Datasets:
metadata
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
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
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
- 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 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.
Accessing Data
The examples below require
labretriever
(pip install labretriever) and/or the
HuggingFace Hub client
(pip install huggingface_hub).
Accessing Data with labretriever
This repository is part of a collection configured as a unified database using labretriever.VirtualDB. Download the collection config and use it to query the data directly in Python, or with an AI assistant using the labretriever plugin.
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:
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:
# install.packages("arrow")
arrow::read_parquet("hackett_2020.parquet")