adding figure that creates old and new n_reads_clade

figures/clade-count-comparison.py

@@ -0,0 +1,120 @@
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from collections import defaultdict
+
+
+dashboard = os.path.expanduser("~/code/mgs-pipeline/dashboard/")
+
+
+BIOPROJECT_DIR = "bioprojects"
+
+if not os.path.exists(f"../bioprojects"):
+    os.mkdir("../bioprojects")
+
+
+TARGET_STUDY_METADATA = {
+    "Bengtsson-Palme 2016": ["PRJEB14051"],
+    "Munk 2022": [
+        "PRJEB13831",
+        "PRJEB27054",
+        "PRJEB27621",
+        "PRJEB40798",
+        "PRJEB40815",
+        "PRJEB40816",
+        "PRJEB51229",
+    ],
+    "Brinch 2020": ["PRJEB13832", "PRJEB34633"],
+    "Ng 2019": ["PRJNA438174"],
+    "Maritz 2019": ["PRJEB28033"],
+    "Brumfield 2022": ["PRJNA812772"],
+    "Yang 2020": ["PRJNA645711"],
+    "Spurbeck 2023": ["PRJNA924011"],
+    "CC 2021": ["PRJNA661613"],
+    "Rothman 2021": ["PRJNA729801"],
+}
+
+
+sample_files = [
+    "hv_clade_counts_new",
+    "hv_clade_counts",
+]
+
+
+def read_clade_counts(file_path):
+    df = pd.read_csv(file_path, sep="\t")
+    sub_df = df[(df["taxid"] == 10239)]["n_reads_clade", "sample"]
+    dict = {}
+    for reads, sample in sub_df.itertuples():
+        dict[sample] = reads
+    return dict
+
+
+def collect_data():
+    data = defaultdict()
+    for study, bioprojects in TARGET_STUDY_METADATA.items():
+        for bioproject in bioprojects:
+            study_author = study.split()[0]
+            bioproject_dir = f"../{BIOPROJECT_DIR}/{study_author}-{bioproject}"
+
+            new_file = os.path.join(bioproject_dir, "hv_clade_counts_new.tsv")
+            old_file = os.path.join(bioproject_dir, "hv_clade_counts.tsv")
+            with open(new_file, "r") as f:
+                for line in f:
+                    (
+                        taxid,
+                        name,
+                        rank,
+                        parent_taxid,
+                        sample,
+                        n_reads_direct,
+                        n_reads_clade,
+                    ) = line.strip().split("\t")
+                    if taxid == "10239":  # Check if taxid is 10239 (Viruses)
+                        data[sample] = [int(n_reads_clade)]
+            with open(old_file, "r") as f:
+                for line in f:
+                    (
+                        taxid,
+                        name,
+                        rank,
+                        parent_taxid,
+                        sample,
+                        n_reads_direct,
+                        n_reads_clade,
+                    ) = line.strip().split("\t")
+                    if taxid == "10239":  # Check if taxid is 10239 (Viruses)
+                        data[sample].append(int(n_reads_clade))
+    new_counts = []
+    old_counts = []
+    for key in data.keys():
+        new_counts.append(data[key][0])
+        old_counts.append(data[key][1])
+
+    return new_counts, old_counts
+
+
+def plot_comparison():
+    plt.figure(figsize=(10, 10))
+    sns.scatterplot(x=old_counts, y=new_counts)
+    plt.xlabel("n_reads_clade (old)")
+    plt.ylabel("n_reads_clade (new)")
+    plt.title(
+        "Comparison of n_reads_clade between old and new hv_clade_counts"
+    )
+
+    # Add diagonal line
+    # max_val = max(data["old"].max(), data["new"].max())
+    # plt.plot([0, max_val], [0, max_val], "r--")
+
+    plt.xscale("log")
+    plt.yscale("log")
+    plt.tight_layout()
+    plt.savefig("hv_clade_counts_comparison.png", dpi=300)
+    plt.close()
+
+
+new_counts, old_counts = collect_data()
+
+plot_comparison()