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import streamlit as st |
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from utils import fetch_resource, get_degs, ui_model_selection |
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st.set_page_config(layout="wide") |
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st.markdown(""" |
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# Differentially expressed genes |
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Genes below have been determined using `sc.tl.rank_genes_groups` where `t-test_overestim_var` |
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is the default method. |
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""") |
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ui_model_selection() |
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filter_flag = [] |
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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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degs_by = st.sidebar.selectbox( |
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"**Select by**", |
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[x for x in adata.uns_keys() if "degs" in x], |
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index=None, |
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placeholder="Differentially expressed genes by", |
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) |
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if degs_by: |
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degs = get_degs(adata, degs_by) |
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group = st.sidebar.multiselect( |
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"**Cell type**", degs["group"].unique(), placeholder="Select group ..." |
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) |
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genes = st.sidebar.multiselect( |
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"**Gene**", sorted(degs["names"].unique()), placeholder="Select genes ..." |
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) |
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foldchange = st.sidebar.number_input( |
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"**Log2 fold-change**", |
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value=1, |
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) |
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pval_adj = st.sidebar.number_input( |
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"**Adjusted p-value**", |
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value=0.05, |
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) |
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if group: |
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filter_flag.append("group == @group") |
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if genes: |
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filter_flag.append("@genes in names") |
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if foldchange: |
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filter_flag.append( |
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"logfoldchanges > @foldchange" |
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if foldchange > 0 |
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else "logfoldchanges < @foldchange" |
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) |
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if pval_adj: |
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filter_flag.append("pvals_adj < @pval_adj") |
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subset = degs.query(" & ".join(filter_flag)) if filter_flag else degs |
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st.dataframe(subset, use_container_width=True, height=650) |
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