Update README.md

README.md

@@ -81,68 +81,89 @@ configs:
 ---
 # 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.
+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)
+[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)
 
-## Dataset Details
+This repo provides 1 dataset:
 
-The data was extract from the [Calico website](https://idea.research.calicolabs.com/data).
+- **hackett_2020**: TF overexpression data from Hackett 2020.
 
-I pulled the 'Raw & processed gene expression data' versions and did some minimal parsing to
-save the data as a partitioned parquet dataset (see `scripts/`)
+## Usage
 
-### Dataset Structure
+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.
 
-The data is stored in a single parquet file which has the following fields. 
+### `tfbpapi`
 
-| Field                       | Description                                                                                                                                                                                                 |
-|-----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `regulator_locus_tag`       | induced transcriptional regulator systematic ID                                                                                                                |
-| `regulator_symbol`          | induced transcriptional regulator common name. If no common name exists, then the `regulator_locus_tag` is used.                                               |
-| `target_locus_tag`          | Systmatic ID of the feature to which the induced transcriptional regulator's affect is ascribed                                                                |
-| `target_symbol`             | Common name of feature to which the induced transcriptional regulator's affect is ascribed. If there is no common name, the systematic ID is used.             |                                                                                                                                                       |
-| `time`                      | time point (minutes)                                                                                                                                                          |
-| `mechanism`                 | induction system (GEV or ZEV)                                                                                                                                                           |
-| `restriction`               | nutrient limitation (M, N or P)                                                                                                                                                          |
-| `date`                      | date performed                                                                                                                                                           |
-| `strain`                    | strain name                                                                                                                                                           |
-| `green_median`              | Median of green (reference) channel fluorescence                                                                                                                                                           |
-| `red_median`                | Median of red (experimental) channel fluorescence                                                                                                                                                          |
-| `log2_ratio`                | log2(red / green) subtracting value at time zero                                                                                                                                                           |
-| `log2_cleaned_ratio`        | Non-specific stress response and prominent outliers removed                                                                                                                                                |
-| `log2_noise_model`          | Estimated noise standard deviation                                                                                                                                                                         |
-| `log2_cleaned_ratio_zth2d`  | Cleaned timecourses hard-thresholded based on multiple observations (or last observation) passing the noise model                                                                                           |
-| `log2_selected_timecourses` | Cleaned timecourses hard-thresholded based on single observations passing noise model and impulse evaluation of biological feasibility                                                                     |
-| `log2_shrunken_timecourses` | Selected timecourses with observation-level shrinkage based on local FDR (false discovery rate). **Most users of the data will want to use this column.**                                                       |
+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
 
-## Usage
+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)
+```
 
-I recommend using `huggingface_hub.snapshot_download` to pull the repository. After that, use your favorite
-method of interacting with `parquet` files (eg duckDB, but you could use dplyr in R or pandas, too).
+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"
+    repo_type="dataset",
+    token=hf_token
 )
 
-print(f"Repository downloaded to: {repo_path}")
+print(f"\n✓ Repository downloaded to: {repo_path}")
 
-# Construct path to the parquet file
+# 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}")
+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()
 
@@ -153,7 +174,4 @@ WHERE regulator_symbol = 'ACA1'
 """
 result = conn.execute(query, [parquet_path]).df()
 print(f"Found {result}")
-```
-
-
-**Dataset Author and Contact**: Chase Mateusiak [@cmatKhan](https://github.com/cmatkhan/)
+```