Update dash_plotly_QC_scRNA.py
Browse files- dash_plotly_QC_scRNA.py +3 -2
dash_plotly_QC_scRNA.py
CHANGED
@@ -324,6 +324,8 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
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#Drop categories that are not in the filtered data
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dff = dff.with_columns(dff[col_chosen].cast(pl.Categorical))
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# Plot figures
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fig_violin = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all",
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color=col_chosen, hover_name=col_chosen,template="seaborn")
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@@ -374,9 +376,8 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
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# TO DO
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expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
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# Order the
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expression_means = expression_means.sort(col_chosen, descending=True)
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dff = dff.sort(col_chosen, descending=True)
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#expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
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#Drop categories that are not in the filtered data
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dff = dff.with_columns(dff[col_chosen].cast(pl.Categorical))
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dff = dff.sort(col_chosen)
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# Plot figures
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fig_violin = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all",
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color=col_chosen, hover_name=col_chosen,template="seaborn")
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# TO DO
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expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
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+
# Order the dataframe on ascending categories
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expression_means = expression_means.sort(col_chosen, descending=True)
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#expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
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