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import datasets |
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import streamlit as st |
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import numpy as np |
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import pandas as pd |
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import altair as alt |
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st.set_page_config(layout='wide') |
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st.markdown(""" |
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# CryptoCEN Expression Scatter |
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**CryptoCEN** is a co-expression network for *Cryptococcus neoformans* built on 1,524 RNA-seq runs across 34 studies. |
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A pair of genes are said to be co-expressed when their expression is correlated across different conditions and |
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is often a marker for genes to be involved in similar processes. |
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To Cite: |
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MJ O'Meara, JR Rapala, CB Nichols, C Alexandre, B Billmyre, JL Steenwyk, A Alspaugh, |
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TR O'Meara CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals |
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novel proteins involved in DNA damage repair |
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* Code available at https://github.com/maomlab/CalCEN/tree/master/vignettes/CryptoCEN |
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* Full network and dataset: https://huggingface.co/datasets/maomlab/CryptoCEN |
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## Plot scatter plot expression for a pair of genes across studies. |
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Put in the ``CNAG_#####`` gene_id for two genes. |
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""") |
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h99_transcript_annotations = datasets.load_dataset( |
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path = "maomlab/CryptoCEN", |
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data_files = {"h99_transcript_annotations": "h99_transcript_annotations.tsv"}) |
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h99_transcript_annotations = h99_transcript_annotations["h99_transcript_annotations"].to_pandas() |
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estimated_expression_meta = datasets.load_dataset( |
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path = "maomlab/CryptoCEN", |
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data_files = {"estimated_expression_meta": "Data/estimated_expression_meta.tsv"}) |
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estimated_expression_meta = estimated_expression_meta["estimated_expression_meta"].to_pandas() |
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estimated_expression = datasets.load_dataset( |
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path = "maomlab/CryptoCEN", |
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data_files = {"estimated_expression": "estimated_expression_matrix.parquet"}) |
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estimated_expression = estimated_expression["estimated_expression"].to_pandas() |
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print(f"estimated_expression shape: {estimated_expression.shape}") |
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col1, col2, padding = st.columns(spec = [0.2, 0.2, 0.6]) |
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with col1: |
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gene_id_1 = st.text_input( |
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label = "Gene ID 1", |
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value = "CNAG_04365", |
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max_chars = 10, |
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help = "CNAG Gene ID e.g. CNAG_04365") |
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with col2: |
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gene_id_2 = st.text_input( |
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label = "Gene ID 2", |
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value = "CNAG_04222", |
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max_chars = 10, |
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help = "CNAG Gene ID e.g. CNAG_04222") |
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try: |
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cnag_id_1 = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id_1]["cnag_id"].values |
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except: |
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st.error(f"Unable to locate cnag_id for Gene ID 1: {gene_id_1}, it should be of the form 'CNAG_######'") |
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try: |
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cnag_id_2 = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id_2]["cnag_id"].values |
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except: |
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st.error(f"Unable to locate cnag_id for Gene ID 2: {gene_id_2}, it should be of the form 'CNAG_######'") |
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chart_data = pd.DataFrame({ |
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"expression_1": np.log10(estimated_expression.loc[h99_transcript_annotations["gene_id"] == gene_id_1].to_numpy()[0] + 1), |
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"expression_2": np.log10(estimated_expression.loc[h99_transcript_annotations["gene_id"] == gene_id_2].to_numpy()[0] + 1), |
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"run_accession": estimated_expression.columns, |
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"run_accession_meta": estimated_expression_meta["run_accession"], |
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"study_accession": estimated_expression_meta["study_accession"]}) |
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print(f"run_ids are equal: {sum(chart_data['run_accession'] == chart_data['run_accession_meta'])}") |
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chart = ( |
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alt.Chart(chart_data) |
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.mark_circle() |
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.encode( |
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x="expression_1", |
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y="expression_2", |
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size=5, |
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color="study_accession", |
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tooltip=["run_accession", "study_accession"])) |
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st.altair_chart( |
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chart, |
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use_container_width=True) |
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