import numpy as np import pandas as pd import datasets import streamlit as st from streamlit_cytoscapejs import st_cytoscapejs import networkx as nx st.set_page_config(layout='wide') # parse out gene_ids from URL query args to it's possible to link to this page query_params = st.query_params if "gene_ids" in query_params.keys(): input_gene_ids = query_params["gene_ids"] else: input_gene_ids = "CNAG_04365,CNAG_06468" # use "\n" as the separator so it shows correctly in the text area input_gene_ids = input_gene_ids.replace(",", "\n") if "coexp_score_threshold" in query_params.keys(): coexp_score_threshold = query_params["coexp_score_threshold"] else: coexp_score_threshold = "0.85" if "max_per_gene" in query_params.keys(): max_per_gene = query_params["max_per_gene"] else: max_per_gene = "25" st.markdown(""" # CryptoCEN Network **CryptoCEN** is a co-expression network for *Cryptococcus neoformans* built on 1,524 RNA-seq runs across 34 studies. A pair of genes are said to be co-expressed when their expression is correlated across different conditions and is often a marker for genes to be involved in similar processes. To Cite: O'Meara MJ, Rapala JR, Nichols CB, Alexandre C, Billmyre RB, Steenwyk JL, A Alspaugh JA, O'Meara TR CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair. PLoS Genet 20(2): e1011158. (2024) https://doi.org/10.1371/journal.pgen.1011158 * Code available at https://github.com/maomlab/CalCEN/tree/master/vignettes/CryptoCEN * Full network and dataset: https://huggingface.co/datasets/maomlab/CryptoCEN ## Plot a network for a set of genes Put a ``CNAG_#####`` gene_id, one one each row to seed the network """) h99_transcript_annotations = datasets.load_dataset( path = "maomlab/CryptoCEN", data_files = {"h99_transcript_annotations": "h99_transcript_annotations.tsv"}) h99_transcript_annotations = h99_transcript_annotations["h99_transcript_annotations"].to_pandas() top_coexp_hits = datasets.load_dataset( path = "maomlab/CryptoCEN", data_files = {"top_coexp_hits": "top_coexp_hits.tsv"}) top_coexp_hits = top_coexp_hits["top_coexp_hits"].to_pandas() col1, col2, col3 = st.columns(spec = [0.3, 0.2, 0.5]) with col1: input_gene_ids = st.text_area( label = "Gene IDs", value = f"{input_gene_ids}", height = 130, help = "CNAG Gene ID e.g. CNAG_04365") with col2: coexp_score_threshold = st.text_input( label = "Co-expression threshold [0-1]", value = f"{coexp_score_threshold}", help = "Default: 0.85") try: coexp_score_threshold = float(coexp_score_threshold) except: st.error(f"Co-expression threshold should be a number between 0 and 1, instead it is '{coexp_score_threshold}'") if coexp_score_threshold < 0 or 1 < coexp_score_threshold: st.error(f"Co-expression threshold should be a number between 0 and 1, instead it is '{coexp_score_threshold}'") max_per_gene = st.text_input( label = "Max per gene", value = f"{max_per_gene}", help = "Default: 25") try: max_per_gene = int(max_per_gene) except: st.error(f"Max per gene should be a number greater than 0, instead it is '{max_per_gene}'") if max_per_gene <= 0: st.error(f"Max per gene should be a number greater than 0, instead it is '{max_per_gene}'") ################################## # Parse and check the user input # ################################## seed_gene_ids = [] for input_gene_id in input_gene_ids.split("\n"): gene_id = input_gene_id.strip() if gene_id == "": continue else: seed_gene_ids.append(gene_id) neighbors = [] for seed_gene_id in seed_gene_ids: hits = top_coexp_hits[ (top_coexp_hits.gene_id_1 == seed_gene_id) & (top_coexp_hits.coexp_score > coexp_score_threshold)] if len(hits.index) > max_per_gene: hits = hits[0:max_per_gene] neighbors.append(hits) neighbors = pd.concat(neighbors) neighbor_gene_ids = list(set(neighbors.gene_id_2)) gene_ids = seed_gene_ids + neighbor_gene_ids gene_types = ['seed'] * len(seed_gene_ids) + ['neighbor'] * len(neighbor_gene_ids) cnag_ids = [] gene_products = [] descriptions = [] for gene_id in gene_ids: try: cnag_id = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id]["cnag_id"].values[0] gene_product = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id]["gene_product"].values[0] description = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id]["description"].values[0] except: st.error(f"Unable to locate cnag_id for Gene ID: '{gene_id}', it should be of the form 'cnag_#####'") cnag_id = None gene_product = None description = None cnag_ids.append(cnag_id) gene_products.append(gene_product) descriptions.append(description) node_info = pd.DataFrame({ "gene_index": range(len(gene_ids)), "gene_id" : gene_ids, "gene_type" : gene_types, "cnag_id": cnag_ids, "gene_product": gene_products, "description": description}) neighbors = neighbors.merge( right = node_info, left_on = "gene_id_1", right_on = "gene_id") neighbors = neighbors.merge( right = node_info, left_on = "gene_id_2", right_on = "gene_id", suffixes = ("_a", "_b")) ################################ # Use NetworkX to layout graph # ################################ # note I think CytoscapeJS can layout graphs # but I'm unsure how to do it through the streamlit-cytoscapejs interface :( st.write(neighbors) G = nx.Graph() for i in range(len(neighbors.index)): edge = neighbors.iloc[i] G.add_edge( edge["gene_index_a"], edge["gene_index_b"], weight = edge["coexp_score"]) layout = nx.spring_layout(G) node_color_lut = { "seed" : "#4866F0", # blue "neighbor" : "#F0C547" # gold } elements = [] singleton_index = 0 for i in range(len(node_info.index)): node = node_info.iloc[i] if node["gene_index"] in layout.keys(): layout_x = layout[node["gene_index"]][0] * 600 + 1500/2 layout_y = layout[node["gene_index"]][1] * 600 + 1500/2 else: layout_x = (singleton_index % 8) * 150 + 100 layout_y = np.floor(singleton_index / 8) * 50 + 30 singleton_index += 1 elements.append({ "data": { "id": node["gene_id"], "label": node["gene_product"] if node["gene_product"] is not None else node["gene_id"], "color": node_color_lut[node["gene_type"]]}, "position": { "x" : layout_x, "y" : layout_y}}) for i in range(len(neighbors.index)): edge = neighbors.iloc[i] elements.append({ "data" : { "source" : edge["gene_id_1"], "target" : edge["gene_id_2"], "width" : 20 if edge["coexp_score"] > 0.99 else 15 if edge["coexp_score"] > 0.96 else 10 if edge["coexp_score"] > 0.94 else 8 if edge["coexp_score"] > 0.89 else 5}}) with col3: st.text('') # help alignment with input box st.download_button( label = "Download as as TSV", data = neighbors.to_csv(sep ='\t').encode('utf-8'), file_name = f"CryptoCEN_network.tsv", mime = "text/csv") ########################################################## stylesheet = [ {"selector": "node", "style": { "width": 140, "height": 30, "shape": "rectangle", "label" : "data(label)", "labelFontSize": 100, 'background-color': 'data(color)', "text-halign": "center", "text-valign": "center", }}, {"selector": "edge", "style": { "width": "data(width)" }} ] st.title("ToxoCEN Network") clicked_elements = st_cytoscapejs( elements = elements, stylesheet = stylesheet, width = 1000, height= 1000, key = "1")