improving clarity of readme

README.md

@@ -1,40 +1,83 @@
 ---
 language:
-  - en 
+- en
 homepage: https://github.com/ylaboratory/methylation-classification
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+tags:
+- biology
+- bioinformatics
+- biomedical
+- DNA-methylation
+- multi-label-classification
+pretty_name: 450k DNA methylation tissue classification
+size_categories:
+- 10K<n<100K
 ---
 
 
-# Methylation-classification Dataset
+# DNA Methylation Tissue Classification Dataset
 
-## Dataset Description
+## Dataset Summary
 
 - **Homepage:** https://github.com/ylaboratory/methylation-classification
 - **Pubmed:** False
 - **Public:** True
 
-Methylation classification is a pan-tissue dataset and analysis paper from publicaly available DNA methylation microarrays (450K) from the Gene Expression Omnibus. The uploaded dataset of 16,959 samples encompasss 86 tissue and cell types.
+
+This data resource is vast, curated reference atlas of DNA methylation (DNAm) profiles
+spanning 16,959 *healthy* primary human tissue and cell samples profiled on [Illumina 450K arrays](https://www.illumina.com/documents/products/datasheets/datasheet_humanmethylation450.pdf).
+Samples cover 86 unique tissues and cell types and are manually mapped to a common set of terms in the [UBERON anatomical ontology](https://www.ebi.ac.uk/ols4/ontologies/uberon).
+This dataset is intended to be used as a baseline resource for multi-label classification in
+the biomedical domain, particuarly for tissue/cell‑type classification, deconvolution, and epigenetic biomarker discovery.
+
+> **Key stats:**
+> **16,959** total DNAm samples from **210** studies in the [Gene Expression Omnibus](https://www.ncbi.nlm.nih.gov/geo/) (GEO)
+> **86** tissue/cell types (55 in training set, 31 in holdout)
+> **297,598** quality controlled CpG sites (M-values) per sample
+> **10,351** samples used for training (>= 2 studies per label)
+> **6,608** samples reserved in holdout set to evalulate generalization/label transfer
 
 ## Data and usage
 
-Analysis code, annotation, data files, and skeleton preprocessing code (if running starting from raw files) are located in this repo and [github](https://github.com/ylaboratory/methylation-classification).  
-  
-mvalues files contain methylation M-values preprocessed and quality controlled using minfi and wateRmelon:  
-- background corrected using [preprocessNoob](https://rdrr.io/bioc/minfi/man/preprocessNoob.html)
-- normalized using [BMIQ](https://rdrr.io/bioc/wateRmelon/man/BMIQ.html)
+The dataset itself is divided into two sets, one used for training and cross-validation, and a separate holdout set used to for evaluation on unseen labels.
+For faster loading and improved the files are stored as [parquet files](https://parquet.apache.org).
+
+For each partition there are two main file types: 
+- `_mvalues`: containing preprocessed and quality controlled DNAm M-values background corrected using [preprocessNoob](https://rdrr.io/bioc/minfi/man/preprocessNoob.html) and normalized using [BMIQ](https://rdrr.io/bioc/wateRmelon/man/BMIQ.html).
+- '_meta': metadata files containing the sample id, dataset, and UBERON tissue/cell identifiers and labels. There are two columns corresponding to UBERON identifiers, one contains the most descriptive tissue or cell term, and the second contains a more general term used to create a larger training compendium. (e.g., `pericardial fat` vs. `visceral fat`). 
+
+The full list of files include:
+- `full_ontology.edgelist`: a [networkx](https://networkx.org/documentation/stable/reference/readwrite/edgelist.html) file containing the tissue/cell ontology connecting all 86 tissue and cell terms
+- `training_ontology.edgelist`: a [networkx](https://networkx.org/documentation/stable/reference/readwrite/edgelist.html) file containing the tissue/cell ontology connecting the 55 tissue and cell terms
+- `training_meta.parquet`: metadata for the samples in the training partition
+- `label_transfer_meta.parquet`: metadata for the samples in the holdout partition
+- `training_mvalues.parquet`: m-values measuring DNAm at 297,598 CpG sites across the genome for samples in the training partition
+- `label_transfer_mvalues.parquet`: m-values measuring DNAm at 297,598 CpG sites across the genome for samples in the holdout partition
 
-meta files contain the following:  
-- Sample ID
-- training.ID: UBERON ID used for training
-- training.Name: Tissue name for training ID
-- Dataset: GSE study ID
-- Original.ID: Most descriptive UBERON ID
-- Original.Name: Tissue name for original ID
+The columns in metadata files:  
+- `Sample ID`: sample identifier in GEO (GSM ID)
+- `training.ID`: standardized UBERON ID used for training
+- `training.Name`: corresponding tissue/cell name for the training ID
+- `Dataset`: dataset identifier in GEO (GSE ID)
+- `Original.ID`: manually annotated most descriptive UBERON ID
+- `Original.Name`: correpsonding tissue/cell name for original ID
+
+Mvalue files are structured probe (rows) by samples (columns). Columns are labeled with GSM identifiers
+with one additional column for probe which contains Illumina CpG IDs (e.g., `cg03128332`).
 
 ## Quick start 
+
+Using python with the `huggingface_hub` and `pyarrow` packages, and the optional `pandas` and `networkx` packages
+installed we can quickly get started with this dataset.
+
 ```
-# Load the dataset using the Hugging Face datasets library
 from datasets import load_dataset
+import pyarrow.parquet as pq
+
+import pandas as pd
+import networkx as nx
 import seaborn as sns
 import matplotlib.pyplot as plt
 
@@ -56,10 +99,10 @@ labtransfer_meta = load_dataset(
     data_files="https://huggingface.co/datasets/ylab/methyl-classification/resolve/main/labtransfer_meta.parquet"
 ).to_pandas().set_index('Sample')
 
-# Describe tissue distribution
+# View the training set metadata
 print(training_meta.describe())
 
-# Plot mvalue density plots for first five samples
+# Plot m-value density plots for first five samples
 sns.kdeplot(data=training_mv.iloc[:5].T, common_norm=False)
 plt.xlabel("Methylation Value")
 plt.ylabel("Density")
@@ -67,6 +110,7 @@ plt.title("Methylation Density for 5 Samples")
 plt.show()
 
 ```
+
 <!-- If using our model:
 ```
 git clone https://github.com/ylaboratory/methylation-classification.git
@@ -85,9 +129,28 @@ cd download
 huggingface-cli download ylab/methyl-classification
 ``` -->
 
-## Citation Information
+## Code for data processing, analysis, and tissue classification
+
+This dataset, while designed to be standalone, was generated as a part of a larger paper predicting tissue and cell type.
+The code for processing the raw data files and conducting the analysis in that paper can
+be found on the project [Github](https://github.com/ylaboratory/methylation-classification).  
+
+
+## Citation
+
+If you use this dataset in your work, please cite:
+> Kim et al., “Ontology‑aware DNA methylation classification with a curated atlas of human tissues and cell types”, \[bioRxiv preprint\] (2025).
 
 ```
-coming soon
+@article{kim2024methylation_atlas,
+  title   = {Ontology-aware DNA methylation classification with a curated atlas of human tissues and cell types},
+  author  = {Kim, Mirae and Dannenfelser, Ruth and Cui, Yufei and Allen, Genevera and Yao, Vicky},
+  journal = {bioRxiv preprint},
+  year    = {2025},
+  doi     = {10.1101/2024.XX.XXXXXX}
+}
 ```
 
+## License
+
+This dataset is released under CC BY 4.0, permitting both academic and commercial use with attribution.