Update README.md

README.md

@@ -536,10 +536,12 @@ 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".
+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)
+
 ## Usage
 
 You may access just the Dataset metadata like this:
@@ -559,44 +561,67 @@ card_dict.get("dataset_info").get("partitioning").get("keys")
 Output:
 
 ```raw
-
+[{'name': 'regulator_symbol',
+  'dtype': 'string',
+  'levels': ['YER045C',
+   'YLR131C',
+   'YDR448W',
+   'YDR216W',
+   'YGL071W',
+   'YPL202C',
+   ...]},
+ {'name': 'time',
+  'dtype': 'string',
+  'levels': [0,
+   2,
+   5,
+   7,
+   ...]},
+ {'name': 'mechanism', 'dtype': 'string', 'levels': ['GEV', 'ZEV']},
+ {'name': 'restriction', 'dtype': 'string', 'levels': ['M', 'N', 'P']},
+ {'name': 'date',
+  'dtype': 'string',
+  'levels': [20150101,
+   20150616,
+   20150903,
+   ...]},
+ {'name': 'strain',
+  'dtype': 'string',
+  'levels': ['SMY10',
+   'SMY104',
+   'SMY108',
+   'SMY108n',
+   ...]}]
 ```
 
-You can use this information to pull only the partition you're interested in, eg
+You can use this information to pull only the partition(s) you're interested in, eg
 
 ```python
-from huggingface_hub import hf_hub_download
+from pathlib import Path
+from huggingface_hub import snapshot_download
+import pyarrow.dataset as ds
 
-# Download a single partition file and store it in the local cache
-local_file = hf_hub_download(
-    repo_id="BrentLab/harbison_2004",
+# pull all data for regulator `YAL051W`
+root = snapshot_download(
+    repo_id="BrentLab/hackett_2020",
     repo_type="dataset",
-    filename="data/condition=YPD/regulator_locus_tag=YER045C/part-0.parquet"
+    allow_patterns=[
+        "data/regulator_locus_tag=YAL051W/*/*/*/*/*/part-0.parquet"
+    ],
 )
+
+base = Path(root) / "data"  # the dataset root directory
+dataset = ds.dataset(base, format="parquet", partitioning="hive")
+print(dataset.schema)
 ```
 
 <details>
 <summary><strong>Dataset Details</strong></summary>
 
-The data was extract from The [Young Lab's website](http://younglab.wi.mit.edu/regulatory_code/GWLD.html).
-
-I pulled the 'Excel' versions of both the P values (the downloaded file is called files_for_paper_abbr.zip)
-and Binding ratios (Ratio_forpaper_abbr.zip).  
-
-The following is copied from the [Young's lab explanation of the analysis](http://younglab.wi.mit.edu/regulatory_code/Analysis.html):  
-
-> The microarrays were scanned using an Axon200B scanner, and the images were analyzed with Genepix 5.0. Columns corresponding to the background
-> subtracted intensities and standard deviation of the background were extracted for further analysis. The intensities for the two channels, representing
-> the immunoprecipitated (test) and unenriched (control) samples, were normalized by using the median of each channel to calculate a normalization factor,
-> normalizing all datasets to a single median intensity. The log ratio of the intensity in the test channel to the control channel was calculated.
-> To account for biases in the immunoprecipitation reaction, these log ratios were normalized for each spot by subtracting the average log ratio of each
-> spot across all arrays. The intensities in the test channel were then adjusted to yield this normalized ratio. Finally, an error model was used to
-> calculate significance of enrichment on each chip and to combine data for replicates to obtain a final average ratio and significance of enrichment
-> for each intergenic region. Each intergenic region was assigned to the genes it is most likely to regulate.
-> We have included new refinements in our analysis relative to that used in Lee et al. Notably, we have excluded artefactual spots from analysis,
-> selected more reliable probes for normalization and assigned quality metrics to individual arrays to identify low quality experiments.
+The data was extract from the [Calico website](https://idea.research.calicolabs.com/data).
 
-The script used to parse this data from the data provided by the Young lab into the parquet dataset presented here is included in `scripts/`
+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/`)
 
 </details>
 
@@ -605,16 +630,27 @@ The script used to parse this data from the data provided by the Young lab into
 
 ### data/
 
-This is a **Parquet** dataset where the partitions are based on `condition` and `regulator_locus_tag`.
-Each row represents the effect and pvalue on a given target gene (`target_locus_tag`)
+This is a **Parquet** dataset where the partitions are based on `regulator_locus_tag`, `time`, `mechanism`, `restriction`, `date`, `strain`.
+This means that each individual parquet file represents a single experiment. Each record provides data on the effect of the induction of
+a transcriptional regulator.
 
-| Field                 | Description                                                                                                                                                                                      |
-|-----------------------|-------------------------------------------------------------------------------------------------------------------------|
-| `condition`           | See below for a definition of each level                                                                                |
-| `regulator_locus_tag` | The systematic ID of the ChIP'ed regulator. See hf/BrentLab/yeast_genome_resources to map to the common name (symbol)   |
-| `target_locus_tag`    | the systematic ID of the feature to which the Young lab assigned the effect/pvalue                                      |
-| `effect`              | chip based binding ratio                                                                                                |
-| `pvalue`              | pvalue of the effect                                                                                                    |
+| Field                       | Description                                                                                                                                                                                                 |
+|-----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `regulator_locus_tag`       | induced transcriptional regulator                                                                                                                                                         |
+| `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                                                                                                                                                           |
+| `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.**                                                       |
 
 </details>