Statistics

Statistics Overview

Statistics included within Metabolon’s Integrated Bioinformatics Platform provide additional utility and control for exploring and interpreting your data. As your research grows, the statistics tool supports further hypothesis testing and analyses using key features such as outlier removal and modifying metadata and enables researchers to run additional statistical tests such as T-tests and ANOVAs, all following an easy, step-by-step process.

Researchers handling large sample datasets often need help filtering out unnecessary data and focusing on what’s important. Statistics within the Bioinformatics Platform includes easy-to-use features that help simplify data analysis, allowing for deeper insights—even without experience in bioinformatics or statistical methods.

Statistics within Metabolon’s Integrated Bioinformatics Platform

The Statistics tool plays a critical role in supporting complex research initiatives. It enables users to design and execute customized analyses, manage diverse metadata configurations, and visualize results in intuitive and insightful ways.

Outlier Removal
The outlier removal feature enables you to remove samples manually by selecting them from the sample metadata table. Alternatively, you can remove outliers in the PCA and PLS-DA modules for a data-driven approach to outlier removal.

Metadata Management
Scientific research is iterative, and what you derive from your sample data may evolve. The Statistics tool allows a flexible approach to updating sample metadata while retaining original and intermediate copies of the metadata table. Statistical analyses are associated with distinct instances of sample metadata, so you can keep exploring every facet of your dataset and always trace back what you did.

Custom Comparison With Advanced Statistics
Statistics forms the foundation of scientific research, enabling researchers to align their methods with the research objectives and the underlying data structure for reliable and meaningful results. The Statistics tool guides you through a step-by-step process to create comparisons and employ the most appropriate statistics for the research question. Select from T-tests, One-way ANOVA, and Two-way ANOVA to quickly iterate and test hypotheses and maintain a complete record of your analysis work.

Statistics Features

Upload Your Own Stats

This tab allows you to upload and blend your existing statistical data with an existing project. Click on the ‘upload’ button and follow the step by step instructions to provide additional dimensions and filters to your data tables and visualizations.

Analyses

The “Analyses” tab allows you to monitor the status and review the specific details of analyses in the current project. Completed analyses’ can be activated for visualization in the other modules of the Integrated Bioinformatics Platform. Keep track of your entire statistical workflow in one place and maintain clear traceability of your research activity.

Define New Analysis

The “Define Analysis” tab follows a step-by-step guide to quickly launch advanced analysis on your data by configuring test selection (e.g., T-Test, One-way ANOVA, and Two-way ANOVA), data selection, analysis settings, and, finally, specific sample group comparisons.

Modify Sample Metadata

The “Modify Sample Metadata” tab lets you view and edit original and modified metadata for samples in a project. The updated sample metadata can power new statistical analyses and comparisons while maintaining persistent copies of previous metadata for traceability.

Outlier Removal

This tab allows you to remove samples or outliers from the sample metadata table to create a new sample set. New sample sets are reanalyzed, and the results are then available for visualization in the other modules of the Integrated Bioinformatics Platform, such as Volcano Plot or PCA. Outliers can also be automatically detected in PCA and PLS-DA and selected for removal.

Subset Dataset

Organize your research to suit you. Split projects into discrete groups of samples termed “Sample Sets” to perform independent analysis on them. This is especially useful for complex project designs with multiple experiments within a single project in which you want to isolate analysis to specific samples for the specific research question at hand.