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import streamlit as st |
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from utils import fetch_resource, ui_model_selection |
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st.set_page_config(layout="wide") |
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st.markdown(""" |
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# SHAP features |
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Predicted features (genes) used by the scANVI classifier to determine a cell type. The features |
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have been determined using [SHAP](https://shap.readthedocs.io/en/latest/). |
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Each metric for a feature is determined from 10 random boostraps with replacement. |
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- weight_mean: $\mu$ of SHAP value |
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- weight_std: $\sigma$ of SHAP value |
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- weight_ci_upper: $\mu$ + $\sigma$ |
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- weight_ci_lower: $\mu$ - $\sigma$ |
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- logfoldchanges: Log2fold change from differentiation expression analysis |
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- pvals_adj: Adjusted p-value from differentiation expression analysis |
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- scores: Estimated score from differentiation expression analysis |
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""") |
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ui_model_selection() |
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filter_condition = [] |
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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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explainer = st.sidebar.selectbox( |
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"**Explainer**", |
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adata.uns["explainer"].keys(), |
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index=None, |
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placeholder="Select explainer ...", |
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) |
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if explainer: |
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shap_values = ( |
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adata.uns["explainer"][explainer] |
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.pop("shap_values") |
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.reset_index() |
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.rename(columns={"index": "feature"}) |
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) |
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params = [f"{k}:\t{v}" for k, v in adata.uns["explainer"][explainer].items()] |
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st.sidebar.markdown("**Parameters**") |
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for k, v in adata.uns["explainer"][explainer].items(): |
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st.sidebar.markdown(f"{k}:\t{v}") |
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celltype = st.sidebar.selectbox( |
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"**Cell type**", |
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adata.obs.ct.cat.categories, |
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index=None, |
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placeholder="Select cell type ...", |
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) |
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features = st.sidebar.multiselect( |
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"**Genes**", |
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sorted(shap_values.feature.unique()), |
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placeholder="Select genes ...", |
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) |
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if celltype: |
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filter_condition.append("ct == @celltype") |
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if features: |
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filter_condition.append("feature in @features") |
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if filter_condition: |
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shap_values = shap_values.query(" & ".join(filter_condition)) |
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st.dataframe(shap_values, use_container_width=True, height=650) |
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