update_readme_paperlink

README.md

@@ -14,37 +14,9 @@ pretty_name: "Kemmeren, 2014 Overexpression"
 size_categories:
 - 1M<n<10M
 
-experimental_conditions:
-  temperature_celsius: 30
-  cultivation_method: plate
-  growth_phase_at_harvest:
-    phase: "early_mid_log"
-    od600: 0.6
-    od600_tolerance: 0.1
-  media:
-    name: synthetic_complete
-    carbon_source:
-      - compound: D-glucose
-        # Kemmeren et al 2014: 2% D-glucose
-        concentration_percent: 2
-    nitrogen_source:
-      - compound: yeast_nitrogen_base
-        # Kemmeren et al 2014: 6.71 g/l
-        concentration_percent: 0.671
-        specifications:
-          - without_amino_acids
-          - without_carbohydrate
-          - with_ammonium_sulfate
-      - compound: amino_acid_dropout_mix
-        # Kemmeren et al 2014: 2.0 g/l
-        concentration_percent: 0.2
-doi: https://doi.org/10.1016/j.cell.2014.02.054
-citation: >-
-  Kemmeren, P, Sameith, K, van de Pasch, LA, Benschop, JJ, Lenstra, TL, Margaritis, T, et al. 2014. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell, 157: 740-752
 configs:
 - config_name: kemmeren_2014
-  description: >-
-    Microarray expression data comparing strains in which the gene encoding a single protein has been deleted. Includes sequence-specific DNA-binding TFs as well kinases, phosphatases, and many other potential regulatory proteins. Cells were grown on synthetic complete medium with glucose.
+  description: Transcriptional regulator overexpression perturbation data with differential expression measurements
   dataset_type: annotated_features
   default: true
   metadata_fields: ["regulator_locus_tag", "regulator_symbol"]
@@ -53,183 +25,36 @@ configs:
     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
+      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.
+      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
+      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.
+      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)
+      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
-    - name: responsive
-      dtype: string
-      description: Per the paper(s), The authors consider a target responsive if Madj > 1.7 and pval < 0.05
-
-- config_name: control_2014
-  description: This stores the WT/background strains "WT-BY4743", "WT-MATA" and "WT-YPD" from Kemmeren 2014
-  dataset_type: annotated_features
-  metadata_fields: ["strain"]
-  data_files:
-  - split: train
-    path: control_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: strain
-      dtype: string
-      description: >-
-        The genotype or strain used in the experiment,
-        one of "WT-BY4743", "WT-MATA" and "WT-YPD"
-      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: A
+    - name: Madj
       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)
+      description: M value with the cell cycle signal removed (see paper cited in the introduction above)
       role: quantitative_measure
     - name: pval
       dtype: float64
@@ -237,41 +62,12 @@ configs:
       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
+      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
+      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: description
-      dtype: string
-      description: >-
-        functional description of the induced regulator
-        from the original paper supplement
-      role: experimental_condition
-    - name: mating_type
-      dtype: string
-      description: mating type of the strain background used in the experiment
-      role: experimental_condition
-    - name: chase_notes
-      dtype: string
-      description: notes added during data curation and parsing
-    - name: responsive
-      dtype: string
-      description: >-
-        Per the paper(s), The authors consider a target responsive
-        if M > 1.7 and pval < 0.05
 ---
 # Kemmeren 2014
 
@@ -295,75 +91,89 @@ 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)
 
-**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`.
+This repo provides 1 dataset:
 
-## Accessing Data
+- **kemmeren_2014**: Transcriptional regulator overexpression perturbation data with
+      differential expression measurements.
 
-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`).
+## Usage
 
-### Accessing Data with labretriever
+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.
 
-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).
+### `tfbpapi`
 
-```python
-from labretriever.virtual_db import VirtualDB
-from labretriever.datacard import DataCard
+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.
 
-# Citation and metadata
-card = DataCard("BrentLab/kemmeren_2014")
-print([c.config_name for c in card.configs])  # list available datasets
-info = card.info()
-print(info["doi"])
-print(info["citation"])
+### huggingface_cli/duckdb
 
-# path to the downloaded brentlab_yeast_collection.yaml
-vdb = VirtualDB("/path/to/brentlab_yeast_collection.yaml")
+You can retrieves and displays the file paths for each configuration of
+the "BrentLab/kemmeren_2014" dataset from Hugging Face Hub.
 
-print(vdb.get_dataset_description("kemmeren"))
-vdb.query("SELECT * FROM kemmeren LIMIT 5")
-```
+```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()
 
-### Direct parquet access
+# Get partition information
+dataset_paths_dict = {d.get("config_name"): d.get("data_files")[0].get("path") for d in card_dict.get("configs")}
 
-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.
+pprint(dataset_paths_dict)
+```
 
-Most files in this repository are single parquet files and can be read directly:
+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="BrentLab/kemmeren_2014",
+    repo_id=repo_id,
     repo_type="dataset",
-    allow_patterns="kemmeren_2014.parquet",
+    token=hf_token
 )
-conn = duckdb.connect()
-# returns a pandas DataFrame with the first 5 rows
-conn.execute(
-    "SELECT * FROM read_parquet(?) LIMIT 5",
-    [f"{repo_path}/kemmeren_2014.parquet"],
-).df()
+
+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}")
 ```
 
-### Accessing using R
+Use your favorite method of interacting with `parquet` files (eg duckDB, but you could
+use dplyr in R or pandas, too).
 
-Clone the repository and read parquet files directly with
-[arrow](https://arrow.apache.org/docs/r/):
+```python
+# Connect to DuckDB and query the parquet file
+conn = duckdb.connect()
 
-```r
-# install.packages("arrow")
-arrow::read_parquet("kemmeren_2014.parquet")
+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`.

control_2014.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:490665c992e5c586a914077e10578bec0a829f54425a1212b40021f174926133
-size 661123

kemmeren_2014.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e4003f0d49e17671ccf29f39e36b0e95091aba7988b3d3f6b371f2ecd1a4930f
-size 301348699
+oid sha256:3c463017444bd7690959c0d6fff0d0ebe2faa7b916dd9c44e305156f6c98b863
+size 319218929

kemmeren_2014.parquet.md5

@@ -0,0 +1 @@
+9109e2e853acab9fa341a1eebc1c649c  kemmeren_2014.parquet

scripts/parse_kemmeren_data.R

@@ -119,14 +119,9 @@ deleteome_all_mutants_controls_long = deleteome_all_mutants_controls %>%
   mutate(kemmeren_regulator = toupper(str_remove(tolower(kemmeren_regulator), "-del-1$|-del-mata$|-del$"))) %>%
   mutate(kemmeren_regulator = ifelse(kemmeren_regulator == "ARG5,6", "ARG56", kemmeren_regulator))
 
-kem_sup1_regulator_info = read_excel(here("data/kemmeren/supplemental_table1_strain_info.xlsx")) %>%
-  mutate(`profile first published` = str_replace(`profile first published`, ", ", ","))
-kem_sup1_regulator_info_straininfo = read_excel(here("data/kemmeren/supplemental_table1_strain_info_origins.xlsx"))
-
-kem_sup1_regulator_info = kem_sup1_regulator_info %>%
-  left_join(kem_sup1_regulator_info_straininfo) %>%
-  mutate(`profile first published` = citation) %>%
-  select(-citation)
+kem_sup1_regulator_info = read_excel(here("data/kemmeren/mmc1.xlsx")) %>%
+  # additional columns are not tabular
+  .[,1:9]
 
 parsed_regulators = deleteome_all_mutants_controls_long %>%
   select(kemmeren_regulator) %>%
@@ -184,8 +179,7 @@ regulators_munging_df = bind_rows(regulators_munging_list) %>%
                     regulator_symbol = symbol)) %>%
   replace_na(list(chase_notes = "none")) %>%
   mutate(regulator_locus_tag = ifelse(str_detect(kemmeren_regulator, "^WT-"), kemmeren_regulator, regulator_locus_tag),
-         regulator_symbol = ifelse(str_detect(kemmeren_regulator, "^WT-"), kemmeren_regulator, regulator_symbol)) %>%
-  janitor::clean_names()
+         regulator_symbol = ifelse(str_detect(kemmeren_regulator, "^WT-"), kemmeren_regulator, regulator_symbol))
 
 
 stopifnot(setequal(regulators_munging_df$kemmeren_regulator,
@@ -257,48 +251,20 @@ final_parsed_df = Reduce(left_join, final_parsed_list) %>%
   # duplicated to mulitple Madj. This removes those duplicates
   distinct(reporterId, .keep_all = TRUE) %>%
   ungroup() %>%
-  left_join(select(regulators_munging_df,
-                   -c(gene, `orf_name`))) %>%
-  dplyr::rename(nr_sign_changes = nr_sign_changes_p_0_05_fc_1_7,
-                primary_hybsets = primary_hybset_s,
-                source_of_deletion_mutants = source_of_deletion_mutant_s,
-                slides = slide_s,
-                regulator_desc = description) %>%
-  arrange(regulator_locus_tag)
-  # select(regulator_locus_tag, regulator_symbol, reporterId,
-  #        target_locus_tag, target_symbol, M, Madj, A, pval,
-  #        variable_in_wt, multiple_probes)
-
-db_kemmeren_meta = read_csv("data/kemmeren/db_kemmeren_meta_20251126.csv") %>%
-  mutate(id = ifelse(regulator_locus_tag == 'YLR352W', 0, id)) %>%
-  select(id, regulator_locus_tag) %>%
-  distinct() %>%
-  mutate(id = as.integer(id)) %>%
-  dplyr::rename(db_id = id)
+  left_join(select(regulators_munging_df, kemmeren_regulator,
+                   regulator_locus_tag, regulator_symbol)) %>%
+  select(regulator_locus_tag, regulator_symbol, reporterId,
+         target_locus_tag, target_symbol, M, Madj, A, pval,
+         variable_in_wt, multiple_probes)
 
-final_df_parsed_with_ids = final_parsed_df %>%
-  left_join(db_kemmeren_meta) %>%
-  replace_na(list(db_id = 0)) %>%
+final_parsed_df %>%
   arrange(regulator_locus_tag) %>%
-  group_by(regulator_locus_tag) %>%
-  mutate(sample_id = cur_group_id()) %>%
-  relocate(sample_id, db_id,
-           regulator_locus_tag, regulator_symbol,
-           reporterId, target_locus_tag, target_symbol,
-           M, Madj, A, pval,
-           variable_in_wt, multiple_probes)
-
-# note! verify before overwriting that the sample_id for the unique sample
-# tuple is the same as it is in the current hackett_2020, or that any changes
-# are intentional
-
-# final_df_parsed_with_ids %>%
-#   write_parquet("~/code/hf/kemmeren_2014/kemmeren_2014.parquet",
-#                 compression = "zstd",
-#                 chunk_size = 6181,
-#                 write_statistics = TRUE,
-#                 use_dictionary = c(
-#                   regulator_locus_tag = TRUE,
-#                   target_locus_tag = TRUE
-#                 )
-#   )
+  write_parquet(here("data/kemmeren/kemmeren_2014.parquet"),
+                compression = "zstd",
+                chunk_size = 6181,
+                write_statistics = TRUE,
+                use_dictionary = c(
+                  regulator_locus_tag = TRUE,
+                  target_locus_tag = TRUE
+                )
+  )