Statistics Toolbox

Unlock the Microbiome Using the Cosmos-Hub Statistics Toolbox

Cosmos-Hub allows researchers to create virtual cohorts for microbiome analysis using their study metadata. Simply upload any categorical or continuous metadata from your study via the software’s standard template and create virtual cohorts to compare against each other.

 
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Metabolomics

Unlock the Metabolome Using Cosmos-Hub

We’re excited to announce that the Cosmos-Hub metabolomics module is coming soon! Users will be able to upload their metabolomics data using a standardized data import template and take advantage of all of the existing benefits offered by the Cosmos-Hub integrated data platform including storage of metabolite data, study metadata, access to the Cosmos-Hub statistics toolbox as well as RITA, our AI co-pilot, helping users interpret their data and publish their findings.

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Statistics Toolbox

Unlock the Microbiome Using the Cosmos-Hub Statistics Toolbox

Cosmos-Hub allows researchers to create virtual cohorts for microbiome analysis using their study metadata. Simply upload any categorical or continuous metadata from your study via the software’s standard template and create virtual cohorts to compare against each other.

 
Statistics Toolbox

 Importance of Metabolomics Analysis in Microbiome Research 

Cmbio has offered both targeted and untargeted metabolomics lab services for a number of years given its importance in understanding mechanism of action in microbiome studies. Microbial communities produce, modify, and degrade a vast array of metabolites, many of which are absorbed and circulate throughout the host, influencing health and disease. 

As such, we’re excited to be launching an interactive software option for biologists embarking on metabolomics studies as part of their microbiome research journey.

Microbe-Host Metabolic Interactions

Metabolomics enables the identification of metabolites that are uniquely produced or transformed by the microbiome, such as short-chain fatty acids, bile acids, and neurotransmitter precursors.

Health and Disease Links

Changes in microbial composition or activity can be directly linked to metabolic alterations associated with conditions like obesity, diabetes, inflammatory bowel disease, and even neurological disorders.

Personalized Interventions

By integrating metabolomics with microbiome data, researchers can pinpoint which metabolites are under microbial versus genetic control, guiding targeted dietary, probiotic, or pharmacological interventions for improved health outcomes.

 

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Cosmos-Hub Statistics Toolbox for Metabolomics

The Cosmos-Hub platform already enables a very user-friendly statistics toolbox that enables users to conduct customizable univariate and multivariate comparative analyses to reveal interactive charts, visualizations and accompanying statistics.  Users have the flexibility to switch between different outputs, and export them in different formats. Some of these include but are not limited to:
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Interactive Tables

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Heatmaps

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Abundance Distribution Analysis

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PCA

To learn more about the current suite of tools, please visit our Statistics Toolbox page.

As part of the new metabolomics module, we’re launching a number of new statistical analysis methods and visualizations commonly used in the metabolomics field:

  • Upgraded PCA - Bi-plots & Loadings Plots
  • PLS-DA
  • Multivariate Statistics
  • Additional Univariate Statistics
  • Machine Learning Frameworks

Advanced Principle Component Analysis (PCA)

Unlock deeper insights from your complex metabolomics datasets with our intuitive Principal Component Analysis (PCA) module, seamlessly integrated into the Cosmos-Hub Statistics Toolbox. 
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Principal Component Analysis (PCA) is a powerful unsupervised statistical technique that transforms high-dimensional metabolomics data into a simpler, lower-dimensional representation. By identifying the principal components – the directions of greatest variance in your dataset – PCA helps you:
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Uncover Discriminatory Patterns with PLS-DA

Elevate your metabolomics research with our advanced Partial Least Squares Discriminant Analysis (PLS-DA) module, a powerful supervised learning tool integrated into the Cosmos-Hub Statistics Toolbox. Designed to identify the metabolic signatures that best distinguish between your predefined sample groups, our PLS-DA module helps you move from complex data to actionable biological insights with greater clarity and confidence.
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Distinguish Between Groups with PLS-DA

Partial Least Squares Discriminant Analysis (PLS-DA) is a sophisticated statistical method tailored for classification and biomarker discovery in high-dimensional metabolomics datasets. Unlike unsupervised methods like PCA, PLS-DA leverages prior knowledge of sample classes (e.g., control vs. treatment, healthy vs. disease) to maximize the separation between these groups, making it exceptionally effective for:
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Multi-variate Mixed effects modeling: Unlocking Robust Biomarker Discovery from Your Metabolomics Data
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The inherent complexity and high dimensionality of metabolomics data, coupled with influences from various covariates (like age, diet, or environment), demand sophisticated analytical approaches for novel biomarker discovery. Multivariate mixed effects modeling rises to this challenge, providing a robust framework for your biomarker discovery pipeline.

Statistical frameworks for multivariate analysis  allow users to:
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Why Choose Cosmos-HUB for Virtual Cohort Analysis?
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Seamless Data Import

Directly import your processed metabolomics data from various file formats or connect to your existing data lakes within our platform.

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Compatible with Targeted & Untargeted Metabolomics Data

Import, analyze and compare any metabolomics datasets from any metabolomics services provider.

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Interactive and Customizable Analysis

Users love the interactive charts and visualizations in Cosmos-Hub as they allow them to interpret results more easily and interact better with compositional data.

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Scalability and Performance

Leverage the power of AWS for analyzing large datasets without being constrained by local computing resources.

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Collaboration and Sharing

Securely share your results, visualizations, and insights with colleagues and collaborators directly within the platform.

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Export Options

Easily export high-quality plots in various formats (PNG, SVG, PDF) and data tables (e.g., principal component scores, loadings) for further analysis or publication.

Register your interest in the Metabolomics Module

Join our waiting list and be the first to see the metabolomics module when it's launched and receive exclusive offers.