From e37b85bddc6d64dfb7c972e9f51f13e81c7dd36d Mon Sep 17 00:00:00 2001
From: DIANE <abderrahim.diane@cefe.cnrs.fr>
Date: Thu, 28 Nov 2024 09:43:06 +0100
Subject: [PATCH] selected when no clustering is performed
---
src/pages/1-samples_selection.py | 39 +++++++++++++++++++++++---------
1 file changed, 28 insertions(+), 11 deletions(-)
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index cfefd42..b38cec1 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -562,17 +562,36 @@ if not spectra.empty:
st.subheader('III - Selected Samples for Reference Analysis', divider='blue')
if selected:
c16, c17 = st.columns([3, 1])
- c16.write("Tabular identifiers of selected samples for reference analysis:")
+ with c16:
+ st.write("Tabular identifiers of selected samples for reference analysis:")
- if 'labels' in globals():
- labels['cluster'] = labels.index
- labels.index = labels['names']
- result = DataFrame({'names': selected,
- 'cluster': np.array(labels)[selected]}, index=selected)
+ if 'labels' in globals():
+ labels['cluster'] = labels.index
+ labels.index = labels['names']
+ result = DataFrame({'names': selected,
+ 'cluster': np.array(labels)[selected]}, index=selected)
- else:
-
- st.write(meta_data)
+ else:
+ if 'name' in meta_data.columns:
+ subset = meta_data.drop('name', axis = 1).loc[selected]
+ else:
+ subset = meta_data.loc[selected]
+ st.write(subset)
+
+ with c17:
+ st.info(f'Information !\n - The total number of samples: {n_specs}.\n- The number of samples selected for reference analysis: {len(selected)}.\n - The proportion of samples selected for reference analysis: {round(len(selected)/n_specs*100)}%.')
+
+ selected_col = st.selectbox('Color by: ', options=filter, format_func=lambda x: x if x else "<Select>",
+ disabled=True if len(filter) == 1 else False)
+ if selected_col:
+ cmap2 = dict(
+ zip(set(md_df_st_.loc[selected][selected_col]), colorslist[:len(set(md_df_st_.loc[selected][selected_col]))]))
+ st.write('The distribution of selected samples across categories')
+
+ barhsel = barhplot(md_df_st_.loc[selected][[specs_col]], cmap=cmap2)
+ st.pyplot(barhsel)
+
+
# if meta_data.empty:
@@ -587,8 +606,6 @@ if selected:
# sam1.insert(loc=0, column='index', value=selected_samples_idx)
# sam1.insert(loc=1, column='cluster', value=np.array(labels)[selected_samples_idx])
# sam1.index = np.arange(len(selected_samples_idx))+1
-# with c17:
-# st.info(f'Information !\n - The total number of samples: {n_specs}.\n- The number of samples selected for reference analysis: {sam1.shape[0]}.\n - The proportion of samples selected for reference analysis: {round(sam1.shape[0]/n_specs*100)}%.')
# sam = sam1
# if clus_method =='HDBSCAN':
--
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