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Data Challenge #2

Overview

The data and codes for this challenge come from a manuscript titled, "Computer-assisted beat-pattern analysis and the flagellar waveforms of bovine spermatozoa", authored by Benjamin J. Walker, Shiva Phuyal, Kenta Ishimoto, Chih-Kuan Tung, and Eamonn A. Gaffney. The data includes beat patterns of bovine spermatozoa, and the authors have shared the MATLAB files, scripts, and supplementary materials to accompany their research findings. The dataset is specifically related to the analysis and characterization of flagellar waveforms in bovine spermatozoa using computer-assisted methods. Researchers and participants in the Hackathon can explore this data to gain insights, conduct further analyses, and potentially contribute to the field of reproductive biology or computer-assisted sperm analysis.

Data

The original description and links to the dataset can be found here:

https://ora.ox.ac.uk/objects/uuid:45ec598b-a674-4738-bd00-e1b761f49411

The accompanying paper can be found here:

https://royalsocietypublishing.org/doi/10.1098/rsos.200769

Suggestions

Here are some ideas for Hackathon projects:

 

  1. Beat Pattern Classification:

    • Develop a machine learning model to classify sperm beat patterns based on the provided data. Use features such as tangent angles, Cartesian coordinates, flagellum length, and beat period. Evaluate the model's accuracy in distinguishing between fresh, frozen, and blebbed sperm.

  2. Period Finding Algorithm Enhancement:

    • Improve the accuracy of the period finding algorithm (`find_period.m`) for flagellar beats. Experiment with different signal processing techniques and algorithms to enhance the precision of period estimation, potentially using machine learning approaches.

  3. Population-Level PCA Analysis:

    • Enhance the `pop_pca.m` script to perform Principal Component Analysis (PCA) on the entire population of sperm beat patterns. Visualize the principal components and explore how variations in beat patterns contribute to the overall variability in the data.

  4. Beat Pattern Anomaly Detection:

    • Develop a tool to identify anomalous beat patterns in the dataset. Use unsupervised learning techniques to detect patterns that deviate significantly from the norm, potentially indicating abnormalities in sperm motility.

  5. Interactive Research Reproducibility:

    • Create an interactive and user-friendly platform that allows researchers to reproduce the analyses and figures presented in the manuscript. Ensure that the platform facilitates easy exploration and understanding of the research findings.

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