Merge pull request #25 from abzer005/main

Author: axelwalter

Statistics_for_Metabolomics.py

@@ -10,8 +10,8 @@
 ## Quickstart Guide
 
 Welcome to the FBMN-STATS, a web app implementation of the [statistics notebooks](https://github.com/Functional-Metabolomics-Lab/Statistical-analysis-of-non-targeted-LC-MSMS-data) for metabolomics by the [Functional Metabolomics Lab](https://github.com/Functional-Metabolomics-Lab).
-             as part of the article '[The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data](https://doi.org/10.26434/chemrxiv-2023-wwbt0)'. 
-            These notebooks are developed by the Virtual Multi Omics Lab ([VMOL](https://vmol.org/)).
+             as part of the article '[The Statistical Analysis of Feature-based Molecular Networking Results from Non-Targeted Metabolomics Data](https://doi.org/10.26434/chemrxiv-2023-wwbt0)'. 
+            These notebooks are developed by the Virtual MultiOmics Lab ([VMOL](https://vmol.org/)).
             This app facilitates downstream statistical analysis of Feature-Based Molecular Networking data, simplifying the process for researchers.       
 
 ### Getting Started
@@ -81,7 +81,7 @@
 
 st.subheader('Settings Panel')
 st.markdown("""
-1. **P-value Correction:** These are FDR (False Disovery Rate) corrections applied for multiple univariate tests. Available options include Bonferroni, Sidak, Benjamini-Hochberg (BH), Benjamini-Yekutieli (BY), and an option for no correction, with Bonferroni set as the default.  
+1. **P-value Correction:** These are FDR (False Disovery Rate) corrections applied for multiple univariate tests. Available options include Bonferroni, Sidak, Benjamini-Hochberg (BH), Benjamini-Yekutieli (BY), and an option for no correction.  
             While Bonferroni is known for controlling false positives, it may inadvertently increase false negatives. Advanced methods like BH and BY aim to balance true discoveries against false positives more effectively. We recommend BH for FDR correction to optimize analysis outcomes.
             
     Note that changing the p-value correction settings does not automatically update the corrected p-values. To update results re-run the analysis.
@@ -98,7 +98,7 @@
 
 st.subheader('Citation and Resources')
 st.markdown("""
-For citations and further resources, please refer to our [article](https://doi.org/10.26434/chemrxiv-2023-wwbt0).
+For citations and further resources, please refer to our [article](https://doi.org/10.26434/chemrxiv-2023-wwbt0). You can download the Windows executables of the app from our [homepage](https://www.functional-metabolomics.com/resources).
 """)
 
 
@@ -123,18 +123,18 @@
     unsafe_allow_html=True,
 )
 c2.markdown(
-    """<a href="https://www.youtube.com/@functionalmetabolomics">
+    """<a href="https://www.functional-metabolomics.com/">
     <img src="data:image/png;base64,{}" width="100">
     </a>""".format(
-        base64.b64encode(open("./assets/youtube-logo.png", "rb").read()).decode()
+        base64.b64encode(open("./assets/fmlab_logo_colored.png", "rb").read()).decode()
     ),
     unsafe_allow_html=True,
 )
 c3.markdown(
-    """<a href="https://twitter.com/func_metabo_lab">
+    """<a href="https://www.youtube.com/@functionalmetabolomics">
     <img src="data:image/png;base64,{}" width="100">
     </a>""".format(
-        base64.b64encode(open("./assets/x-logo.png", "rb").read()).decode()
+        base64.b64encode(open("./assets/youtube-logo.png", "rb").read()).decode()
     ),
     unsafe_allow_html=True,
 )

assets/GNPS2_logo.png

@@ -17,9 +17,9 @@
                    "md_gnps",
                    "df_gnps_annotations")
 
-corrections_map = {"Bonferroni": "bonf",
+corrections_map = {"Benjamini/Hochberg FDR": "fdr_bh",
                    "Sidak": "sidak",
-                   "Benjamini/Hochberg FDR": "fdr_bh",
+                   "Bonferroni": "bonf",
                    "Benjamini/Yekutieli FDR": "fdr_by",
                    "no correction": "none"}
 
@@ -28,6 +28,14 @@ def reset_dataframes():
     for key in dataframe_names:
         st.session_state[key] = pd.DataFrame()
 
+def clear_cache_button():
+   if st.button("Clear Cache"):
+        # Clear cache for both newer and older Streamlit versions
+        if hasattr(st, "cache_data"):
+            st.cache_data.clear()
+        if hasattr(st, "cache_resource"):
+            st.cache_resource.clear()
+        st.success("Cache cleared!")
 
 def page_setup():
     # streamlit configs
@@ -56,11 +64,42 @@ def page_setup():
                 ["svg", "png", "jpeg", "webp"],
                 key="image_format",
             )
+
+            # Add the clear cache button
+            v_space(1)
+            clear_cache_button()
+
+        # Display two images side by side in the sidebar
         v_space(1)
-        st.image("assets/FBMN-STATS-GUIed_logo2.png", use_column_width=True)
+        col1, col2 = st.columns(2)
+        with col1:
+            st.markdown(
+                f'<a href="https://github.com/Functional-Metabolomics-Lab/FBMN-STATS" target="_blank">'
+                f'<img src="data:image/png;base64,{base64.b64encode(open("assets/FBMN-STATS-GUIed_logo2.png", "rb").read()).decode()}" width="130">'
+                '</a>',
+                unsafe_allow_html=True,
+            )
+        with col2:
+             st.markdown(
+                f'<a href="https://gnps2.org/homepage" target="_blank">'
+                f'<img src="data:image/png;base64,{base64.b64encode(open("assets/GNPS2_logo.png", "rb").read()).decode()}" width="150">'
+                '</a>',
+                unsafe_allow_html=True,
+            )
+            
+           # st.image("assets/GNPS2_logo.png", use_column_width=True)
+
         v_space(1)
-        st.image("assets/vmol-icon.png", use_column_width=True) 
+
+        #st.image("assets/vmol-icon.png", use_column_width=True)
+        st.markdown(
+                f'<a href="https://vmol.org/ " target="_blank">'
+                f'<img src="data:image/png;base64,{base64.b64encode(open("assets/vmol-icon.png", "rb").read()).decode()}" width="300">'
+                '</a>',
+                unsafe_allow_html=True,
+            )
         v_space(1)
+
         st.markdown("## Functional-Metabolomics-Lab")
         c1, c2, c3 = st.columns(3)
         c1.markdown(
@@ -72,20 +111,20 @@ def page_setup():
             unsafe_allow_html=True,
         )
         c2.markdown(
-            """<a href="https://www.youtube.com/@functionalmetabolomics">
+            """<a href="https://www.functional-metabolomics.com/">
             <img src="data:image/png;base64,{}" width="50">
             </a>""".format(
-                base64.b64encode(open("./assets/youtube-logo.png", "rb").read()).decode()
+                base64.b64encode(open("./assets/fmlab_logo_colored.png", "rb").read()).decode()
             ),
             unsafe_allow_html=True,
         )
         c3.markdown(
-            """<a href="https://twitter.com/func_metabo_lab">
+            """<a href="https://www.youtube.com/@functionalmetabolomics">
             <img src="data:image/png;base64,{}" width="50">
             </a>""".format(
-                base64.b64encode(open("./assets/x-logo.png", "rb").read()).decode()
+                base64.b64encode(open("./assets/youtube-logo.png", "rb").read()).decode()
             ),
-            unsafe_allow_html=True
+            unsafe_allow_html=True,
         )
 
 

assets/fmlab_logo_colored.png

@@ -53,7 +53,7 @@ def load_example():
     md = open_df("example-data/MetaData.txt").set_index("filename")
     return ft, md
 
-@st.cache_data
+
 def load_from_gnps(task_id, cmn=False):
 
     try: # GNPS2 will run here

src/common.py

@@ -5,7 +5,7 @@
 import skbio
 from scipy.spatial import distance
 
-
+@st.cache_data
 def permanova_pcoa(scaled, distance_matrix, attribute):
     # Create the distance matrix from the original data
     distance_matrix = skbio.stats.distance.DistanceMatrix(

src/fileselection.py

@@ -9,7 +9,6 @@
 from sklearn.metrics import classification_report
 from sklearn.metrics import confusion_matrix, accuracy_score
 
-@st.cache_data
 def run_random_forest(attribute, n_trees, random_seed=None):
     # initialize a log to print out in the app later
     log = ""

src/pcoa.py

@@ -133,3 +133,8 @@ def download_plotly_figure(fig, col=None, filename=""):
             file_name=filename,
             mime="application/png",
         )
+
+# Button to clear the cache
+if st.button("Clear Cache"):
+    st.cache_data.clear()
+    st.success("Cache cleared!")