OSCR

Bipixel rotation method: An algorithm for chirality analysis of biological mesh network.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § Materials and methods › Bipixel method ↔ scripts/Step4_GUI_CA_DRbias.py, lines 1–48 · score 0.94 · Step4_GUI_CA_DRbias.py, fitted spline, global maximum, spline curve, gui bipixel, asymmetry score
  2. [2] § Results › The bipixel method and rotation algorithm ↔ scripts/Step4_GUI_CA_DRbias.py, lines 109–182 · score 0.56 · log ratio, spline interpolation, Asymmetry score, peak, smoothed, CA
  3. [3] § CRediT authorship contribution statement ↔ scripts/Step4_GUI_CA_DRbias.py, lines 1–48 · score 0.56 · Kanta Kumaki, Koichi Matsuo
  4. [4] § CRediT authorship contribution statement ↔ scripts/Step3_GUI_Bipixel_Counts.py, lines 1–43 · score 0.55 · Kanta Kumaki, Koichi Matsuo

Paper

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The authors' code

Python · 348 lines · 13 KB · MIT · 3 matches

  1. # Step4_GUI_CA_DRbias.py
  2. # Dated: 2026-05-8 (Updated)
  3. # Author: Kanta Kumaki & Koichi Matsuo [Keio University School of Medicine & CIEM]
  4. """
  5. Chirality Analysis and Asymmetry Scoring Tool
  7. Usage:
  8. 1. CLI Mode:
  9. python Step4_GUI_CA_DRbias.py <excel_file1> [<excel_file2> ...]
  10. 2. GUI Mode:
  11. python GUI_CA_DRbias.py
  12. (Then select Excel files and plot options via dialogs)
  14. Description:
  15. This script performs automated chirality analysis on bipixel count data.
  17. Workflow:
  18. - Reads Excel files (compatible with GUI_Bipixel_Counts.py output).
  19. - For each sheet, fits a robust periodic spline to the 'angle' vs. 'count' data.
  20. - Handles circular periodicity (180-degree wrapping) for seamless boundary analysis.
  21. - Identifies the 'Center Angle' based on the global maximum of the fitted curve.
  22. - Calculates key metrics:
  23. 1. Center Angle (deg)
  24. 2. Log10(S1/S2) Ratio
  25. 3. Asymmetry Score (Mean absolute difference normalized by amplitude)
  26. - Generates publication-quality plots containing:
  27. - Raw samples and fitted spline curve.
  28. - Center angle (dashed vertical line).
  29. - S1 (Left) and S2 (Right) integrated areas filled from y=0.
  30. - Outputs a "Summary_Analysis" sheet as the first sheet in the original Excel file.
  32. Requirements:
  33. - numpy (2.0+ compatible), pandas, matplotlib, scipy, openpyxl
  34. """
  36. import os
  37. import sys
  38. import argparse
  39. import numpy as np
  40. import pandas as pd
  41. import matplotlib.pyplot as plt
  42. import tkinter as tk
  43. from tkinter import filedialog, messagebox
  44. from pathlib import Path
  45. from dataclasses import dataclass
  46. from typing import Dict, List, Optional
  47. from scipy.interpolate import UnivariateSpline
  48. from scipy.integrate import trapezoid # Required for NumPy 2.0+ compatibility
  50. # --- 1. Physics & Mathematical Helpers ---
  52. def wrap180(a: np.ndarray | float) -> np.ndarray:
  53. """Wrap angle(s) to [-90, +90) on a 180° circle."""
  54. return ((np.asarray(a) + 90.0) % 180.0) - 90.0
  56. def build_interpolant_tiled(x_deg: np.ndarray, y: np.ndarray, smoothing: float = None):
  57. """
  58. Fits a UnivariateSpline with ±180° tiling for periodic boundary stability.
  59. Strictly uses s=0 (interpolating through all points) by default to maintain
  60. biological fidelity. If the fit fails or results in non-finite values,
  61. it returns None to indicate the data quality is insufficient for analysis.
  62. """
  63. x = np.asarray(x_deg, float)
  64. y = np.asarray(y, float)
  66. # Pre-process: Remove NaN values
  67. mask = ~np.isnan(y)
  68. x, y = x[mask], y[mask]
  70. # Ensure at least enough points exist for a cubic spline (k=3)
  71. if len(x) < 4:
  72. return None
  74. # Pre-process: Ensure circular periodicity by ±180° tiling
  75. x_ext = np.concatenate([x - 180.0, x, x + 180.0])
  76. y_ext = np.concatenate([y, y, y])
  78. try:
  79. # Use user-defined smoothing if provided; otherwise, force s=0
  80. s_val = smoothing if smoothing is not None else 0
  82. # Fit the spline (Cubic, k=3)
  83. spl = UnivariateSpline(x_ext, y_ext, s=s_val, k=3)
  85. # Validate that the resulting spline is numerically stable at original points
  86. if not np.all(np.isfinite(spl(x))):
  87. return None # Return None if fit is unstable
  89. except Exception:
  90. # Return None for any convergence or mathematical errors
  91. return None
  93. # Return a functional wrapper for the spline interpolant
  94. return lambda ang: spl(np.asarray(ang, float))
  96. @dataclass
  97. class AnalysisResult:
  98. """Container for chirality metrics and visualization data."""
  99. status: str
  100. center_angle: Optional[float] # 失敗時は None
  101. asymmetry_score: Optional[float]
  102. S1: Optional[float]
  103. S2: Optional[float]
  104. log_S1_over_S2: Optional[float]
  105. details: Dict
  107. # --- 2. Core Analysis Engine ---
  109. def run_analysis(x_deg: np.ndarray, y: np.ndarray, smoothing: float = None) -> AnalysisResult:
  110. """
  111. Core Analysis Engine: Computes the center angle and integrates S1/S2 areas.
  113. This function reconciles the experimental data with a spline interpolant.
  114. If the interpolation fails due to poor data quality (returning None),
  115. the function returns a 'Failed' AnalysisResult with null (None) metrics.
  116. """
  117. f = build_interpolant_tiled(x_deg, y, smoothing=smoothing)
  119. # Handle cases where spline fitting is impossible (e.g., s=0 convergence failure)
  120. if f is None:
  121. return AnalysisResult(
  122. status="Failed",
  123. center_angle=None,
  124. asymmetry_score=None,
  125. S1=None, S2=None,
  126. log_S1_over_S2=None,
  127. details={}
  128. )
  130. # Generate a dense grid for numerical integration and peak detection
  131. xd = np.linspace(-90.0, 90.0, 3600)
  132. yd = f(xd)
  134. if np.all(np.isnan(yd)):
  135. raise ValueError("Interpolation resulted in All-NaN slice.")
  137. amp = np.nanmax(yd) or 1.0
  139. # Locate peak-based center (Global Maximum) as the primary Center Angle candidate
  140. gmax_idx = np.nanargmax(yd)
  141. max_ang = float(xd[gmax_idx])
  143. # Internal helper to calculate Asymmetry Score based on a given center angle
  144. def calc_asymmetry(ca: float) -> float:
  145. offs = np.linspace(0.0, 90.0, 900)
  146. y_plus = f(wrap180(ca + offs))
  147. y_minus = f(wrap180(ca - offs))
  148. return float(np.mean(np.abs(y_plus - y_minus)) / amp)
  150. best_ca = max_ang
  151. best_score = calc_asymmetry(best_ca)
  153. # Calculate S1 (Left) and S2 (Right) integrals split by the identified center angle
  154. t_span = np.linspace(-90.0, 90.0, 3600)
  155. y_centered = f(wrap180(best_ca + t_span))
  157. left_mask = t_span <= 0
  158. right_mask = t_span >= 0
  160. # Perform numerical integration using the trapezoidal rule
  161. S1 = float(trapezoid(y_centered[left_mask], t_span[left_mask]))
  162. S2 = float(trapezoid(y_centered[right_mask], t_span[right_mask]))
  164. log_ratio = np.log10(S1 / S2) if S1 > 0 and S2 > 0 else None
  166. # Helper function to extract periodic visualization segments
  167. def get_fill_data(t_start, t_end):
  168. ts = np.linspace(t_start, t_end, 1800)
  169. return wrap180(best_ca + ts), f(wrap180(best_ca + ts))
  171. return AnalysisResult(
  172. status="Success",
  173. center_angle=best_ca,
  174. asymmetry_score=best_score,
  175. S1=S1, S2=S2,
  176. log_S1_over_S2=log_ratio,
  177. details={
  178. "xd": xd, "yd": yd,
  179. "S1_fill": get_fill_data(-90, 0),
  180. "S2_fill": get_fill_data(0, 90)
  181. }
  182. )
  184. # --- 3. Visualization ---
  186. def save_plot(res: AnalysisResult, x_raw, y_raw, sheet_name, out_path, show_legend):
  187. """Generates area plots with specific legend order and periodic fill handling."""
  188. plt.figure(figsize=(8, 5))
  190. # 1. Samples
  191. plt.plot(x_raw, y_raw, 'o', label='Samples', color='black', markersize=5, alpha=0.6, zorder=3)
  193. # 2. Spline curve
  194. plt.plot(res.details["xd"], res.details["yd"], '-', label='Spline curve', color='black', linewidth=1.5, zorder=4)
  196. # 3. Center angle (Dashed line)
  197. plt.axvline(res.center_angle, color='black', linestyle='--', linewidth=1.2,
  198. label='Center angle' if show_legend else "", zorder=5)
  200. # Internal helper to handle plotting wrapped fill areas without horizontal artifacts
  201. def draw_periodic_fill(x, y, color, label_prefix, alpha=0.4):
  202. jumps = np.where(np.abs(np.diff(x)) > 90)[0]
  203. if len(jumps) == 0:
  204. s_idx = np.argsort(x)
  205. plt.fill_between(x[s_idx], y[s_idx], 0, color=color, alpha=alpha,
  206. label=label_prefix if show_legend else "", zorder=2)
  207. else:
  208. idx_splits = jumps + 1
  209. x_segments = np.split(x, idx_splits)
  210. y_segments = np.split(y, idx_splits)
  211. for i, (xs, ys) in enumerate(zip(x_segments, y_segments)):
  212. s_idx = np.argsort(xs)
  213. lbl = label_prefix if (show_legend and i == 0) else ""
  214. plt.fill_between(xs[s_idx], ys[s_idx], 0, color=color, alpha=alpha, label=lbl, zorder=2)
  216. # 4. S1 (Left) and S2 (Right) areas
  217. x_s1, y_s1 = res.details["S1_fill"]
  218. x_s2, y_s2 = res.details["S2_fill"]
  219. draw_periodic_fill(x_s1, y_s1, color='#4C86C6', label_prefix='S1 (Left)')
  220. draw_periodic_fill(x_s2, y_s2, color='#E15759', label_prefix='S2 (Right)')
  222. # Labels and Formatting
  223. plt.title(f"Sheet: {sheet_name}")
  224. plt.xlabel("Rotation angle")
  225. plt.ylabel("Bipixel count")
  227. tick_pos = [-90, -60, -30, 0, 30, 60, 90]
  228. plt.xticks(tick_pos, [f"{v}°" for v in tick_pos])
  229. plt.xlim(-90, 90)
  230. plt.ylim(0, None)
  231. plt.grid(True, linestyle=':', alpha=0.4, zorder=1)
  233. if show_legend:
  234. plt.legend(loc='upper right', frameon=True)
  236. plt.tight_layout()
  237. plt.savefig(out_path, dpi=300)
  238. plt.close()
  240. # --- 4. Main Controller ---
  242. def process_file(file_path: Path, show_legend: bool):
  243. """
  244. Main Controller: Iterates through worksheets and aggregates results into a summary Excel.
  246. Failed analyses are recorded as null entries in the summary table,
  247. effectively excluding 'bad data' while maintaining a record of the attempt.
  248. """
  249. xl = pd.ExcelFile(file_path)
  250. out_dir = file_path.parent / "plots"
  251. out_dir.mkdir(exist_ok=True)
  253. final_summary = []
  254. for sheet in xl.sheet_names:
  255. if "Summary" in sheet: continue # Skip existing summary sheets
  257. try:
  258. df = pd.read_excel(xl, sheet_name=sheet)
  259. df.columns = [str(c).strip() for c in df.columns]
  261. # Identify required columns using flexible keyword matching
  262. a_col = next((c for c in df.columns if 'angle' in c.lower()), None)
  263. v_col = next((c for c in df.columns if any(k in c.lower() for k in ['vertical', 'count', sheet.lower()])), None)
  265. if a_col is None or v_col is None:
  266. print(f"[WARN] Skipping {sheet}: Missing required columns.")
  267. continue
  269. # Extract target columns and remove rows with missing values
  270. df[a_col] = pd.to_numeric(df[a_col], errors='coerce').astype(float)
  271. df[v_col] = pd.to_numeric(df[v_col], errors='coerce').astype(float)
  273. valid_df = df[[a_col, v_col]].dropna().copy()
  274. if valid_df.empty:
  275. continue
  277. # Endpoint Harmonization:
  278. # If both -90° and +90° exist, average their values into -90°
  279. # and drop +90° to ensure seamless circular periodicity.
  280. idx_neg = valid_df[valid_df[a_col] == -90.0].index
  281. idx_pos = valid_df[valid_df[a_col] == 90.0].index
  283. if not idx_neg.empty and not idx_pos.empty:
  284. v_mean = (valid_df.at[idx_neg[0], v_col] + valid_df.at[idx_pos[0], v_col]) / 2.0
  285. valid_df.at[idx_neg[0], v_col] = v_mean
  286. valid_df = valid_df.drop(idx_pos)
  288. # Sort by angle and extract as numpy arrays for analysis
  289. valid_df = valid_df.sort_values(by=a_col)
  290. x, y = valid_df[a_col].values, valid_df[v_col].values
  292. # Execute analysis pipeline
  293. res = run_analysis(x, y)
  295. # Only generate visualization if the analysis was successful
  296. if res.status == "Success":
  297. save_plot(res, x, y, sheet, out_dir / f"{sheet}_plot.png", show_legend)
  298. print(f"[Done] {sheet}: Analysis successful.")
  299. else:
  300. print(f"[Skip] {sheet}: Spline fit failed (insufficient data quality).")
  302. # Append results to summary list (Metrics will be None/null if status is 'Failed')
  303. final_summary.append({
  304. "Sheet": sheet,
  305. "Status": res.status,
  306. "CenterAngle_deg": res.center_angle,
  307. "Log_S1_over_S2": res.log_S1_over_S2,
  308. "AsymmetryScore": res.asymmetry_score,
  309. "S1": res.S1,
  310. "S2": res.S2
  311. })
  313. except Exception as e:
  314. print(f"[ERROR] In sheet '{sheet}': {str(e)}")
  316. # Export aggregated summary to the original Excel file
  317. if final_summary:
  318. summary_df = pd.DataFrame(final_summary)
  319. with pd.ExcelWriter(file_path, engine='openpyxl', mode='a', if_sheet_exists='replace') as writer:
  320. # None values are automatically converted to empty cells in Excel
  321. summary_df.to_excel(writer, sheet_name="Summary_Analysis", index=False)
  323. # Reorder worksheets to place the Summary at the first position
  324. wb = writer.book
  325. wb._sheets.insert(0, wb._sheets.pop(-1))
  327. def main():
  328. """Application entry point handling arguments or GUI interaction."""
  329. parser = argparse.ArgumentParser(description="Chirality Analysis Pipeline with Hybrid GUI/CLI")
  330. parser.add_argument("--excel", nargs="*", help="List of Excel files to process")
  331. args = parser.parse_args()
  333. files = args.excel
  334. if not files:
  335. root = tk.Tk(); root.withdraw(); root.attributes('-topmost', True)
  336. files = filedialog.askopenfilenames(title="Select Excel Files", filetypes=[("Excel", "*.xlsx")])
  337. if not files: return
  338. show_legend = messagebox.askyesno("Plot Setting", "Show Legend in Plots?")
  339. else:
  340. show_legend = True
  342. for f in files:
  343. print(f"[INFO] Processing: {Path(f).name}")
  344. process_file(Path(f), show_legend)
  345. print("[DONE] Analysis completed.")
  347. if __name__ == "__main__":
  348. main()

Step4_GUI_CA_DRbias.py at commit faa0af4, under MIT · at the source

Overview

Authors: Kanta Kumaki1, Katsuhiro Kawaai1, Shinobu Noji1, Yukiko Kuroda1, Koichi Matsuo1,2
ORCID iDs: Yukiko Kuroda
  1. Laboratory of Cell and Tissue Biology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
  2. Central Institute for Experimental Medicine and Life Science (CIEM), 3-25-22 Tonomachi, Kawasaki 210-0821, Japan
Institutions: Keio University (Japan)
Journal: Journal of structural biology: X, volume 14, article 100154
Dates: received 11 May 2026; accepted 22 July 2026; published online 23 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.yjsbx.2026.100154 · PMID 42564810 · PMCID PMC13446290 · OpenAlex W7170147798
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Statistics, Connectivity, Evoked potentials
Keywords: Chirality, Vasculature, Image analysis, Porcine, Femur, Bone
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Keio University
Citations: not cited yet (Europe PMC); 27 references in the paper

Abstract

Vertebrates are bilateral organisms characterized by both symmetrical and asymmetrical structures. While skeletons are grossly symmetrical, microscopic biological networks within the bone—such as cortical canals containing vascular networks—often exhibit deviations from precise mirror-image symmetry. These deviations make it challenging to detect subtle chiral biases amidst random structural variations. We developed the “bipixel rotation method” to quantify the extent of these biases, thereby enabling the characterization of porcine femoral cortical canals. In this approach, binarized 2D images are rotated stepwise and skeletonized, followed by the quantification of vertically adjacent pixel pairs. We defined “center angle” as the rotation angle that yields the peak vertical bipixel count, and calculated “down-right bias” based on the asymmetry of the distribution of vertical bipixels around this center angle. After validating the method using alphabetical fonts and geometric wallpaper patterns, we applied the method to the cortical canal layers in porcine fibrolamellar bone visualized via nano-computed tomography (nano-CT). The method successfully detected potential chirality in the cortical canals, identifying up-right versus down-right biases, demonstrating its capability to capture even subtle structural chirality. These results suggest that the bipixel method is a versatile tool for chirality analysis of diverse biological mesh structures in the body.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

Koichimts/Bipixel_Method

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: faa0af4ccfd90e694470fe3404c43705b78339dc, 29 July 2026
Languages: Python (2)
Size: 55 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, CITATION.cff, environment (requirements.txt)
Not found: tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files), Matplotlib (1 file), Pillow (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/Koichimts/Bipixel_Method

Zenodo 21439490

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files), Matplotlib (1 file), Pillow (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source: doi.org/10.5281/zenodo.21439490

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Codes for performing the bipixel method are available at https://github.com/Koichimts/Bipixel_Method and are archived at Zenodo (https://doi.org/10.5281/zenodo.21439490).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 1 funder, 26 references.

Cite

This paper

Kumaki, K., Kawaai, K., Noji, S., Kuroda, Y., & Matsuo, K. (2026). Bipixel rotation method: An algorithm for chirality analysis of biological mesh network. Journal of structural biology: X, 14, 100154. https://doi.org/10.1016/j.yjsbx.2026.100154

BibTeX

@article{kumaki2026bipixel,
author = {Kumaki, Kanta and Kawaai, Katsuhiro and Noji, Shinobu and Kuroda, Yukiko and Matsuo, Koichi},
title = {{Bipixel rotation method: An algorithm for chirality analysis of biological mesh network}},
journal = {Journal of structural biology: X},
year = {2026},
month = jul,
volume = {14},
pages = {100154},
publisher = {Elsevier},
issn = {2590-1524},
doi = {10.1016/j.yjsbx.2026.100154},
url = {https://doi.org/10.1016/j.yjsbx.2026.100154},
pmid = {42564810},
pmcid = {PMC13446290}
}

RIS

TY - JOUR
AU - Kumaki, Kanta
AU - Kawaai, Katsuhiro
AU - Noji, Shinobu
AU - Kuroda, Yukiko
AU - Matsuo, Koichi
TI - Bipixel rotation method: An algorithm for chirality analysis of biological mesh network
T2 - Journal of structural biology: X
J2 - J Struct Biol X
PY - 2026
DA - 2026/07/23
VL - 14
SP - 100154
SN - 2590-1524
PB - Elsevier
DO - 10.1016/j.yjsbx.2026.100154
UR - https://doi.org/10.1016/j.yjsbx.2026.100154
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.yjsbx.2026.100154",
"type": "article-journal",
"title": "Bipixel rotation method: An algorithm for chirality analysis of biological mesh network",
"container-title": "Journal of structural biology: X",
"author": [
{
"family": "Kumaki",
"given": "Kanta"
},
{
"family": "Kawaai",
"given": "Katsuhiro"
},
{
"family": "Noji",
"given": "Shinobu"
},
{
"family": "Kuroda",
"given": "Yukiko"
},
{
"family": "Matsuo",
"given": "Koichi"
}
],
"container-title-short": "J Struct Biol X",
"volume": "14",
"page": "100154",
"DOI": "10.1016/j.yjsbx.2026.100154",
"PMID": "42564810",
"PMCID": "PMC13446290",
"ISSN": "2590-1524",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.yjsbx.2026.100154",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
23
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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[5] doi:10.1126/sciadv.aec4911 [code]
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[7] doi: [code]
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[8] doi:10.1371/journal.pone.0356243 [code]
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Journal: Nature methods
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