use load_datasets

README.md

@@ -31,8 +31,43 @@ meta files contain the following:
 - Original.ID: Most descriptive UBERON ID
 - Original.Name: Tissue name for original ID
 
-## Quick start  
-If using our model:
+## Quick start 
+```
+# Load the dataset using the Hugging Face datasets library
+from datasets import load_dataset
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+training_mv = load_dataset(
+    "parquet",
+    data_files="https://huggingface.co/datasets/ylab/methyl-classification/resolve/main/training_mvalues.parquet"
+).to_pandas().set_index('Sample')
+training_meta = load_dataset(
+    "parquet",
+    data_files="https://huggingface.co/datasets/ylab/methyl-classification/resolve/main/training_meta.parquet"
+).to_pandas().set_index('Sample')
+
+labtransfer_mv = load_dataset(
+    "parquet",
+    data_files="https://huggingface.co/datasets/ylab/methyl-classification/resolve/main/labtransfer_mvalues.parquet"
+).to_pandas().set_index('Sample')
+labtransfer_meta = load_dataset(
+    "parquet",
+    data_files="https://huggingface.co/datasets/ylab/methyl-classification/resolve/main/labtransfer_meta.parquet"
+).to_pandas().set_index('Sample')
+
+# Describe tissue distribution
+print(training_meta.describe())
+
+# Plot mvalue density plots for first five samples
+sns.kdeplot(data=training_mv.iloc[:5].T, common_norm=False)
+plt.xlabel("Methylation Value")
+plt.ylabel("Density")
+plt.title("Methylation Density for 5 Samples")
+plt.show()
+
+```
+<!-- If using our model:
 ```
 git clone https://github.com/ylaboratory/methylation-classification.git
 cd methylation-classification
@@ -48,7 +83,7 @@ If using only our data:
 mkdir methyl-classification
 cd download
 huggingface-cli download ylab/methyl-classification
-```
+``` -->
 
 ## Citation Information