app.py

import datasets
import streamlit as st
import numpy as np
import pandas as pd
import altair as alt

st.set_page_config(layout='wide')

# parse out feature_names from URL query args to it's possible to link to this page
query_params = st.query_params
if "feature_name_1" in query_params.keys():
    feature_name_1 = query_params["feature_name_1"]
else:
    feature_name_1 = "B9J08_000003"

if "feature_name_2" in query_params.keys():
    feature_name_2 = query_params["feature_name_2"]
else:
    feature_name_2 = "B9J08_000004"

    


st.markdown("""
# CaurisCEN Expression Scatter
**CaurisCEN** is a co-expression network for *Candida auris* built on 577 RNA-seq runs across 2 96-well plates formats in 3 biological replicas.
A pair of genes are said to be co-expressed when their expression is correlated across different conditions and
is often a marker for genes to be involved in similar processes. 
To Cite:
    Rapala JR, MJ O'Meara, TR O'Meara
    CaurisCEN: A Co-Expression Network for Candida auris
* Code available at https://github.com/maomlab/CaurisCEN/tree/master/vignettes/CaurisCEN
* Full network and dataset: https://huggingface.co/datasets/maomlab/CaurisCEN

## Plot scatter plot expression for a pair of genes across studies.
Put in the B9J08 locus tag  (e.g. `B9J08_004112`) for two genes.
""")

gene_metadata = datasets.load_dataset(
    path = "maomlab/CaurisCEN",
    name = "gene_metadata",
    data_dir = "gene_metadata/data")['train'].to_pandas()

expression_runs = datasets.load_dataset(
    path = "maomlab/CaurisCEN",
    name = "run_metadata",
    data_dir = "run_metadata/data")['train'].to_pandas()

expression_matrix = datasets.load_dataset(
    path = "maomlab/CaurisCEN",
    name = "expression_matrix",
    data_dir = "expression_matrix")['train'].to_pandas()


#DEBUG
print(f"expression_matrix shape: {expression_matrix.shape}")

col1, col2, col3, padding = st.columns(spec = [0.2, 0.2, 0.2, 0.4])
with col1:
    feature_name_1 = st.text_input(
        label = "Feature Name 1",
        value = f"{feature_name_1}",
        max_chars = 12,
        help = "B9J08 Locus Tag e.g. B9J08_000003")

with col2:
    feature_name_2 = st.text_input(
        label = "Feature Name 2",
        value = f"{feature_name_2}",
        max_chars = 12,
        help = "B9J08 New Locus Tag e.g. B9J08_000004")

# check the user input
try:
    locus_tag_new_1 = gene_metadata.loc[gene_metadata["locus_tag_old"] == feature_name_1]["locus_tag_new"].values[0]
    gene_name_1 = gene_metadata.loc[gene_metadata["locus_tag_old"] == feature_name_1]["gene_name"].values[0]
    description_1 = gene_metadata.loc[gene_metadata["locus_tag_old"] == feature_name_1]["description"].values[0]
except:
    st.error(f"Unable to locate locus tag for gene 1: {feature_name_1}, it should be of the form 'B9J08_#######'")

try:
    locus_tag_new_2 = gene_metadata.loc[gene_metadata["locus_tag_old"] == feature_name_2]["locus_tag_new"].values[0]
    gene_name_2 = gene_metadata.loc[gene_metadata["locus_tag_old"] == feature_name_2]["gene_name"].values[0]
    description_2 = gene_metadata.loc[gene_metadata["locus_tag_old"] == feature_name_2]["description"].values[0]
except:
    st.error(f"Unable to locate locus tag for gene 2: {feature_name_2}, it should be of the form 'B9J08_#######'")

expression_1 = expression_matrix.loc[expression_matrix["gene"] == locus_tag_new_1].transpose().iloc[1:,0].to_numpy().astype('float64')
expression_2 = expression_matrix.loc[expression_matrix["gene"] == locus_tag_new_2].transpose().iloc[1:,0].to_numpy().astype('float64')

chart_data = pd.DataFrame({
    "feature_name_1": feature_name_1,
    "feature_name_2": feature_name_2,
    "expression_1": expression_1,
    "expression_2": expression_2,
    "log_expression_1": np.log10(expression_1 + 1),
    "log_expression_2": np.log10(expression_2 + 1),
    "run_accession": expression_matrix.columns[1:]})
chart_data = chart_data.merge(
    right = expression_runs,
    on = "run_accession")

with col3:
    st.text('') # help alignment with input box
    st.download_button(
        label = "Download data as TSV",
        data = chart_data.to_csv(sep ='\t').encode('utf-8'),
        file_name = f"CaurisCEN_expression_{feature_name_1}_vs_{feature_name_2}.tsv",
        mime = "text/csv")


st.markdown(f"""
#### Gene 1:
* *Gene ID*: [{feature_name_1}](http://www.candidagenome.org/cgi-bin/locus.pl?locus={feature_name_1}&organism=C_auris_B8441)
{'* *Gene Name*:' + gene_name_1 if gene_name_1 is not None else ''}
* *Description*: {description_1}
* *Top [Co-Expressed Partners](https://huggingface.co/spaces/maomlab/CaurisCEN-TopHits?feature_name={feature_name_1})*

#### Gene 2:
* *Gene ID*: [{feature_name_2}](http://www.candidagenome.org/cgi-bin/locus.pl?locus={feature_name_2}&organism=C_auris_B8441)
{'* *Gene Name*:' + gene_name_2 if gene_name_2 is not None else ''}
* *Description*: {description_2}
* *Top [Co-Expressed Partners](https://huggingface.co/spaces/maomlab/CaurisCEN-TopHits?feature_name={feature_name_2})*
""")

chart = (
   alt.Chart(
       chart_data,
       width = 750,
       height = 750)
   .mark_circle()
   .encode(
       x=alt.X("log_expression_1", title=f"Log10[{feature_name_1}+1] Expression"),
       y=alt.Y("log_expression_2", title=f"Log10[{feature_name_2}+1] Expression"),
       color=alt.Color("study_accession", title="Study Accession"),
       tooltip=["run_accession", "study_accession"]))

st.altair_chart(
    chart)