Research project
Statistical Analysis Pipeline for Neuroscience Data
A comprehensive statistical analysis framework for neuroscience research data. Supports multi-group comparisons (ANOVA, t-tests, Mann-Whitney), post-hoc corrections (Bonferroni, Tukey HSD), effect size calculations, and publication-ready visualisations via R/ggplot2 integration.
- My role
- Solo Developer
- Duration
- 1 month
- Discipline
- Research project
- Year
- 2024
Project Overview
A web-accessible statistical analysis pipeline built into the Grant Lab research platform. Researchers upload experimental datasets and receive comprehensive statistical reports with multiple test types, post-hoc corrections, and publication-quality plots — all without writing code or using external statistics software.
Project Details
Technologies Used
- Python (SciPy, pandas, NumPy)
- R (ggplot2, scales, readxl, dplyr)
- Flask (web interface)
- Dask (distributed computation for large datasets)
Key Features
- Multi-Test Framework: ANOVA (one-way, two-way), independent/paired t-tests, Mann-Whitney U, Kruskal-Wallis
- Post-Hoc Corrections: Bonferroni, Tukey HSD, Holm-Sidak for multiple comparison correction
- Effect Size Calculations: Cohen's d, eta-squared for quantifying practical significance
- R Plot Generation: Publication-ready box plots, violin plots, density plots, and interaction plots via ggplot2
- Batch Processing: Run the same analysis across multiple brain regions or experimental conditions
- Data Validation: Automatic normality testing (Shapiro-Wilk) to recommend parametric vs non-parametric tests
- Export: Results as CSV/Excel tables, plots as high-resolution PNG/SVG
Supported Plot Types
- Box plots with individual data points
- Violin plots with quartile markers
- Density distribution plots
- Interaction plots for two-way comparisons
- Bar plots with error bars
Technical Highlights
- Intelligent Test Selection: Automatically recommends appropriate statistical tests based on data distribution and group structure
- Python-R Bridge: Python orchestrates data processing and test execution, then delegates visualisation to R/ggplot2 for publication-quality output
- Reproducibility: Every analysis generates a complete log of parameters, tests applied, and p-values for audit trails
Impact
Eliminates the need for researchers to manually run statistics in SPSS, R, or Excel. Used to generate statistical analyses and figures for synaptome mapping publications.