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3c85094
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Parent(s):
fcd198b
add visualization
Browse files- app.py +17 -25
- requirements.txt +2 -1
app.py
CHANGED
@@ -4,10 +4,10 @@ import cas9on
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import cas9off
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import pandas as pd
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import streamlit as st
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import numpy as np
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from pathlib import Path
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# title and documentation
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st.markdown(Path('crisprTool.md').read_text(), unsafe_allow_html=True)
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@@ -103,38 +103,30 @@ if selected_model == 'Cas9':
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# Process predictions
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if predict_button and gene_symbol:
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predictions = cas9on.process_gene(gene_symbol, cas9on_path)
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# Sort predictions by the 'Prediction' score in descending order and take the top 10
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sorted_predictions = sorted(predictions, key=lambda x: x[-1], reverse=True)[:10]
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st.session_state['on_target_results'] = sorted_predictions
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# On-target results display
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if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
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# Convert the results to a pandas DataFrame for better display
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df = pd.DataFrame(st.session_state['on_target_results'],
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columns=["Gene ID", "Start Pos", "End Pos", "Strand", "gRNA", "Prediction"])
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st.write('Top on-target predictions:')
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st.dataframe(df)
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#
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label='Download all on-target predictions',
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data=full_predictions_csv,
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file_name='on_target_results.csv',
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mime='text/csv'
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)
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elif target_selection == 'off-target':
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ENTRY_METHODS = dict(
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import cas9off
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import pandas as pd
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import streamlit as st
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from pygenomeviz import GenomeViz
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import numpy as np
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from pathlib import Path
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# title and documentation
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st.markdown(Path('crisprTool.md').read_text(), unsafe_allow_html=True)
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# Process predictions
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if predict_button and gene_symbol:
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predictions = cas9on.process_gene(gene_symbol, cas9on_path)
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sorted_predictions = sorted(predictions, key=lambda x: x[-1], reverse=True)[:10]
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st.session_state['on_target_results'] = sorted_predictions
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if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
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df = pd.DataFrame(st.session_state['on_target_results'],
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columns=["Gene ID", "Start Pos", "End Pos", "Strand", "gRNA", "Prediction"])
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st.write('Top on-target predictions:')
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st.dataframe(df)
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# Initialize GenomeViz
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gv = GenomeViz()
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genome_size = max(
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df["End Pos"]) # Assuming the max end position approximates the genome size for visualization purposes
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track = gv.add_feature_track("CRISPR Targets", genome_size)
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for _, row in df.iterrows():
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start, end, strand = row["Start Pos"], row["End Pos"], row["Strand"]
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label = row["gRNA"]
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track.add_feature(start, end, strand, label=label)
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# Save and display the visualization
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gv_fig_path = "crispr_targets.png"
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gv.savefig(gv_fig_path)
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st.image(gv_fig_path, caption="CRISPR Targets Visualization")
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elif target_selection == 'off-target':
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ENTRY_METHODS = dict(
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requirements.txt
CHANGED
@@ -3,4 +3,5 @@ biopython==1.80
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pandas==1.5.2
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tensorflow==2.11.0
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tensorflow-probability==0.19.0
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plotly==5.18.0
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pandas==1.5.2
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tensorflow==2.11.0
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tensorflow-probability==0.19.0
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plotly==5.18.0
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pygenomeviz
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