time, mechanism, restriction, date, strain) tuple.
---
# Hackett 2020

This Dataset is a parsed version of the data provided by
[Calicolabs](https://idea.research.calicolabs.com/data) under the heading "Raw &
processed gene expression data". See `scripts/` for more details on the parsing from the
data provided by Calico to this Dataset.


[Hackett SR, Baltz EA, Coram M, Wranik BJ, Kim G, Baker A, Fan M, Hendrickson DG, Berndl
M, McIsaac RS. Learning causal networks using inducible transcription factors and
transcriptome-wide time series. Mol Syst Biol. 2020 Mar;16(3):e9174. doi:
10.15252/msb.20199174. PMID: 32181581; PMCID:
PMC7076914.](https://doi.org/10.15252/msb.20199174)

## 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).

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

# Citation and metadata
card = DataCard("BrentLab/hackett_2020")
print([c.config_name for c in card.configs])  # list available datasets
info = card.info()
print(info["doi"])
print(info["citation"])

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

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

### Direct parquet access

The repository contains more data than what is exposed through the collection
configuration. Use `DataCard.info()` to inspect available files, then download
and query with DuckDB.

Most files in this repository are single parquet files and can be read directly:

```python
from huggingface_hub import snapshot_download
import duckdb

repo_path = snapshot_download(
    repo_id="BrentLab/hackett_2020",
    repo_type="dataset",
    allow_patterns="hackett_2020.parquet",
)
conn = duckdb.connect()
# returns a pandas DataFrame with the first 5 rows
conn.execute(
    "SELECT * FROM read_parquet(?) LIMIT 5",
    [f"{repo_path}/hackett_2020.parquet"],
).df()
```

### Accessing using R

Clone the repository and read parquet files directly with
[arrow](https://arrow.apache.org/docs/r/):

```r
# install.packages("arrow")
arrow::read_parquet("hackett_2020.parquet")
```