--- license: mit language: - en tags: - genomics - yeast - transcription - perturbation - response - knockout - TFKO pretty_name: "Kemmeren, 2014 Overexpression" size_categories: - 1M- Transcriptional regulator overexpression perturbation data with differential expression measurements dataset_type: annotated_features default: true metadata_fields: ["regulator_locus_tag", "regulator_symbol"] data_files: - split: train path: kemmeren_2014.parquet dataset_info: features: - name: sample_id dtype: integer description: >- unique identifier for a specific sample. The sample ID identifies a unique regulator. - 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 loci that were originally parsed incorrectly. - 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: reporterId dtype: string description: probe ID as reported from the original data - 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: M dtype: float64 description: log₂ fold change (mutant vs wildtype) role: quantitative_measure - name: Madj dtype: float64 description: >- M value with the cell cycle signal removed (see paper cited in the introduction above) role: quantitative_measure - name: A dtype: float64 description: >- average log2 intensity of the two channels, a proxy for expression level (This is a guess based on microarray convention -- not specified on holstege site) role: quantitative_measure - name: pval dtype: float64 description: significance of the modeled effect (M), from limma role: quantitative_measure - name: variable_in_wt dtype: string description: >- True if the given locus is variable in the WT condition. Recommended to remove these from analysis. False otherwise. See Holstege website for more information role: experimental_condition - name: multiple_probes dtype: string description: >- True if there is more than one probe associated with the same genomic locus. False otherwise role: experimental_condition - name: kemmeren_regulator dtype: string description: >- True if the regulator is one of the regulators studied in the original Kemmeren et al. (2014) global regulator study. False otherwise role: experimental_condition - name: regulator_desc dtype: string description: >- functional description of the induced regulator from the original paper supplement role: experimental_condition - name: functional_category dtype: string description: functional classification of the regulator from the original paper supplement role: experimental_condition - name: slides dtype: string description: identifier(s) for the microarray slide(s) used in this experiment role: experimental_condition - name: mating_type dtype: string description: mating type of the strain background used in the experiment role: experimental_condition - name: source_of_deletion_mutants dtype: string description: origin of the strain role: experimental_condition - name: primary_hybsets dtype: string description: identifier for the primary hybridization set to which this sample belongs role: experimental_condition - name: responsive_non_responsive dtype: string description: >- classification of the regulator as responsive or not to the deletion from the original paper supplement role: experimental_condition - name: nr_sign_changes dtype: integer description: >- number of significant changes in expression detected for the regulator locus tag (abs(M) > log2(1.7) & pval < 0.05). Note that there is a slight difference when calculating from the data provided here, I believe due to a difference in the way the targets are parsed and filtered (some ORFs that have since been removed from the annotations are removed). I didn't investigate this closely, though. role: experimental_condition - name: profile_first_published dtype: string description: citation or reference indicating where this expression profile was first published role: experimental_condition - name: chase_notes dtype: string description: notes added during data curation and parsing --- # Kemmeren 2014 This Dataset is a parsed version of the data provided by the [Holstege lab](https://deleteome.holstegelab.nl/). Both the original mutant/wt effect (`M`), and the effect after removal of an inferred cell cycle signal (`Madj`), are provided in the data. The paper describing the entire data set is: [Kemmeren P, Sameith K, van de Pasch LA, Benschop JJ, Lenstra TL, Margaritis T, O'Duibhir E, Apweiler E, van Wageningen S, Ko CW, van Heesch S, Kashani MM, Ampatziadis-Michailidis G, Brok MO, Brabers NA, Miles AJ, Bouwmeester D, van Hooff SR, van Bakel H, Sluiters E, Bakker LV, Snel B, Lijnzaad P, van Leenen D, Groot Koerkamp MJ, Holstege FC. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell. 2014 Apr 24;157(3):740-52. doi: 10.1016/j.cell.2014.02.054. PMID: 24766815.](https://doi.org/10.1016/j.cell.2014.02.054) And the paper describing the removal of the cell cycle effect is: [O'Duibhir E, Lijnzaad P, Benschop JJ, Lenstra TL, van Leenen D, Groot Koerkamp MJ, Margaritis T, Brok MO, Kemmeren P, Holstege FC. Cell cycle population effects in perturbation studies. Mol Syst Biol. 2014 Jun 21;10(6):732. doi: 10.15252/msb.20145172. PMID: 24952590; PMCID: PMC4265054.](https://doi.org/10.15252/msb.20145172) This repo provides 1 dataset: - **kemmeren_2014**: Transcriptional regulator overexpression perturbation data with differential expression measurements. ## 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/kemmeren_2014" dataset from Hugging Face Hub. ```python from huggingface_hub import ModelCard from pprint import pprint card = ModelCard.load("BrentLab/kemmeren_2014", 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/kemmeren_2014" 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 kemmeren_2014 parquet file parquet_path = os.path.join(repo_path, "kemmeren_2014.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 * FROM read_parquet(?) WHERE regulator_locus_tag = 'YJR060W' """ result = conn.execute(query, [parquet_path]).fetchall() print(f"Found {len(result)} rows for YJR060W") ``` **NOTE:** There are some loci with multiple probes (`multiple_probes == TRUE`). One strategy to deduplicate these is to take the max `M`, `Madj` and min `pval`.