Update README.md

README.md

@@ -1 +1,105 @@
-Hello
+---
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+pipeline_tag: tabular-classification
+tags:
+- medical
+- biology
+- code
+---
+# HCC TIIC Random Forest Model
+**Developed by:** Yifu (Evan) Zuo
+
+This is a Random Forest classifier for automatically classifying tumor-infiltrating immune cells in hepatocellular carcinoma tumor microenvironments in 40 categories based on expression data from 107 CD45+ genes.
+
+## How to use it
+
+#### 1. Download the model from Files
+This is pretty straight forward. Head to the Files tab of this repository and download the model. The size of the RF model in pickle format is 2.1G.
+
+#### 2. Create a New Interactive Python Notebook
+Open Jupyter Notebook or Google Colab, and create a new notebook file. This environment will allow you to interactively run Python commands and visualize outputs step-by-step.
+
+#### 3. Import Required Libraries
+Start by importing the required libraries in your notebook. This includes:
+```
+import joblib
+import pandas as pd
+from sklearn.impute import SimpleImputer
+import matplotlib.pyplot as plt
+```
+
+These libraries are needed to load the model, handle the data, and create visualizations.
+
+#### 4. Load the Downloaded Model
+Use the following command to load the model into your notebook:
+```
+loaded_rf_model = joblib.load('path_to_downloaded_model.pkl')
+```
+Replace `'path_to_downloaded_model.pkl'` with the actual file path of the downloaded model.
+#### 5. Load the Data in CSV Format
+Load the Data in CSV Format:
+`data = pd.read_csv('path_to_csv_file.csv')`
+
+•	Each row should represent a cell.
+
+•	Each column should represent a gene.
+
+•	The required genes must be present in the data (Check Step 9 to see the full list).
+
+Before loading the data in CSV format, make sure the UMI counts for each gene is normalized. The UMI counts should be scaled to 10,000 as standard practice. R and Seurat are recommended for the conversion to CSV.
+
+#### 7. Preprocess the Data for Model Compatibility
+Prepare the data before feeding it to the model.
+
+•	Replace hyphens in column names with dots:
+```
+data.columns = data.columns.str.replace('-', '.')
+```
+•	Drop irrelevant rows and columns:
+```
+# Rename columns based on the mapping dictionary
+data.rename(columns=feature_mapping, inplace=True))
+```
+Ensure that the feature mapping is correctly defined in your code.
+
+#### 9. Select the Required Features for Prediction
+Define the list of genes to be used by the model:
+```
+selected_features = ['CD3D', 'CD3E', 'CD3G', 'CCR7', 'LEF1', 'SELL', 'TCF7', 'S1PR1', 'ANXA1', 'ANXA2', 
+                     'IL7R', 'CD74', 'TYROBP', 'CD4', 'HAVCR2', 'PDCD1', 'GZMB', 'ITGAE', 'CXCL13', 'FOXP3', 
+                     'CTLA4', 'IL2RA', 'MKI67', 'STMN1', 'CMC1', 'CD8A', 'CD8B', 'CX3CR1', 'KLRG1', 'FCGR3A', 
+                     'FGFBP2', 'GZMH', 'GZMK', 'CCL4', 'CCL5', 'NKG7', 'KLRD1', 'KLRF1', 'GNLY', 'IL32', 
+                     'SLC4A10', 'KLRB1', 'ZBTB16', 'NCR3', 'NCAM1', 'CCL3', 'IFNG', 'CD69', 'HSPA1A', 
+                     'XCL1', 'AREG', 'CD160', 'TIGIT', 'CXCR4', 'ZNF331', 'DNAJB1', 'HSPA1B', 'HSPA6', 
+                     'TUBB', 'CST3', 'LYZ', 'CD14', 'VCAN', 'S100A9', 'RNASE2', 'S100A12', 'FCER1G', 'LST1', 
+                     'AIF1', 'IFITM3', 'CD1C', 'FCER1A', 'CLEC10A', 'VEGFA', 'IRF4', 'RGS2', 'CLEC9A', 
+                     'IRF8', 'IDO1', 'CLNK', 'XCR1', 'LAMP3', 'CD274', 'LTB', 'CCL19', 'CCL21', 'CD68', 
+                     'THBS1', 'S100A8', 'CD163', 'SIGLEC1', 'C1QA', 'SLC40A1', 'GPNMB', 'APOE', 'SAT1', 
+                     'HLA.DQB1', 'S100A4', 'HLA.DRA', 'HLA.DQA1', 'MARCO', 'CD79A', 'CPA3', 'KIT', 'CD19', 
+                     'MS4A1', 'CD22']
+X_test_data = data[selected_features]
+```
+#### 10. Handle Missing Values in the Data
+Replace missing values (NaN) with the mean of each column using SimpleImputer:
+```
+imputer = SimpleImputer(strategy='mean')
+X_test_data = imputer.fit_transform(X_test_data)
+```
+#### 11. Make Predictions with the Loaded Model
+Use the model to make predictions:
+```
+predictions = loaded_rf_model.predict(X_test_data)
+```
+##### 12. Add Predictions to the Data and Display the Updated Data
+```
+data['label'] = predictions
+print(data.head())
+plt.figure(figsize=(10, 4))
+plt.title('Predicted Cell Type Distribution')
+data['label'].value_counts().plot.bar(rot=0)
+plt.show()
+```