Update dash_plotly_QC_scRNA.py
Browse files- dash_plotly_QC_scRNA.py +9 -8
dash_plotly_QC_scRNA.py
CHANGED
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@@ -374,19 +374,20 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
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total_count = len(dff)
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category_counts = category_counts.with_columns((pl.col("count") / total_count * 100).alias("normalized_count"))
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# Calculate the mean expression
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round_precision = 2
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category_counts = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).count().alias("count"))
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category_sums = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).sum().alias("sum"))
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total_count = len(dff)
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category_means = category_sums.select((pl.col("sum") / total_count).round(round_precision).alias("mean"))
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# Display the result
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labels = category_counts["batch"].to_list()
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values = category_counts["normalized_count"].to_list()
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total_cells = total_count # Calculate total number of cells
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pie_title = f'Percentage of Total Cells: {total_cells}' # Include total cells in the title
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fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn")
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@@ -436,7 +437,7 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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fig_scatter_12 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, size=
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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total_count = len(dff)
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category_counts = category_counts.with_columns((pl.col("count") / total_count * 100).alias("normalized_count"))
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# Display the result
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labels = category_counts["batch"].to_list()
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values = category_counts["normalized_count"].to_list()
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total_cells = total_count # Calculate total number of cells
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pie_title = f'Percentage of Total Cells: {total_cells}' # Include total cells in the title
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# Calculate the mean expression
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round_precision = 2
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category_counts = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).count().alias("count"))
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category_sums = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).sum().alias("sum"))
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total_count = len(dff)
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category_means = category_sums.select((pl.col("sum") / total_count).round(round_precision).alias("mean"))
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values_mean = category_counts["mean"].to_list()
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fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn")
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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fig_scatter_12 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, size=values_mean,
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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