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import streamlit as st |
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from constants import DEFAULT_DR, DEFAULT_META |
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from utils import fetch_resource, plot_feature, plot_sc_embedding, ui_model_selection |
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st.set_page_config(layout="wide") |
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st.markdown(""" |
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# Gene expression |
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Levels of gene activity along differentiation. |
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""") |
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ui_model_selection() |
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if st.session_state["SPECIE"] and st.session_state["VERSION"]: |
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adata = fetch_resource(st.session_state["SPECIE"], st.session_state["VERSION"]) |
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sl_dr = st.sidebar.selectbox( |
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"**Dimension reduction**", |
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adata.obsm_keys(), |
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index=adata.obsm_keys().index(DEFAULT_DR), |
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placeholder="Select method ...", |
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) |
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st.sidebar.markdown( |
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f"Visualization done on `{adata.uns['neighbors']['params']['use_rep']}` space." |
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) |
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sl_metadata = st.sidebar.selectbox( |
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"**Metadata**", |
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adata.obs.columns, |
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index=adata.obs.columns.get_loc(DEFAULT_META), |
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placeholder="Select column ...", |
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) |
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sl_feature = st.sidebar.selectbox( |
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"**Gene**", |
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sorted(adata.raw.var_names), |
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index=0, |
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placeholder="Select gene ...", |
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) |
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is_imputed = sl_feature in adata.var_names |
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sl_denoised = st.sidebar.checkbox( |
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"Use denoised expression?", |
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help="Denoised expression is sampled from the decoder.", |
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disabled=(not is_imputed), |
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) |
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col1, col2 = st.columns(2) |
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plot_sc_embedding(adata, group_by=sl_metadata, reduction_key=sl_dr, ax=col1) |
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plot_sc_embedding( |
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adata, feature=sl_feature, reduction_key=sl_dr, layer=sl_denoised, ax=col2 |
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) |
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st.markdown("## Raw gene expression") |
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plot_feature(adata, feature=sl_feature, group_by=sl_metadata, kind="box") |
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