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