diff --git a/src/find_interaction_cluster/community_file_tools/perform_topgo_analysis.py b/src/find_interaction_cluster/community_file_tools/perform_topgo_analysis.py
index 3e92eb4fc3d828c7861d523ceb292b5446718ce5..345a87a13bd329d0d8e2d0b73ee31c23c35d6e49 100644
--- a/src/find_interaction_cluster/community_file_tools/perform_topgo_analysis.py
+++ b/src/find_interaction_cluster/community_file_tools/perform_topgo_analysis.py
@@ -7,10 +7,17 @@ Description: Perform a topGo analysis for each list of genes in the file \
produced by the script community_file_2_gene_list in results/clusters_gene_list
"""
+import math
from pathlib import Path
from typing import List
+import numpy as np
import pandas as pd
+import plotly.express as px
+import polars as pl
+
+from src.figures_utils.component_variance_figure import update_layout_fig
+from src.figures_utils.stacked_barplot_figures import get_color_pallette
from ..config import Config
@@ -30,14 +37,12 @@ def write_input_file(mfile: Path, folder: Path) -> List[Path]:
"""
input_files = []
df = pd.read_csv(mfile, sep="\t")
- df = df[-df["hg38_symbol"].isna()].copy()
+ df = df[-df["symbol"].isna()].copy()
for cluster in df["cluster"].unique():
tmp = df[df["cluster"] == cluster]
cl = cluster.replace(".", "_")
outfile = folder / f"{cl}.txt"
- tmp[["hg38_symbol"]].to_csv(
- outfile, sep="\t", index=False, header=False
- )
+ tmp[["symbol"]].to_csv(outfile, sep="\t", index=False, header=False)
input_files.append(outfile)
return input_files
@@ -53,9 +58,9 @@ def write_background(mfile: Path, folder: Path) -> Path:
outf = folder / "input_topgo"
outf.mkdir(exist_ok=True)
df = pd.read_csv(mfile, sep="\t")
- df = df[-df["hg38_symbol"].isna()].copy()
+ df = df[-df["symbol"].isna()].copy()
outfile = folder / "background.txt"
- ndf = pd.DataFrame({"hg38_symbol": df["hg38_symbol"].unique()})
+ ndf = pd.DataFrame({"symbol": df["symbol"].unique()})
ndf.to_csv(outfile, sep="\t", index=False, header=False)
return outfile
@@ -80,6 +85,158 @@ def execute_topgo(
sp.check_call(cmd, shell=True)
+def get_enrichment_files(output_folder: Path) -> list[Path]:
+ """
+ Get the enrichment files generated by topgo
+
+ :param output_folder: The output folder where the enrichment files are \
+ generated
+ :return: A list of Path objects representing the enrichment files
+ """
+ return list(output_folder.glob("**/*_genes_CC_*.txt"))
+
+
+def open_enrichment_file(outfile: Path) -> pl.DataFrame:
+ """
+ Open an enrichment file generated by topgo
+
+ :param outfile: The output file to open
+ :return: A DataFrame containing the enrichment data
+ """
+ df = pl.read_csv(
+ outfile,
+ separator="\t",
+ dtypes={
+ "GO.ID": pl.Utf8(),
+ "Term": pl.Utf8(),
+ "Annotated": pl.Int64(),
+ "Significant": pl.Int64(),
+ "Expected": pl.Float64(),
+ "fish": pl.Utf8(),
+ "pvalue": pl.Utf8(),
+ "padj": pl.Utf8(),
+ },
+ ).with_columns(
+ community=pl.lit(outfile.parent.name),
+ fish=pl.col("fish").str.replace("< ", "").cast(pl.Float64()),
+ pvalue=pl.col("pvalue").str.replace("< ", "").cast(pl.Float64()),
+ padj=pl.col("padj").str.replace("< ", "").cast(pl.Float64()),
+ )
+ return df
+
+
+def load_many_files(outfiles: list[Path]) -> pl.DataFrame:
+ """Load many enrichment files generated by topgo
+
+ :param outfiles: A list of Path objects representing the enrichment files
+ :return: A DataFrame containing the enrichment data
+ """
+ dfs = [open_enrichment_file(outfile) for outfile in outfiles]
+ return pl.concat(dfs)
+
+
+def generate_figure(
+ df: pl.DataFrame,
+ output: Path,
+ go_term: str,
+ term: str,
+ cummunities_name: str,
+) -> None:
+ """Generate a figure for a given GO term
+
+ :param df: The DataFrame containing the enrichment data
+ :param output: The output path for the figure
+ :param go_term: The GO term to generate the figure for
+ :param term: The term description
+ :param cummunities_name: The name of the clusters studied
+ """
+
+ fig = px.scatter(
+ df,
+ x="mlog10padj",
+ y="log2fc",
+ color="SPIN" if "SPIN" in df.columns else "community",
+ size="Significant",
+ hover_data=["padj", "log2fc", "community", "Significant"],
+ title=f"GO Term: {go_term} - {term} - {cummunities_name}",
+ color_discrete_sequence=df["color"].tolist(),
+ )
+ fig = update_layout_fig(fig)
+ fig.update_layout(font=dict(size=15))
+ fig.add_hline(
+ y=0, line_dash="dash", line_color="black", line_width=2, layer="below"
+ )
+ fig.add_vline(
+ x=-math.log10(0.05),
+ line_dash="dash",
+ line_color="black",
+ line_width=2,
+ layer="below",
+ )
+ fig.write_html(output)
+
+
+def add_color_columns(df: pd.DataFrame, order: list[str]) -> pd.DataFrame:
+ """
+ Add color columns to the dataframe based on the community column
+
+ :param df: The dataframe to add color columns to
+ :param order: The order of the communities
+ :return: The dataframe with color columns added
+ """
+ if not df[df["community"].str.contains("Speckle")].empty:
+ df["SPIN"] = [x[-1] for x in df["community"].str.split("_")]
+ tmp = [x.split("_")[-1] for x in order]
+ indexes = np.unique(tmp, return_index=True)[1]
+ order = [order[i] for i in sorted(indexes)]
+ c_dic = dict(zip(order, get_color_pallette("turbo", order)))
+ df["color"] = df["community"].map(c_dic)
+ return df
+
+
+def create_figures(outfolder: Path, mfile: Path) -> None:
+ """
+ Generate the enrichment scatter plot for each GO term
+
+ :param outfolder: The output folder where the figures will be saved
+ :param mfile: A file containing cluster with hg38 names
+ """
+ order = pd.read_csv(mfile, sep="\t")["cluster"].unique().tolist()
+ outf = outfolder / "scatter_fig"
+ outf.mkdir(exist_ok=True)
+ enri_file = get_enrichment_files(outfolder)
+ df = load_many_files(enri_file)
+ df = df.with_columns(
+ log2fc=(pl.col("Significant") / pl.col("Expected")).log(base=2),
+ mlog10padj=pl.col("padj").log(base=10) * -1,
+ )
+ df.write_csv(outfolder / f"GO_{mfile.stem}_enrichment.csv", separator="\t")
+ gt = {
+ k: v[0]
+ for k, v in df.filter(pl.col("padj") < 0.05)
+ .select(["GO.ID", "Term"])
+ .unique(subset="GO.ID")
+ .unique()
+ .rows_by_key("GO.ID")
+ .items()
+ }
+ df = df.filter(pl.col("GO.ID").is_in(gt.keys()))
+ df.write_csv(
+ outfolder / f"GO_{mfile.stem}_filtered_enrichment.csv", separator="\t"
+ )
+ df = df.to_pandas()
+ df = add_color_columns(df, order)
+ df["community"] = pd.Categorical(
+ df["community"], categories=order, ordered=True
+ )
+ df = df.sort_values("community")
+ for go_term, term in gt.items():
+ tmp = df[df["GO.ID"] == go_term]
+ nterm = term.replace(" ", "_").replace("/", "-")[:20]
+ outfile = outf / f"{go_term}_{nterm}_{mfile.stem}.html"
+ generate_figure(tmp, outfile, go_term, term, mfile.stem)
+
+
def execute_cmds(mfile: Path, top: int = 20) -> None:
"""
Execute topgo for each cluster defined in mfile
@@ -97,6 +254,7 @@ def execute_cmds(mfile: Path, top: int = 20) -> None:
cfolder = folder / cinput.stem
cfolder.mkdir(exist_ok=True)
execute_topgo(cinput, background, cfolder, top)
+ create_figures(folder, mfile)
@lp.parse(gene_list="file")