app.py

import streamlit as st
import re
import numpy as np
import pandas as pd
import sklearn
import xgboost
import shap
import plotly.tools as tls
import dash_core_components as dcc
import matplotlib
import plotly.graph_objs as go
try:
    import matplotlib.pyplot as pl
    from matplotlib.colors import LinearSegmentedColormap
    from matplotlib.ticker import MaxNLocator
except ImportError:
    pass

st.set_option('deprecation.showPyplotGlobalUse', False)

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')


cdict1 = {
    'red': ((0.0, 0.11764705882352941, 0.11764705882352941),
            (1.0, 0.9607843137254902, 0.9607843137254902)),

    'green': ((0.0, 0.5333333333333333, 0.5333333333333333),
              (1.0, 0.15294117647058825, 0.15294117647058825)),

    'blue': ((0.0, 0.8980392156862745, 0.8980392156862745),
             (1.0, 0.3411764705882353, 0.3411764705882353)),

    'alpha': ((0.0, 1, 1),
              (0.5, 1, 1),
              (1.0, 1, 1))
}  # #1E88E5 -> #ff0052
red_blue = LinearSegmentedColormap('RedBlue', cdict1)

def matplotlib_to_plotly(cmap, pl_entries):
    h = 1.0/(pl_entries-1)
    pl_colorscale = []

    for k in range(pl_entries):
        C = list(map(np.uint8, np.array(cmap(k*h)[:3])*255))
        pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])

    return pl_colorscale

red_blue = matplotlib_to_plotly(red_blue, 255)


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)
    st.caption("Interactive SHAP Summary Plot of All Input Genes")
    mpl_fig = shap_summary_plot(shap_values, df_shap, show=False)
    plotly_fig = tls.mpl_to_plotly(mpl_fig)
    plotly_fig['layout'] = {'xaxis': {'title': 'SHAP value (impact on model output)'}}

    feature_order = np.argsort(np.sum(np.abs(shap_values), axis=0)[:-1])
    feature_order = feature_order[-min(max_display, len(feature_order)):]
    text = gene_list
    text = iter(text)
    for i in range(1, len(plotly_fig['df_shap']), 2):
        t = text.__next__()
        plotly_fig['df_shap'][i]['name'] = ''
        plotly_fig['df_shap'][i]['text'] = t
        plotly_fig['df_shap'][i]['hoverinfo'] = 'text'
    colorbar_trace  = go.Scatter(x=[None],
                                 y=[None],
                                 mode='markers',
                                 marker=dict(
                                     colorscale=red_blue, 
                                     showscale=True,
                                     cmin=-5,
                                     cmax=5,
                                     colorbar=dict(thickness=5, tickvals=[-5, 5], ticktext=['Low', 'High'], outlinewidth=0)
                                 ),
                                 hoverinfo='none'
                                )
    plotly_fig['layout']['showlegend'] = False
    plotly_fig['layout']['hovermode'] = 'closest'
    plotly_fig['layout']['height']=600
    plotly_fig['layout']['width']=500
    plotly_fig['layout']['xaxis'].update(zeroline=True, showline=True, ticklen=4, showgrid=False)
    plotly_fig['layout']['yaxis'].update(dict(visible=False))
    plotly_fig.add_trace(colorbar_trace)
    plotly_fig.layout.update(
                             annotations=[dict(
                                  x=1.18,
                                  align="right",
                                  valign="top",
                                  text='Feature value',
                                  showarrow=False,
                                  xref="paper",
                                  yref="paper",
                                  xanchor="right",
                                  yanchor="middle",
                                  textangle=-90,
                                  font=dict(family='Calibri', size=14)
                                )
                             ],
                             margin=dict(t=20)
                            )
    st.plotly_chart(plotly_fig)

else:
    pass


input_gene = st.text_input("Input individual HGNC gene:")
df2 = df_total[df_total.index == input_gene]
df2['Gene'] = df2.index
df2.reset_index(drop=True, inplace=True)
df2 = df2[['Gene','XGB_Score', 'mousescore_Exomiser',
 'SDI', 'Liver_GTExTPM',  'pLI_ExAC',
 'HIPred',
 'Cells - EBV-transformed lymphocytes_GTExTPM',
 'Pituitary_GTExTPM',
 'IPA_BP_annotation']]
st.dataframe(df2)

if input_gene:
    df2_shap = df_total[df_total.index == input_gene]
    df2_shap.drop(columns='XGB_Score', inplace=True)
    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_output = df_total
df_total_output['Gene'] = df_total_output.index
df_total_output.reset_index(drop=True, inplace=True)
df_total_output = df_total_output[['Gene','XGB_Score', 'mousescore_Exomiser',
 'SDI', 'Liver_GTExTPM',  'pLI_ExAC',
 'HIPred',
 'Cells - EBV-transformed lymphocytes_GTExTPM',
 'Pituitary_GTExTPM',
 'IPA_BP_annotation']]
st.dataframe(df_total_output)