File size: 5,293 Bytes
27f6851
 
 
 
 
 
 
 
 
 
 
 
df07b29
27f6851
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
903fca3
27f6851
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
72230c3
27f6851
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import streamlit as st
import pandas as pd
import numpy as np
import time
import plotly.graph_objects as go
from scipy.ndimage import gaussian_filter1d
from zipfile import ZipFile

np.random.seed(2024)

uids = pd.read_csv("uniprot_ids.tsv.gz", names=["selection"], header=None, sep="\t")
# del_sub_merge = pd.read_csv("del_sub_data.csv.gz")
zf = ZipFile("ALL_hum_proteins_ESM1b_del_sub.zip")

width=600

def plot_interactive_scatter(uid: str):
    
    user_data = pd.read_csv(zf.open(f"{uid}.csv"))
        
    # Create scatter plot for user-specified data
    user_trace = go.Scatter(
    x=-np.log10(user_data.aPLLR),
    y=user_data.avg_LLR,
    mode='markers',
    name=f"{uid}<br>Data",
    text=user_data.site,
    hoverinfo='text',
    marker=dict(color='orange'))
    
    return user_trace, user_data

def plot_interactive_line(uid_data: pd.DataFrame, uid: str, score: str, mutation: str,
                          hline1: float, hline2: float):
    
    esm_data = -np.log10(uid_data[score]) if score == "aPLLR" else uid_data[score]
    x_ticks = uid_data["site"].tolist()
    
    plot_data = esm_data
    hover_text = [f"{x}: {np.round(y, 3)}" for x, y in zip(uid_data.site, plot_data)]
    
    line_trace = go.Scatter(
        x=np.arange(1, len(uid_data)+1),
        y=plot_data,
        mode='lines',
        text=hover_text,
        hoverinfo='text',
        marker=dict(color='orange')
    )
    line_fig = go.Figure(data=[line_trace])
    line_fig.update_layout(
        title=f"{uid} {mutation} Scores by Position",
        yaxis_title=f'{mutation} Score<br>(More Negative = More Damaging)',
        yaxis=dict(showgrid=False, zeroline=False, showline=False),
        height=300,
        hoverlabel=dict(  # Set hover label font size
            font=dict(size=16)  # Specify the font size of the hover text
        )
    )
    for hline in [hline1, hline2]:
        line_fig.add_shape(        
            type='line',
            x0=0, x1=1, y0=hline, y1=hline,
            xref='paper', yref='y',
            line=dict(color='Black', dash='dash'),
        )
    return line_fig
                           
selection = st.selectbox("", uids.selection, index=11409)
selection_uid = selection.split(",")[0]
                               
# Base dataset
base_data = pd.read_csv("rand_samp_gw_del_sub.csv.gz")

# Create base scatter plot
base_trace = go.Scatter(
    x=-np.log10(base_data.aPLLR),
    y=base_data.avg_LLR,
    mode='markers',
    name='Sample of<br>Genome-Wide<br>Data',
    hoverinfo='none', # Disable hover information for the base data
    marker=dict(color='grey')
)

# User-specified data
ut, ud = plot_interactive_scatter(selection_uid)

# Combine traces
fig = go.Figure([base_trace, ut])

# Customize layout
fig.update_layout(
    title='Deletion v Substitution Effects',
    xaxis_title='Deletion Score',
    yaxis_title='Substitution Score',
    yaxis=dict(showgrid=False, showline=False, zeroline=False),
    legend=dict(
        font=dict(size=15), # Specify the font size of the legend text
        bordercolor="grey",
        borderwidth=1
    ),
    hoverlabel=dict(  # Set hover label font size
        font=dict(size=16)  # Specify the font size of the hover text
    )
)

fig.update_yaxes(showgrid=False)

# Extract out percentiles
del_bot, del_top =  0.16500809479645437, -0.7801050825906862
for del_cutoff in [del_bot, del_top]:
    fig.add_shape(
        type='line',
        x0=del_cutoff, x1=del_cutoff, y0=0, y1=1,
        xref='x', yref='paper',
        line=dict(color='Black', width=2)
    )

# to avoid reading the entire dataset into memory
sub_bot, sub_top = -12.004105263157896, -4.871947368421053
for sub_cutoff in [sub_bot, sub_top]:
    fig.add_shape(
        type='line',
        x0=0, x1=1, y0=sub_cutoff, y1=sub_cutoff,
        xref='paper', yref='y',
        line=dict(color='Black', width=2),
    )

fig.add_annotation(
    x=2.5,
    y=-18,
    text=r"D<sup>+</sup>S<sup>—</sup>",
    font=dict(color="green", size=24),
    showarrow=False
)

fig.add_annotation(
    x=-1.5,
    y=0.5,
    text=r"D<sup>—</sup>S<sup>+</sup>",
    font=dict(color="red", size=24),
    showarrow=False
)

lt_apllr = plot_interactive_line(ud, selection_uid, "aPLLR", "Deletion", del_bot, del_top)

lt_llr = plot_interactive_line(ud, selection_uid, "avg_LLR", "Substitution", sub_bot, sub_top)

# Show the scatter plot
st.plotly_chart(fig)

show_line_plots = st.checkbox("Show Deletion and Substitution Effects Alone")

if show_line_plots:
    st.plotly_chart(lt_apllr)
    st.plotly_chart(lt_llr)

st.download_button(
    label=f"Download {selection_uid} data as CSV",
    data=ud.reset_index(drop=True)[["site", "aPLLR", "avg_LLR"]].to_csv(),
    file_name = f"{selection_uid}_del_sub.csv",
    mime='text/csv'
)



st.markdown("""
**README**:
- Deletion scores are *visualized* on the -log10 scale. 
- The genome-wide dataset can be downloaded by clicking [here](https://huggingface.co/spaces/goldmangrant/diff-tol/blob/main/ALL_hum_proteins_ESM1b_del_sub.zip) (or go to files tab).
- Non-aggregated substitution effects can be downloaded or browsed [here](https://huggingface.co/spaces/ntranoslab/esm_variants).
- Additional supplementary data from the paper can be downloaded [here](https://github.com/ntranoslab/diff-tol).
""")