app.py

import streamlit as st
import re
import numpy as np
import pandas as pd
import sklearn
import xgboost
import shap
st.set_option('deprecation.showPyplotGlobalUse', False)

# CSS to inject contained in a string
hide_table_row_index = """
            <style>
            thead tr th:first-child {display:none}
            tbody th {display:none}
            </style>
            """

# Inject CSS with Markdown
st.markdown(hide_table_row_index, unsafe_allow_html=True)

seed=42

annotations = pd.read_csv("annotations_dataset.csv")
annotations = annotations.set_index("Gene")

training_data = pd.read_csv("./selected_features_training_data.csv", header=0)
training_data.columns = [
    regex.sub("_", col) if any(x in str(col) for x in set(("[", "]", "<"))) else col
    for col in training_data.columns.values
]

training_data["BPlabel_encoded"] = training_data["BPlabel"].map(
    {"most likely": 1, "probable": 0.75, "least likely": 0.1}
)
Y = training_data["BPlabel_encoded"]
X = training_data.drop(columns=["BPlabel_encoded","BPlabel"])
xgb = xgboost.XGBRegressor(
    n_estimators=40,
    learning_rate=0.2,
    max_depth=4,
    reg_alpha=1,
    reg_lambda=1,
    random_state=seed,
    objective="reg:squarederror")


xgb.fit(X, Y)
prediction_list = list(xgb.predict(annotations))
predictions = [round(prediction, 2) for prediction in prediction_list]

output = pd.Series(data=predictions, index=annotations.index, name="XGB_Score")
df_total = pd.concat([annotations, output], axis=1)


df_total = df_total[['XGB_Score', 'mousescore_Exomiser',
 'SDI', 'Liver_GTExTPM',  'pLI_ExAC',
 'HIPred',
 'Cells - EBV-transformed lymphocytes_GTExTPM',
 'Pituitary_GTExTPM',
 'IPA_BP_annotation']]


st.title('Blood Pressure Gene Prioritisation Post-GWAS')
st.markdown("""
A machine learning pipeline for predicting disease-causing genes post-genome-wide association study in blood pressure.
""")


collect_genes = lambda x : [str(i) for i in re.split(",|,\s+|\s+", x) if i != ""]

input_gene_list = st.text_input("Input list of HGNC genes (enter comma separated):")
gene_list = collect_genes(input_gene_list)
explainer = shap.TreeExplainer(xgb)

@st.experimental_memo
def convert_df(df):
   return df.to_csv(index=False).encode('utf-8')

if len(gene_list) > 1:
    df = df_total[df_total.index.isin(gene_list)]
    df['Gene'] = df.index
    df.reset_index(drop=True, inplace=True)
    df = df[['Gene','XGB_Score', 'mousescore_Exomiser',
 'SDI', 'Liver_GTExTPM',  'pLI_ExAC',
 'HIPred',
 'Cells - EBV-transformed lymphocytes_GTExTPM',
 'Pituitary_GTExTPM',
 'IPA_BP_annotation']]
    st.dataframe(df)
    output = df[['Gene', 'XGB_Score']]
    csv = convert_df(output)
    st.download_button(
       "Download Gene Prioritisation",
       csv,
       "bp_gene_prioritisation.csv",
       "text/csv",
       key='download-csv'
    )
    df_shap = df_total[df_total.index.isin(gene_list)]
    df_shap.drop(columns='XGB_Score', inplace=True)
    shap_values = explainer.shap_values(df_shap)
    summary_plot = shap.summary_plot(shap_values, df_shap, show=False)
    st.caption("SHAP Summary Plot of All Input Genes")
    st.pyplot(fig=summary_plot)

else:
    pass


input_gene = st.text_input("Input individual HGNC gene:")
df2 = df_total[df_total.index == input_gene]
df2_shap = df_total[df_total.index == input_gene]
df2['Gene'] = df2.index
st.dataframe(df2)
df2.drop(columns='XGB_Score', inplace=True)

if input_gene:
    shap_values = explainer.shap_values(df2_shap)
    shap.getjs()
    force_plot = shap.force_plot(
    explainer.expected_value,
    shap_values,
    df2_shap , 
    matplotlib = True,show=False)
    st.pyplot(fig=force_plot)
else:
    pass

st.markdown("""
Total Gene Prioritisation Results:
""")

df_total['Gene'] = df_total.index
st.dataframe(df_total)