--- license: mit language: - en tags: - genomics - yeast - transcription - perturbation - response - overexpression pretty_name: Hackett, 2020 Overexpression size_categories: - 1M- 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") ```