second

app.py

@@ -1,5 +1,352 @@
+# streamlit_app.py
 import streamlit as st
+import pandas as pd
+pd.options.mode.chained_assignment = None  # default='warn'
+import numpy as np
+from io import BytesIO
+import os
+import sys
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# relative imports
+ROOT = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(ROOT, "./src/"))
+from agstyler import PINLEFT, PRECISION_TWO, draw_grid
 
+st.set_page_config(
+    page_title="Fragment-Protein interactions in Chemical Proteomics Screening", 
+    page_icon=":home:",
+    layout="wide", # "centered",
+    initial_sidebar_state="expanded"
+)
+
+st.markdown("""
+  <style>
+    .css-13sdm1b.e16nr0p33 {
+      margin-top: -75px;
+    }
+  </style>
+""", unsafe_allow_html=True)
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            #header {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+pIdDf = pd.read_csv(os.path.join(ROOT, "../data/general/proteinNames4.tsv"), sep="\t")
+
+pId = pIdDf['UniProtID'].values
+pIdDes = pIdDf['Description'].values
+
+def applyFilters(df, pFil, pAdjFil, hitFil):
+  if pFil != 'no filter':
+    if pFil == '< 0.05':
+      df = df[df['ml10p'] > 1.30103]
+    else:
+      df = df[df['ml10p'] > 2]
+      
+  if pAdjFil != 'no filter':
+    if pAdjFil == '< 0.05':
+      df = df[df['ml10adjP'] > 1.30103]
+    elif pAdjFil == '< 0.1':
+      df = df[df['ml10adjP'] > 1]
+    else:
+      df = df[df['ml10adjP'] > 0.60206]
+      
+  if hitFil != 'no filter':
+    if hitFil == 'Low':
+      df = df[df['mdfClass'] >= 1]
+    elif hitFil == 'Medium (hits)':
+      df = df[df['mdfClass'] >= 2]
+    elif hitFil == 'Low (hits)':
+      df = df[df['mdfClass'] >= 1]
+    else:
+      df = df[df['mdfClass'] == 3]
+      
+  return df
+
+def getVarText(df):
+  if (len(df.index)) > 0:
+    bestProt = df["geneName"].values[0]
+    numProtHitss = len(df.index)
+    df.index = np.arange(1,len(df)+1)
+    protList = df.index[df["accession"]==myPid].tolist()
+    if len(protList) > 0:
+      protRank = protList[0]
+      varText1 = "hit rank is"
+      varText2 = "is best"
+    else:
+      varText1 = "is not a hit"
+      protRank = ""
+      varText2 = "protein is best"
+    del protList
+  else:
+    bestProt = "No "
+    numProtHitss = 0
+    varText1 = "is not a hit"
+    protRank = ""
+    varText2 = ""
+  return [bestProt, numProtHitss, protRank, varText1, varText2]
+
+
+st.sidebar.title("Fragment-Protein Interactions")
+st.title("Chemical Proteomics Screening")
+
+help_input3='''
+    
+    use **:blue[UniProt Accession]**, Short Gene Name(s) or Protein Description to search\n
+    **Tip**:\n
+    To change selected protein, **:red[NO]** need to select whole existing term, delete and type new.\n
+    :blue[Just start to type new protein, old text will be automatically cleared]'''
+
+pIdIndex = st.sidebar.selectbox(label = "Select Protein", help = help_input3, options = range(len(pIdDes)), format_func= lambda x: pIdDes[x], index= 1706)
+
+myPid = pId[pIdIndex]
+
+intDfOri = pd.read_csv(os.path.join(ROOT, "../data/general/finalScreen.tsv"), sep="\t")
+fpDf = pd.read_csv(os.path.join(ROOT, "../data/general/finalFp.tsv"), sep="\t")
+intDf = intDfOri[intDfOri["accession"]==myPid]
+
+if len(intDf) == 0:
+  st.sidebar.write("We did **:red[not]** detect selected protein interacting with any fragment in our screen, try another protein")
+else:
+  selectedGeneName = intDf["geneName"].values[0]
+  tempDf = applyFilters(intDf, '< 0.05', '< 0.25', 'Medium (hits)')
+  if (len(tempDf.index)) > 0:
+    tempDf = tempDf.sort_values(by=['protHits', 'l2fc'], ascending=[True, False])
+    bestFrag = tempDf["fragId"].values[0]
+    top5Frags = tempDf["fragId"].values[0:5]
+    numLigaHits = len(tempDf.index) # numLigaHits is already present in base input table
+    varText3 = "is best"
+  else:
+    bestFrag = "No"
+    varText3 = ""
+    numLigaHits = 0
+    
+######## Screening Protein Centric View ############      
+
+  st.write("**Selected Protein**: ", pIdDes[pIdIndex])
+  st.markdown("""---""")
+
+  st.subheader(f"First generation fragments (Gen1) that enrich **:blue[{selectedGeneName}]** over background")
+  
+  numInt = len(intDf.index)
+  st.write(f"**:blue[{numInt}]** (out of 407 screened) Gen1 fragments enrich **{selectedGeneName}**. **:blue[{numLigaHits}]**/{numInt} fragments are labelled as **hits** by applying **medium** filter Set **(:blue[fS])**. **:blue[{bestFrag}]** {varText3} **hit**.")
+
+  if (numLigaHits/407)>0.1:
+    hitRatio = np.round((numLigaHits/407)*100, 1) 
+    st.write(f"**:blue[{selectedGeneName}]** is a **:red[promiscuous]** protein (**hit**/enriched ratio is **:red[{hitRatio}]**%).")
+
+  col1, col2, col3, colX, colY, colZ = st.columns(6)
+  with col1:
+    pFilter = st.selectbox(label = "*P* Value", help = "Select threshold for signifiance", options = ('< 0.05', 'no filter', '< 0.01'))
+  with col2:
+    pAdjFilter = st.selectbox(label = "adjusted *P* Value", help = "Select threshold for signifiance", options = ('< 0.25', '< 0.1', 'no filter', '< 0.05'))
+  with col3:
+    help_input='''
+    
+    **:blue[0]**.  no filter\n
+    **:blue[1]**.  Low Confidence: Fc > 1,   Median > 1,   p < 0.05, adj.p < 0.25, Rank < 500\n
+    **:blue[2]**.  Medium confidence ('**:blue[hits]**'): Fc > 2.3, Median > 1,   p < 0.05, adj.p < 0.25, Rank < 500\n
+    **:blue[3]**.  High Confidence (also '**:blue[hits]**'):  Fc > 2.3, Median > 2.3, p < 0.01, adj.p < 0.1,  Rank < 500'''
+    mdfClass = st.selectbox(label = "filter Set (**:blue[fS]**)", help = help_input, options = ('Medium (hits)', 'no filter', 'Low', 'High (hits)'))
+    
+  if len(tempDf.index) == 0:
+    st.write("**:red[No]** data to display with selected filters. Applied **:blue[no filter]**")
+    intDf = applyFilters(intDf, 'no filter', 'no filter', 'no filter')
+    
+  else:
+    intDf = applyFilters(intDf, pFilter, pAdjFilter, mdfClass)
+
+  del tempDf
+  
+  intDf = intDf.sort_values(by=['protHits', 'l2fc'], ascending=[True, False])
+    
+  col4, col5  = st.columns(2)
+  with col4:
+    formatter = {
+      'fragId': ('Fragment', {**PINLEFT, 'width': 10}),
+      'l2fc': ('Fc(log2)', {**PRECISION_TWO, 'width': 15}),
+      'l2fcM': ('Fc Median adjusted', {**PRECISION_TWO, 'width': 25}),
+      'protHits': ('# Protein Hits', {'width': 15}),
+      'mdfClass': ('fS', {'width': 10})
+    }
+    data = draw_grid(intDf, formatter=formatter, fit_columns=True, selection='none', max_height=340)
+  with col5:
+    st.image(os.path.join(ROOT, "../assets/proteinCentric/") + myPid + ".png")
+    
+  fragId = st.sidebar.selectbox(label = "Select Gen1 Fragment", options = intDf["fragId"])
+  intDf2 = intDfOri[intDfOri["fragId"]==fragId]
+  
+############ Screening Fragment Centric ###############################
+  
+  st.subheader(f"Proteins enriched by **:blue[{fragId}]**")
+  
+  tempDf2 = intDfOri[intDfOri["fragId"]==fragId]
+  numProtDetected = len(tempDf2.index)
+  tempDf2 = applyFilters(tempDf2, '< 0.05', '< 0.25', 'Medium (hits)')
+
+  tempDf2 = tempDf2.sort_values(by=['ligHits', 'l2fc'], ascending=[True, False])
+  [bestProt, numProtHits, protRank, varText, varText2] = getVarText(tempDf2)
+  
+  if len(tempDf2.index) == 0:
+    intDf3 = applyFilters(intDf2, 'no filter', 'no filter', 'no filter')
+    
+  else:
+    intDf3 = applyFilters(intDf2, pFilter, pAdjFilter, mdfClass)
+  
+  intDf3 = intDf3.sort_values(by=['ligHits', 'l2fc'], ascending=[True, False])
+    
+  st.sidebar.image(os.path.join(ROOT, "../assets/fragFiguresSingle/") + fragId + ".png")
+  
+  st.write(f"**:blue[{numProtDetected}]** proteins were enriched by fragment **{fragId}** (Fc compared to **CRF** control). **:blue[{numProtHits}]** of those proteins were labelled as **hits** by applying **medium** filter Set **(:blue[fS])**. **:blue[{bestProt}]** {varText2} **hit**. **:blue[{selectedGeneName}]** {varText} **:blue[{protRank}]**.")
+  
+  if (numProtHits/numProtDetected)>0.05:
+    fragHitRatio = np.round((numProtHits/numProtDetected)*100, 1) 
+    st.write(f"**:blue[{fragId}]** is **:red[promiscuous]** fragment (**hit**/enriched ratio is **:red[{fragHitRatio}]**%).")
+  
+  col6, col7  = st.columns(2)
+  with col6:
+    st.image(os.path.join(ROOT, "../assets/ligandVolcanoPlots/") + fragId + ".png")
+  with col7:
+    if len(tempDf2.index) == 0:
+      st.write("**:red[No]** data to display with selected filters. Applied **:blue[no filter]**")
+    formatter = {
+      'accession': ('Protein', {**PINLEFT, 'width': 15}),
+      'geneName': ('Gene', {**PINLEFT, 'width': 15}),
+      'l2fc': ('Fc(log2)', {**PRECISION_TWO, 'width': 15}),
+      'l2fcM': ('Fc Median adjusted', {**PRECISION_TWO, 'width': 25}),
+      'ligHits': ('# Fragment Hits', {'width': 15}),
+      'mdfClass': ('fS', {'width': 10})
+    }
+    data = draw_grid(
+      intDf3, formatter=formatter, fit_columns=True, selection='none', max_height=340)
+  if not isinstance(protRank, str):
+    if protRank < 5:
+      st.subheader(f"**:blue[{fragId}-{selectedGeneName}]** interaction: :first_place_medal:")
+      st.write(f"**:blue[{fragId}]** is in top 5 **Fragment hits** for **{selectedGeneName}**. **:blue[{selectedGeneName}]** is in top 5 **Protein hits** for **{fragId}**.")
+
+  del tempDf2
+############# Fingerprinting / Elaborates Data ######################
+
+  gen2List = ["C027", "C028", "C044", "C046", "C064", "C115", "C127", "C160", "C179", "C186", "C197", "C219", "C240", "C270", "C275", "C303", "C310", "C320", "C378", "C391"]
+  if fragId in gen2List:
+    st.markdown("""---""")
+    # st.sidebar.markdown("""---""")
+    
+############ Elaborates Protein Centric View ##########################
+    st.subheader(f"Second generation fragments (Gen2) of **:blue[{fragId}]** that compete **:blue[{selectedGeneName}]**")
+
+    gen1Df = fpDf[fpDf["gen1Lig"]==fragId]
+    
+    numGen2Ligs = len(np.unique(gen1Df['fragId']))
+    
+    temp4Df = gen1Df[gen1Df["accession"]==myPid]
+    temp4Df = temp4Df[temp4Df["mdfClass"]>=1]
+    # temp4Df = temp4Df.sort_values(by='l2fc', ascending=True)
+    temp4Df = temp4Df.sort_values(by='l2fc', ascending=True)
+    
+    sidebarList1 = temp4Df["fragId"]
+
+    if len(temp4Df.index)>0:
+      bestGen2Lig = temp4Df['fragId'].values[0]
+      varText5 = "is best Gen2 fragment hit."
+    else:
+      bestGen2Lig = ""
+      varText5 = ""
+    
+    st.write(f"**:blue[{numGen2Ligs}]** Gen2  fragments were screened in **competition** experiments against Gen1 fragment **{fragId}**. **:blue[{len(temp4Df.index)}]**/{numGen2Ligs} Gen2 fragments of **{fragId}** pass **low** filter Set (**:blue[fS2]**).")
+    # **:blue[{bestGen2Lig}]** {varText5}")
+    # **compete** **:blue[{selectedGeneName}]** after applying 
+    
+    formatter = {
+      'fragId': ('Gen2', {**PINLEFT, 'width': 10}),
+      'l2fc': ('Fc(log2)', {**PRECISION_TWO, 'width': 10}),
+      'l2fcM': ('Fc Median adjusted', {**PRECISION_TWO, 'width': 20}),
+      'protHits': ('# Gen2 Protein Hits', {'width': 15}),
+      'mdfClass': ('fS2', {'width': 10})
+    }
+    
+    col10, col11 = st.columns(2)
+    with col10:
+      st.write(f":blue[Hits] (fS2 > 0)")
+      if len(temp4Df.index)>0:
+        data = draw_grid(
+          temp4Df, formatter=formatter, fit_columns=True, selection='none')
+      
+    temp4Df = gen1Df[gen1Df["accession"]==myPid]
+    temp4Df = temp4Df[temp4Df["mdfClass"] < 1]
+    temp4Df = temp4Df.sort_values(by='l2fc', ascending=True)
+  
+    sidebarList2 = temp4Df["fragId"]
+    
+    with col11:
+      st.write(f":orange[not] Hits (fS2 = 0)")
+      data = draw_grid(
+        temp4Df, formatter=formatter, fit_columns=True, selection='none')
+    
+    temp4Df = gen1Df[gen1Df["accession"]==myPid]
+    temp4Df = temp4Df.sort_values(by='l2fc', ascending=True)
+    temp4Df = temp4Df.sort_values(by=['mdfClass', 'l2fc'], ascending=[False, True])
+
+    sideBarList = pd.concat([sidebarList1, sidebarList2], sort=False)
+    
+############ Elaborates Side Bar Selection ##########################      
+
+    # gen2Id = st.sidebar.selectbox(label = "Select Gen2 Fragment", options = temp4Df["fragId"])
+    gen2Id = st.sidebar.selectbox(label = "Select Gen2 Fragment", options = sideBarList)
+    st.sidebar.image(os.path.join(ROOT, "../assets/fragFiguresSingle/") + gen2Id + ".png")
+    
+############ Elaborates Fragment Centric View ##########################      
+
+    st.subheader(f"Proteins competed by **:blue[{gen2Id}]**")
+    
+    gen2Df = gen1Df[gen1Df["fragId"]==gen2Id]
+    
+    tempDf3 = applyFilters(gen2Df, '< 0.05', '< 0.25', 'Low')
+    tempDf3 = tempDf3.sort_values(by=['ligHits', 'l2fc'], ascending=[True, True])
+    
+    [bestProt2, numProtHits2, protRank2, varText3, varText4] = getVarText(tempDf3)
+    
+    st.write(f"**:blue[{len(gen2Df.index)}]** proteins were reduced in **:blue[{gen2Id}] competition** experiment (Fc compared to **{fragId}** control). **:blue[{numProtHits2}]** of those proteins are labelled as **hits** by applying **low** filter Set **(:blue[fS2])**. **:blue[{bestProt2}]** {varText4} **hit**. **:blue[{selectedGeneName}]** {varText3} **:blue[{protRank2}]**.")
+    
+    col1, col2, col3, colX, colY, colZ = st.columns(6)
+    with col1:
+      pFilterFP = st.selectbox(label = "*P* Value", help = "Select threshold for signifiance", options = ('< 0.05', 'no filter', '< 0.01'), key = 'pFilterFP')
+    with col2:
+      pAdjFilterFP = st.selectbox(label = "adjusted *P* Value", help = "Select threshold for signifiance", options = ('< 0.25', 'no filter', '< 0.1', '< 0.05'), key = 'pAdjFilterFP')
+    with col3:
+      help_input2='''
+      
+      **:blue[0]**.  no filter\n
+      **:blue[1]**.  Low Confidence ('**:blue[hits]**'): Fc < -1,   p < 0.05, adj.p < 0.25, Rank < 500\n
+      **:blue[2]**.  Medium confidence (also '**:blue[hits]**'): Fc < -1.65, p < 0.05, adj.p < 0.25, Rank < 500\n
+      **:blue[3]**.  High Confidence (also '**:blue[hits]**'):  Fc < -2.3, p < 0.01, adj.p < 0.1,  Rank < 500'''
+      mdfClassFP = st.selectbox(label = "Gen2 Fragment filter Set (**:blue[fS2]**)", help = help_input2, options = ('Low (hits)', 'Medium (hits)', 'no filter', 'High (hits)'), key = 'mdfClassFP')
+    
+    if len(tempDf3.index) == 0:
+      st.write("**:red[No]** data to display with selected filters. Applied **:blue[no filter]**")
+      gen2Df2 = applyFilters(gen2Df, 'no filter', 'no filter', 'no filter')
+      
+    else:  
+      gen2Df2 = applyFilters(gen2Df, pFilterFP, pAdjFilterFP, mdfClassFP)
+      
+    gen2Df2 = gen2Df2.sort_values(by=['ligHits', 'l2fc'], ascending=[True, True])
+    
+    col8, col9  = st.columns(2)
+    with col8:
+      formatter = {
+      'accession': ('Protein', {**PINLEFT, 'width': 15}),
+      'geneName': ('Gene', {**PINLEFT, 'width': 15}),
+      'l2fc': ('Fc(log2)', {**PRECISION_TWO, 'width': 10}),
+      'l2fcM': ('Fc Median adjusted', {**PRECISION_TWO, 'width': 20}),
+      'ligHits': ('# Gen2 Fragment Hits', {'width': 15}),
+      'mdfClass': ('fS2', {'width': 10})
+      }
+      data = draw_grid(
+        gen2Df2, formatter=formatter, fit_columns=True, selection='none', max_height=340)
+    with col9:
+      st.image(os.path.join(ROOT, "../assets/gen2VolcanoPlots/") + gen2Id + ".png")