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PenNSAM Nutrition Analytic Core (PNAC)

Director: Li, Hongzhe., PhD


The PenNSAM Nutrition Analytic Core (PNAC) provides services to analyze the big data sets generated from human diet and nutrition studies. PNAC utilizes advanced analytic techniques to analyze high dimensional datasets in genomics, metabolomics, proteomics and immune profiling. Advanced biostatistical, computational, and machine learning methods will be used to integrate results with clinical phenotyping information and to make correlations and predictions.

Services Offered
1. Calculation of food pattern scores based on NDSR, DHQ, and ASA 24 data:
The Healthy Eating Index-2015 (HEI-2015);
The Alternative Mediterranean Diet (AMED);
The Diet Inflammatory Index (DII);
The Empirical Dietary Inflammatory Index (EDII);
and The Diet Approach to Stop Hypertension (DASH) score

2. Basic descriptive statistics, statistical tests and regression analysis, including generalized linear regression and Cox regression analysis.

3. Data processing, quality control, batch-effects adjustments for plasma, urinary, and fecal metabolomic datasets using the standard Bioinformatics software tools such as ComBat and SVA methods.

4. Exploratory analysis to identify possible outliers, data transformation, clusters and patterns in the dietary and nutrient data sets.

5. Differential abundance analysis based on omics data including analysis on 16S rRNA gene and shotgun metagenomic microbiome data.

6. Machine learning methods such as Lasso and random forests to build predictive models for various clinical outcomes associated with diet and nutrition using high dimensional omics data.



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Last updated: 2022-01-19T11:04:19.763-05:00

Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016