Plot&Stats

Jupyter notebooks were created to help us plot and analyze our datasets

Quick Start

Access the notebooks directly in Google Colab for an easy-to-use environment:

• Plot&Stats - Wide to Tidy Format: Transform wide-format data into tidy format for analysis.

  

• Plot&Stats - BoxPlots: Enhanced data visualization, quantifies effect size, adapts to non-standard distributions, streamlines analysis, ensures equitable group representation, achieves dataset balance for fairer comparisons, and delivers in-depth insights from balanced data.

  

• Plot&Stats—dimensionality reduction: A notebook for generating PCA, UMAP, or t-SNE dimensional reduction of multidimensional datasets.

  

• Plot&Stats - .pzfx to .csv Converter: Convert GraphPad Prism .pzfx files into .csv format for analysis.

  

About the Notebooks

Plot and Stats - wide to tidy

This notebook is designed to transform wide-format data into a tidy format for further analysis.

Wide and tidy formats represent two principal ways of structuring tabular data:

• Wide Format:

  • Each row represents a subject or item.
  • Observations spread across multiple columns.
  • Suitable for data entry or presentation.
  • Example with biological repeats:

    | Subject | Cond1_Repeat1 | Cond1_Repeat2 | Cond2_Repeat1 | Cond2_Repeat2 |
    |---------|---------------|---------------|---------------|---------------|
    | 1       | ValueA        | ValueB        | ValueC        | ValueD        |
    

• Tidy Format:

  • Each column is a variable, and each row is an observation.
  • Suited for statistical analysis and plotting.
  • Each row represents a unique combination of variables.
  • Example with biological repeats:

    | Subject | Condition | Repeat | Value  |
    |---------|-----------|--------|--------|
    | 1       | Cond1     | 1      | ValueA |
    | 1       | Cond1     | 2      | ValueB |
    | 1       | Cond2     | 1      | ValueC |
    | 1       | Cond2     | 2      | ValueD |
    

Wide format is more readable for direct comparisons across a subject's measurements, while tidy format is optimized for analysis, making data transformations, summarizations, and visualizations more straightforward.

Plot&Stats - BoxPlots

This Jupyter Notebook is crafted to analyze datasets maintained in a tidy format. It integrates a comprehensive set of functionalities for in-depth data examination, statistical evaluation, and dataset balancing, enhancing your data's analysis and interpretability.

Key Features

• Boxplots with Labels: Creates detailed boxplots that visually differentiate each data point and clearly label repeats, facilitating an immediate understanding of the data distributions.

• Cohen's d Calculation: Enables the computation of Cohen's d value, offering a quantitative measure of the effect size between groups and highlighting the significance of observed differences.

• Randomization Test Based on Cohen's d: Implements a non-parametric randomization test using Cohen's d, suitable for datasets that may not meet the strict assumptions required for traditional parametric tests. More info on randomization tests here.

• Statistical Summaries Export: Automatically generates and exports comprehensive statistical summaries, providing a snapshot of crucial metrics throughout the dataset.

• Dataset Balance Check: Examines the dataset for balance across various conditions and repeats, ensuring that each group is equally represented in subsequent analyses.

• Dataset Resampling: Facilitates the adjustment of the dataset to a balanced condition through downsampling, making comparisons across groups fairer and more meaningful.

• Analysis of Resampled Dataset: Offers tools to further analyze the balanced dataset, with plots and statistical tests designed to uncover robust insights from the equitably represented data.

This notebook acts as a powerful tool for researchers and data analysts, streamlining the workflow from data ingestion to comprehensive analysis, thus enabling a deeper and more accurate exploration of datasets.

Plot&Stats - dimensionality reduction

Key Features

• PCA Analysis & Plots: Generates PCA plots that visually represent the data's variance along principal components, along with the PCA loadings to identify contributing features.
• UMAP or t-SNE Visualization: Utilizes UMAP or t-SNE for dimensionality reduction to project high-dimensional data into a lower-dimensional space, enhancing cluster identification.
• HDBSCAN Clustering: Applies the HDBSCAN algorithm to identify naturally occurring clusters in the data without specifying the number of clusters a priori.
• Fingerprinting Plots: Creates fingerprinting plots that detail the distribution of the identified clusters accross the conditions.
• Boxplots of Clusters: Generates boxplots for each identified cluster to compare distributions across different conditions.

Plot&Stats - .pzfx to .csv Converter

This notebook facilitates the conversion of GraphPad Prism .pzfx files into .csv files for further analysis. The .csv files are packaged into a .zip archive for easy downloading and use.

Key Features

• Extracts tables from .pzfx files and converts them into pandas DataFrames.
• Saves each table as a separate .csv file.
• Packages all generated .csv files into a single .zip archive.

How to Use

1. Upload your .pzfx file when prompted.
2. The notebook will parse the file and create .csv files for each table.
3. After processing, manually download the converted_tables.zip file from the Files pane on the left side of the Colab interface:
   • Open the Files pane in Colab.
   • Locate the file converted_tables.zip.
   • Right-click on it and select Download.

This notebook simplifies working with GraphPad Prism data by enabling seamless integration with other tools and analysis workflows.