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Sperm Motility Analysis of Atelopus sp. under Cryoprotectant Formulations

Statistical analysis of sperm motility data from Atelopus sp. specimens, evaluating the effect of two cryoprotectant formulations (CPA1 and CPA2) across four post-activation time points. Part of an ongoing amphibian cryopreservation research programme at Fundación Jambatu, Ecuador.

Data note: The dataset used here is a simulated representation of pilot experimental findings, produced to protect confidential project information while preserving the statistical characteristics of the original data. Data collection was conducted personally by the author with explicit permission from supervisor Andrea Terán.


Rendered report

The full analysis — figures, formatted tables, and interpretations — is available as a self-contained HTML document:

View analysis.html


Repository structure

.
├── analysis.Rmd          # Source document — prose, code, and output in one file
├── analysis.html         # Rendered report (knitted from analysis.Rmd)
├── prd.xlsx              # Simulated motility dataset
└── Stat_Tests.Rproj      # RStudio project file

Dependencies

R 4.x and RStudio. Install required packages before knitting:

install.packages(c("tidyverse", "readxl", "pwr", "ggdist",
                   "knitr", "kableExtra", "broom"))
Package Purpose
tidyverse Data wrangling (dplyr, tidyr) and plotting (ggplot2)
readxl Import .xlsx dataset
pwr Power analysis for sample size determination
ggdist stat_halfeye() for raincloud plot half-violins
knitr / kableExtra Formatted, captioned tables in the HTML output
broom Converts ANOVA and Tukey objects to tidy data frames

How to run

  1. Clone the repository.
  2. Open Stat_Tests.Rproj in RStudio — this sets the working directory correctly so prd.xlsx is found automatically.
  3. Open analysis.Rmd and click Knit, or run from the terminal:
rmarkdown::render("analysis.Rmd")

The rendered analysis.html will be written to the project folder. All code chunks are collapsed by default in the HTML output (code_folding: hide) — click any Code button to inspect the underlying R.


Analysis overview

Data

39 observations across two treatments and four time lapses. Each row is one motility measurement (% motile sperm) for a given treatment and time point.

Column Description
Treatment Cryoprotectant formulation — CPA1 or CPA2
Time_Lapse Minutes post-activation — 5, 10, 15, or 20
Motility Percentage of motile sperm

Visualisation

Raincloud plots combine a half-violin (distribution shape), box plot (median and IQR), and raw jittered points. This format is recommended for small samples where bar plots would hide individual variation and distribution shape (Allen et al., 2019; Weissgerber et al., 2015). CPA1 and CPA2 are displayed on a shared axis for direct comparison.

Statistical tests

A two-way ANOVA was fitted with Treatment, Time_Lapse, and their interaction as fixed factors. One-way ANOVAs were then fitted per treatment to isolate time effects within each formulation. Tukey's HSD post-hoc test was applied to CPA2, which showed a significant time effect.

Key findings

  • Treatment effect: No significant difference in overall mean motility between CPA1 and CPA2 at the current sample size.
  • Time effect: Time_Lapse is a significant predictor of motility for both formulations — motility declines over time regardless of cryoprotectant.
  • CPA2 post-hoc: Significant motility decline between Time 5 and Time 15, and between Time 5 and Time 20 (Tukey HSD, p adj < 0.05). All other pairwise comparisons were non-significant.
  • CPA1 post-hoc: No significant differences between any time lapse pair.
  • Sample size: A power analysis (Cohen's f = 0.25, α = 0.05, power = 0.80) indicates approximately 45 observations per group are needed for a confirmatory study. The current pilot falls below this threshold.

References

Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit RA (2019). Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Research, 4:63. https://doi.org/10.12688/wellcomeopenres.15191.1

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015). Beyond bar and line graphs: time for a new data presentation paradigm. PLOS Biology, 13(4):e1002128. https://doi.org/10.1371/journal.pbio.1002128


Author

A. Benjamin Garcés Cifuentes · Fundación Jambatu, Ecuador · R 4.x · Ubuntu 24.04 LTS

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Demonstrating data manipulation and statistical analysis abilities through curated datasets and code.

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