|
|
--- |
|
|
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 |
|
|
|
|
|
concentration_percent: 1 |
|
|
configs: |
|
|
- config_name: hackett_2020 |
|
|
description: TF overexpression data from Hackett 2020 |
|
|
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.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. |
|
|
- 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 |
|
|
- 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: float |
|
|
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 |
|
|
|
|
|
concentration_percent: 0.5 |
|
|
phosphate_source: |
|
|
- compound: potassium_phosphate_monobasic |
|
|
|
|
|
concentration_percent: 0.002 |
|
|
N: |
|
|
media: |
|
|
nitrogen_source: |
|
|
- compound: ammonium_sulfate |
|
|
|
|
|
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 |
|
|
--- |
|
|
# 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) |
|
|
|
|
|
This repo provides 1 dataset: |
|
|
|
|
|
- **hackett_2020**: TF overexpression data from Hackett 2020. |
|
|
|
|
|
## Usage |
|
|
|
|
|
The python package `tfbpapi` provides an interface to this data which eases |
|
|
examining the datasets, field definitions and other operations. You may also |
|
|
download the parquet datasets directly from hugging face by clicking on |
|
|
"Files and Versions", or by using the huggingface_cli and duckdb directly. |
|
|
In both cases, this provides a method of retrieving dataset and field definitions. |
|
|
|
|
|
### `tfbpapi` |
|
|
|
|
|
After [installing |
|
|
tfbpapi](https://github.com/BrentLab/tfbpapi/?tab=readme-ov-file#installation), you can |
|
|
adapt this [tutorial](https://brentlab.github.io/tfbpapi/tutorials/hfqueryapi_tutorial/) |
|
|
in order to explore the contents of this repository. |
|
|
|
|
|
### huggingface_cli/duckdb |
|
|
|
|
|
You can retrieves and displays the file paths for each configuration of |
|
|
the "BrentLab/hackett_2020" dataset from Hugging Face Hub. |
|
|
|
|
|
```python |
|
|
from huggingface_hub import ModelCard |
|
|
from pprint import pprint |
|
|
|
|
|
card = ModelCard.load("BrentLab/hackett_2020", repo_type="dataset") |
|
|
|
|
|
# cast to dict |
|
|
card_dict = card.data.to_dict() |
|
|
|
|
|
# Get partition information |
|
|
dataset_paths_dict = {d.get("config_name"): d.get("data_files")[0].get("path") for d in card_dict.get("configs")} |
|
|
|
|
|
pprint(dataset_paths_dict) |
|
|
``` |
|
|
|
|
|
If you wish to pull the entire repo, due to its size you may need to use an |
|
|
[authentication token](https://huggingface.co/docs/hub/en/security-tokens). |
|
|
If you do not have one, try omitting the token related code below and see if |
|
|
it works. Else, create a token and provide it like so: |
|
|
|
|
|
```python |
|
|
from huggingface_hub import snapshot_download |
|
|
import duckdb |
|
|
import os |
|
|
|
|
|
repo_id = "BrentLab/hackett_2020" |
|
|
|
|
|
hf_token = os.getenv("HF_TOKEN") |
|
|
|
|
|
# Download entire repo to local directory |
|
|
repo_path = snapshot_download( |
|
|
repo_id=repo_id, |
|
|
repo_type="dataset", |
|
|
token=hf_token |
|
|
) |
|
|
|
|
|
print(f"\n✓ Repository downloaded to: {repo_path}") |
|
|
|
|
|
# Construct path to the hackett_2020 parquet file |
|
|
parquet_path = os.path.join(repo_path, "hackett_2020.parquet") |
|
|
print(f"✓ Parquet file at: {parquet_path}") |
|
|
``` |
|
|
|
|
|
Use your favorite method of interacting with `parquet` files (eg duckDB, but you could |
|
|
use dplyr in R or pandas, too). |
|
|
|
|
|
```python |
|
|
# Connect to DuckDB and query the parquet file |
|
|
conn = duckdb.connect() |
|
|
|
|
|
query = """ |
|
|
SELECT DISTINCT time, mechanism, restriction, date |
|
|
FROM read_parquet(?) |
|
|
WHERE regulator_symbol = 'ACA1' |
|
|
""" |
|
|
result = conn.execute(query, [parquet_path]).df() |
|
|
print(f"Found {result}") |
|
|
``` |
|
|
|
