From 1274e9394a4e903f016a749fb772357f1d41779e Mon Sep 17 00:00:00 2001
From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com>
Date: Tue, 1 Oct 2024 16:19:15 +0200
Subject: [PATCH] update app
---
.streamlit/config.toml | 1 -
AMR-KG_Database.py | 97 +-
amrkg_chemspace.html | 30 +-
.../combined_bioassay_data.tsv | 148406 +++++++--------
pages/1_Chemical_Space_Exploration.py | 25 +-
pages/2_Model_Prediction.py | 21 +-
6 files changed, 74343 insertions(+), 74237 deletions(-)
rename data/{processed => }/combined_bioassay_data.tsv (72%)
diff --git a/.streamlit/config.toml b/.streamlit/config.toml
index 800ada1..4efd405 100644
--- a/.streamlit/config.toml
+++ b/.streamlit/config.toml
@@ -1,5 +1,4 @@
[theme]
base="light"
primaryColor="#7db5d0"
-secondaryBackgroundColor="#1e78bc"
textColor="#080000"
\ No newline at end of file
diff --git a/AMR-KG_Database.py b/AMR-KG_Database.py
index 77fd1b6..887cf1e 100644
--- a/AMR-KG_Database.py
+++ b/AMR-KG_Database.py
@@ -1,3 +1,4 @@
+import datetime
import pandas as pd
from collections import defaultdict
import streamlit as st
@@ -30,7 +31,7 @@
st.sidebar.image("data/COMBINE_logo.jpg")
st.markdown(
- "AMR-KG Database
",
+ "AMR-KG Database
",
unsafe_allow_html=True,
)
st.markdown(
@@ -68,10 +69,10 @@
help="Information on the data in AMR-KG.",
)
-st.write(
+st.markdown(
"Antimicrobial Resistant Knowledge Graph (AMR-KG) is an exhaustive data warehouse of experimentally validated antibacterial chemicals \
covering Gram-positive, Gram-negative, acid-fast bacteria and fungi. The construction of the AMR-KG involved collecting \
- minimum inhibitory concentration (MIC) data from three different public data resources:"
+ [minimum inhibitory concentration (MIC)](http://purl.obolibrary.org/obo/ARO_3004370) data from three different public data resources:"
)
@@ -82,7 +83,7 @@
container.write("### [ChEMBL](https://www.ebi.ac.uk/chembl/)")
container.write(
- "ChEMBL is a manually curated database of bioactive molecules with drug-like properties. \
+ "ChEMBL (v.34) is a manually curated database of bioactive molecules with drug-like properties. \
It brings together chemical and bioactivity to aid the translation of experimental information into effective new drugs.",
)
@@ -109,7 +110,7 @@
divider="orange",
help="Stats on the underlying data.",
)
-df = pd.read_csv("data/processed/combined_bioassay_data.tsv", sep="\t")
+df = pd.read_csv("data/combined_bioassay_data.tsv", sep="\t")
def get_base_stats():
@@ -138,19 +139,20 @@ def get_base_stats():
_,
_,
_,
+ gp_class,
+ gn_class,
+ fungi_class,
+ af_class,
best_class,
) = row
- if best_class == "gram-positive":
+ if pd.notna(gp_class):
pchem_dist_dict["gram-positive"].append(gram_pos)
-
- elif best_class == "gram-negative":
+ if pd.notna(gn_class):
pchem_dist_dict["gram-negative"].append(gram_neg)
-
- elif best_class == "fungi":
+ if pd.notna(fungi_class):
pchem_dist_dict["fungi"].append(fungi)
-
- elif best_class == "acid-fast":
+ if pd.notna(af_class):
pchem_dist_dict["acid-fast"].append(acid_fast)
return (chembl_cmpds, coadd_cmpds, spark_cmpds), pchem_dist_dict
@@ -162,12 +164,22 @@ def get_base_stats():
plt.subplot(2, 2, 1)
plt.bar(
- ["ChEMBL", "CO-ADD", "SPARK"],
- [len(chembl_cmpds), len(coadd_cmpds), len(spark_cmpds)],
+ [
+ "ChEMBL",
+ "SPARK",
+ "CO-ADD",
+ ],
+ [len(chembl_cmpds), len(spark_cmpds), len(coadd_cmpds)],
)
-plt.title("Compound distribution across resources")
+plt.title("Compound distribution across resources", fontsize=15, fontweight="bold")
plt.yscale("log")
-plt.ylabel("Number of compounds")
+# display the number of compounds on the bars
+for i, v in enumerate([len(chembl_cmpds), len(spark_cmpds), len(coadd_cmpds)]):
+ plt.text(i, v + 10, str(v), ha="center", va="bottom")
+plt.ylabel("Number of deduplicated compounds", fontsize=12)
+plt.xlabel("Data source", fontsize=12)
+plt.xticks(fontsize=12)
+plt.yticks(fontsize=12)
plt.subplot(2, 2, 2)
plt.hist(
@@ -178,10 +190,13 @@ def get_base_stats():
)
plt.hist(pchem_dist_dict["fungi"], alpha=0.5, color="green", label="fungi")
plt.hist(pchem_dist_dict["acid-fast"], alpha=0.5, color="orange", label="acid-fast")
-plt.legend()
-plt.title("Distribution of pChEMBL values")
-plt.ylabel("Number of compounds")
-plt.xlabel("pChEMBL value")
+plt.axvline(5, color="red", linestyle="--", label="pChEMBL threshold")
+plt.legend(title="Pathogen class", fontsize=12)
+plt.title("Distribution of pChEMBL values", fontsize=15, fontweight="bold")
+plt.ylabel("Number of deduplicated compounds", fontsize=12)
+plt.xticks(fontsize=12)
+plt.yticks(fontsize=12)
+plt.xlabel("pChEMBL value", fontsize=12)
st.pyplot(fig)
@@ -189,7 +204,7 @@ def get_base_stats():
st.header(
":arrow_down: Data Download",
divider="orange",
- help="Downloading all the data in the AMR-KG.",
+ help="Downloading the compound-pathogen data in AMR-KG.",
)
@@ -200,11 +215,49 @@ def convert_df(df):
csv = convert_df(df)
st.write("The data files contains the following columns in the tab-seperated manner:")
+st.markdown(
+ """
+ * `compound_inchikey` - InChI key of the compound \n
+ * `compound_smiles` - SMILES representation of the compound \n
+ * `compound_source` - Source database of the compound \n
+ * `gram_positive` - pMIC value for compound activity against Gram-positive pathogen \n
+ * `gram_negative` - pMIC value for compound activity against Gram-negative pathogen \n
+ * `fungi` - pMIC value for compound activity against fungi \n
+ * `acid_fast` - pMIC value for compound activity against acid-fast pathogen \n
+ * `chemical_class` - Chemical class disitribution of the compounds based on NPClassifier \n
+ * `compound_superclass` - Superclass of the compound based on NPClassifier \n
+ * `compound_pathway` - Pathway of the compound based on NPClassifier \n
+ * `gram_positive_label` - Indicating whether a compound is active or inactive for a Gram-positive pathogen based on pChEMBL threshold (i.e. 5) \n
+ * `gram_negative_label` - Indicating whether a compound is active or inactive for a Gram-negative pathogen based on pChEMBL threshold (i.e. 5) \n
+ * `fungi_label` - Indicating whether a compound is active or inactive for a Fungi pathogen based on pChEMBL threshold (i.e. 5) \n
+ * `acid_fast_label` - Indicating whether a compound is active or inactive for a Acid-fast pathogen based on pChEMBL threshold (i.e. 5) \n
+ * `best_class` - Pathogen class of the compound based on the highest pChEMBL values \n
+ """
+)
st.dataframe(df.head(3))
st.download_button(
"Press to Download", csv, "amrkg_data_dump.tsv", "text/tsv", key="download-tsv"
)
# Publucation note
-with st.expander("Check out our publication for more deatils "):
+with st.expander(
+ "If you have found our resource or model useful in your work, please consider citing us: "
+):
st.write("""Manuscript in preparation. Please check back soon for more details.""")
+
+
+# last updated
+date = datetime.datetime.now().strftime("%d-%m-%Y %H:%M:%S")
+st.markdown(
+ f"Last updated: {date}
",
+ unsafe_allow_html=True,
+)
+
+# footer with text and green background
+st.markdown(
+ "