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app.py

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+import streamlit as st
+st.set_page_config(layout="wide")
+
+import pandas as pd
+import numpy as np
+from zipfile import ZipFile
+
+import plotly.express as px
+import plotly.graph_objs as go
+
+LLR_FILE = 'UniProtKB_human_VESM_llrs.zip'
+
+df = pd.read_csv('UniProtKB_id_names.csv', index_col=0)
+if 'shuffled_df' not in st.session_state:
+    st.session_state.shuffled_df = df.sample(frac=1)
+df = st.session_state.shuffled_df
+clinvar = pd.read_csv('clinvar_0325.csv.gz',index_col=0)
+
+f = np.load("logreg_params.npz")
+coef, intercept = f["coef"].item(), f["intercept"].item()
+
+def load_LLR(uniprot_id):
+    '''Loads the LLRs for a given uniprot id. Returns a 20xL dataframe. 
+    Rows are indexed by AA change, 
+    (AAorder=['K','R','H','E','D','N','Q','T','S','C','G','A','V','L','I','M','P','Y','F','W'])
+    Columns indexed by WT_AA+position e.g., "G 12".
+    Usage: load_LLR('P01116') or load_LLR('P01116-2')
+    '''
+    with ZipFile(LLR_FILE) as myzip:
+        data = myzip.open(myzip.namelist()[0] + 'LLRs/' + uniprot_id + '.csv')
+    LLR = pd.read_csv(data, index_col=0)
+    if sigmoid:
+        p = 1/(1 + np.exp(-(LLR.values.ravel()*coef + intercept)))
+        LLR = pd.DataFrame(p.reshape(LLR.shape), index=LLR.index, columns=LLR.columns).round(4)
+    return LLR
+
+def meltLLR(LLR, gene_prefix=None, ignore_pos=False):
+    vars = LLR.melt(ignore_index=False)
+    vars['variant'] = [''.join(i.split(' ')) + j for i, j in zip(vars['variable'], vars.index)]
+    vars['score'] = vars['value']
+    vars = vars.set_index('variant')
+    if not ignore_pos:
+        vars['pos'] = [int(i[1:-1]) for i in vars.index]
+    del vars['variable'], vars['value']
+    if gene_prefix is not None:
+        vars.index = gene_prefix + '_' + vars.index
+    return vars
+    
+    
+def plot_interactive(uniprot_id, show_clinvar=False):
+    primaryLLR = load_LLR(uniprot_id)
+    template = 'plotly_white'
+    zmax=1.09 if sigmoid else 0
+    zmin=0 if sigmoid else -22
+    cmap='rdbu_r' if sigmoid else 'Viridis_r'
+    color = 'score' if sigmoid else 'LLR' 
+    fig = px.imshow(
+        primaryLLR.values, 
+        x=primaryLLR.columns, 
+        y=primaryLLR.index, 
+        color_continuous_scale=cmap, 
+        zmax=zmax, 
+        zmin=zmin,
+        labels=dict(y="Amino acid change", x="Protein sequence", color=color),
+        template=template,
+        title=selection
+    )
+
+    fig.update_xaxes(tickangle=-90,range=[0,99], rangeslider=dict(visible=True), dtick=1)
+    fig.update_yaxes(dtick=1)
+    fig.update_layout(
+        plot_bgcolor='rgba(0, 0, 0, 0)',
+        paper_bgcolor='rgba(0, 0, 0, 0)',
+        font={'family': 'Arial', 'size': 11},
+        hoverlabel=dict(font=dict(family='Arial', size=14))
+    )
+
+    fig.update_traces(
+        hovertemplate="<br>".join(["<b>%{x} %{y}</b> (%{z:.2f})"]) + '<extra></extra>'
+    )
+
+    if show_clinvar:
+        iso_clinvar = clinvar[clinvar.protein == uniprot_id]
+        iso_clinvar = iso_clinvar[iso_clinvar.GoldStars > 1]
+        b_mut = set(iso_clinvar[iso_clinvar.clinvar_label == 0.0].variant.values)
+        p_mut = set(iso_clinvar[iso_clinvar.clinvar_label == 1.0].variant.values)
+
+        hwt_x, hwt_y, cust = [], [], []
+        phwt_x, phwt_y, pcust = [], [], []
+
+        for i in primaryLLR.columns:
+            for j in list(primaryLLR.index):
+                mut = i[0] + i[2:] + j
+                if mut in b_mut:
+                    hwt_x.append(i)
+                    hwt_y.append(j)
+                    cust.append(primaryLLR.loc[j, i])
+                elif mut in p_mut:
+                    phwt_x.append(i)
+                    phwt_y.append(j)
+                    pcust.append(primaryLLR.loc[j, i])
+
+        # draw pathogenic
+        fig.add_trace(go.Scatter(
+            x=phwt_x, y=phwt_y, customdata=pcust,
+            mode='markers',
+            marker=dict(size=8, color='red'),
+            showlegend=False,
+            hoverlabel=dict(bgcolor='crimson', font_color='black'),
+            hovertemplate="<b>%{x} %{y}</b> (%{customdata:.2f})<extra></extra>"
+        ))
+        # draw benign
+        fig.add_trace(go.Scatter(
+            x=hwt_x, y=hwt_y, customdata=cust,
+            mode='markers',
+            marker=dict(size=8, color='white'),
+            showlegend=False,
+            hoverlabel=dict(bgcolor='white', font_color='black'),
+            hovertemplate="<b>%{x} %{y}</b> (%{customdata:.2f})<extra></extra>"
+        ))
+
+        fig.update_layout(
+            hovermode='closest',
+            hoverdistance=10
+        )
+
+    return fig
+
+
+idx = df.index.get_loc('P32245') if 'P32245' in df.index else 0
+selection = st.selectbox("uniprot_id:", df, index=idx)
+uid = df[df.txt == selection].index.values[0]
+
+col1, col2 = st.columns(2)
+with col1:
+    sigmoid = st.checkbox(
+        "Calibrated VESM predictions (0: benign, 1: pathogenic)",
+        value=False
+    )
+with col2:
+    show_clinvar = st.checkbox(
+        "Show ClinVar annotations (red: pathogenic, white: benign)",
+        value=False
+    )
+
+fig = plot_interactive(uid, show_clinvar=show_clinvar)
+fig.update_layout(width=800, height=600, autosize=False)
+st.plotly_chart(fig, use_container_width=True)
+
+st.download_button(
+    label="📥 Download as CSV",
+    data=meltLLR(load_LLR(uid)).to_csv(),
+    file_name=f"{selection}.csv",
+    mime='text/csv'
+)
+st.markdown("---")
+
+st.markdown("""
+- Bulk download precomputed scores at [VESM Effect Scores](https://huggingface.co/datasets/ntranoslab/vesm_scores) for all UniProt, hg19, and hg38 variants.
+- Use VESM locally: Access the source code and installation instructions on [GitHub](https://github.com/ntranoslab/vesm).
+""")

clinvar_0325.csv.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b692c7298c46bcf3397baaca93b89e8f22d32bc891dab0ac2e3af90ac8944c08
+size 2128878

logreg_params.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6411c5d1bcab64080217d97be085397974a5f68b2a5a680e9081b7959289c81d
+size 776

requirements.txt

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+altair
+streamlit
+plotly
+protobuf~=3.19.0