diff --git a/assets/assess_panel/assess_panel.py b/assets/assess_panel/assess_panel.py
index eb6aae5e..620f7fc9 100644
--- a/assets/assess_panel/assess_panel.py
+++ b/assets/assess_panel/assess_panel.py
@@ -9,7 +9,7 @@
import click
-sys.path.append("../../bin")
+sys.path.append("../../bin/")
from utils_context import triplet_context_iterator
diff --git a/bin/add_subgenicregions.py b/bin/add_subgenicregions.py
index 655014f1..eccaead2 100755
--- a/bin/add_subgenicregions.py
+++ b/bin/add_subgenicregions.py
@@ -68,7 +68,7 @@ def main(panel_file, autoexons, autodomains, custom, subgenic_regions_complement
# If you reach ths point, ind_start is defined and valid
# Search for END position starting from ind_start
- search_end = chr_data.iloc[ind_start:,:]
+ search_end = chr_data.iloc[ind_start:]
end_matches = np.where(search_end["POS"] == row["END"])[0]
end_found = len(end_matches) > 0
@@ -117,7 +117,7 @@ def main(panel_file, autoexons, autodomains, custom, subgenic_regions_complement
upd_end = ind_end
# Extract subgenic data and modify gene names
- subgenic_data = chr_data.iloc[upd_start: upd_end + 1, :].copy()
+ subgenic_data = chr_data.iloc[upd_start: upd_end + 1].copy()
subgenic_data["GENE"] = region_name
new_data = pd.concat((new_data, subgenic_data))
diff --git a/bin/annotate_omega_failing.py b/bin/annotate_omega_failing.py
index 7cc1ce1e..f76747e8 100755
--- a/bin/annotate_omega_failing.py
+++ b/bin/annotate_omega_failing.py
@@ -18,7 +18,7 @@
-def load_flagged_tables(paths: List[Path]) -> Tuple[pd.DataFrame, pd.DataFrame]:
+def load_flagged_tables(paths: List[Path]) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
"""Read flagged files and return a normalized DataFrame with possible columns:
SAMPLE, GENE, reason_exclusion
"""
@@ -30,7 +30,7 @@ def load_flagged_tables(paths: List[Path]) -> Tuple[pd.DataFrame, pd.DataFrame]:
df = pd.read_csv(p, sep="\t", header=0, dtype=str)
pieces.append(df)
if not pieces:
- return pd.DataFrame(columns=["sample", "gene", "reason_exclusion"]), pd.DataFrame(columns=["sample", "gene", "reason_exclusion"])
+ return pd.DataFrame(columns=["sample", "gene", "reason_exclusion"]), pd.DataFrame(columns=["sample", "gene", "reason_exclusion"]), pd.DataFrame(columns=["sample", "gene", "reason_exclusion"]), pd.DataFrame(columns=["sample", "gene", "reason_exclusion"])
all_df = pd.concat(pieces, ignore_index=True, sort=False)
diff --git a/bin/check_samplesheet.py b/bin/check_samplesheet.py
index ada3e578..8af3eb05 100755
--- a/bin/check_samplesheet.py
+++ b/bin/check_samplesheet.py
@@ -211,7 +211,8 @@ def check_samplesheet(file_in, file_out, bam_required=False):
with file_in.open(newline="") as in_handle:
reader = csv.DictReader(in_handle, dialect=sniff_format(in_handle))
# Validate the existence of the expected header columns.
- if not required_columns.issubset(reader.fieldnames):
+ fieldnames = reader.fieldnames or []
+ if not required_columns.issubset(fieldnames):
req_cols = ", ".join(required_columns)
logger.critical(f"The sample sheet **must** contain these column headers: {req_cols}.")
sys.exit(1)
@@ -224,7 +225,7 @@ def check_samplesheet(file_in, file_out, bam_required=False):
logger.critical(f"{str(error)} On line {i + 2}.")
sys.exit(1)
checker.validate_unique_samples()
- header = list(reader.fieldnames)
+ header = list(fieldnames)
# See https://docs.python.org/3.9/library/csv.html#id3 to read up on `newline=""`.
with file_out.open(mode="w", newline="") as out_handle:
writer = csv.DictWriter(out_handle, header, delimiter=",")
diff --git a/bin/create_mask_matrix.py b/bin/create_mask_matrix.py
index ba66a870..5a91592b 100755
--- a/bin/create_mask_matrix.py
+++ b/bin/create_mask_matrix.py
@@ -63,7 +63,7 @@ def add_bed_positions(bed_df: pd.DataFrame,
entries_added = 0
for row in bed_df.itertuples():
- for pos in range(row.START, row.END + 1):
+ for pos in range(int(row.START), int(row.END) + 1):
key = (row.CHROM, pos, sample_name)
if key not in masked_positions:
mask_data.append({
diff --git a/bin/explore_saturation.ipynb b/bin/explore_saturation.ipynb
new file mode 100644
index 00000000..5a08591a
--- /dev/null
+++ b/bin/explore_saturation.ipynb
@@ -0,0 +1,652 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "271d39dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7d87ea7",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4a9e0f4c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "amino_acids = {\n",
+ " \"nonpolar\": [\"A\", \"V\", \"I\", \"L\", \"M\", \"F\", \"W\", \"P\", \"G\"],\n",
+ " \"polar\": [\"S\", \"T\", \"N\", \"Q\", \"C\", \"Y\"],\n",
+ " \"positive\": [\"K\", \"R\", \"H\"],\n",
+ " \"negative\": [\"D\", \"E\"],\n",
+ " \"truncated\": [\"*\"],\n",
+ "}\n",
+ "rev_amino_acids = {aa: group for group, aas in amino_acids.items() for aa in aas}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1024a57c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aminoacid_index = sum(amino_acids.values(), []).remove(\"*\")\n",
+ "aminoacid_index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "203612a5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "groups_order = list(amino_acids.keys()).remove(\"truncated\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "987f43ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ "
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+ " \n",
+ "
\n",
+ "
155872 rows × 9 columns
\n",
+ "