Skill UI

PyDESeq2 is a robust Python implementation for conducting differential gene expression analysis on bulk RNA-seq count data. It supports complete statistical workflows, including data loading from various formats, complex multi-factor design specifications (e.g., accounting for batch effects or covariates), normalization, and performing advanced Wald tests. It is ideal for researchers transitioning R-based DESeq2 analyses into scalable Python pipelines.

This skill guides users through the entire statistical analysis workflow, covering everything from framing research questions and checking assumptions to running complex models (t-tests, ANOVA, regression, Bayesian methods). It emphasizes calculating effect sizes and generating fully formatted results compatible with APA guidelines, ensuring rigorous and reproducible research reporting.

Statsmodels is a robust Python library providing comprehensive tools for advanced statistical analysis. It supports a wide range of models, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM like Poisson and Logistic), Weighted Least Squares (WLS), and complex time series forecasting (ARIMA). Use this skill for rigorous econometrics, detailed diagnostic testing, and generating publication-ready statistical inference.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

This skill provides an end-to-end, highly reproducible workflow for bulk RNA-seq differential expression analysis. It orchestrates the entire pipeline, guiding users from raw FASTQ reads through Quality Control (QC), trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and finally executing differential expression testing (pydeseq2) and pathway enrichment analysis. It is designed for robust, statistically sound, and fully documented results.

Calculates statistical power and required sample sizes for study planning. It supports both closed-form methods (for standard tests like t-tests, ANOVA) and simulation-based methods (for complex models like mixed models or survival analysis). Use this skill to determine the minimum detectable effect (MDE) or justify sample sizes for grants and IRB protocols before data collection.

This skill generates detailed, structured research protocols for various academic, clinical, or market research studies. It ensures all critical components—such as objectives, study design, participant criteria, methodology, data management, statistical analysis plan, and ethical considerations—are comprehensively documented, providing a robust framework for any scientific investigation.