# Novel analysis of association between microbiome and treatment infection in AML

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $402,064

## Abstract

Project Summary
The advance of the Human Microbiome Project provides the unprecedented opportunities for exploring the
critical roles played by commensal microbiota in human health, immune maintenance, and disease. Massive
megagenomic sequencing data have been produced in this blooming research area. Unique features, including
extremely large dimensionality, complex correlation, zero-inflation, and compositional nature, of the produced
data pose a huge challenge for analysis in terms of both methodology and computation, and render many
existing statistical approaches inapplicable. Ignoring or inappropriately handling these features likely leads to
distorted medical conclusions. Unfortunately, few formal analysis tools are available to address these
challenges, mainly data transformation and dimension reduction methods (typically, PCA) in mediation analysis
that lack of direct interpretability of the results; and penalized variable selection methods that are incapable of
handling longitudinal response variables, and high-dimensional functional and compositional covariates. This
proposal is devoted to developing a new set of statistically systematic and computationally efficient methods
for utilizing complex and high-throughput microbial taxa measurements to explore the associations with
treatment-related infection in disease. The specific aims are: (1) Developing a clustering mediation model
system to study the mediating effects of microbiota on chemotherapy in terms of the association with infections
in AML; (2) Performing variable selection and covariance estimation for longitudinal microbial alpha-diversity in
varying-coefficient models with high-dimensional and compositional taxa measurements as the covariates in
AML; (3) Identifying important microbial taxa, which have two unique features---functional (measured over the
time) and compositional (relative abundance), to be associated and predictive of chemotherapy-related
infection in AML. Testing and validating the proposed analytical tools, and software development are two
accompanying secondary aims. Mediation analysis, clustering, functional varying-coefficient models, functional
regression models, data adaptive regularization for model selection, standard and non-standard theory for
statistical tests are among the major statistical components in the proposal.

## Key facts

- **NIH application ID:** 10229577
- **Project number:** 5R01AI143886-03
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Jianhua Hu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $402,064
- **Award type:** 5
- **Project period:** 2019-09-23 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10229577

## Citation

> US National Institutes of Health, RePORTER application 10229577, Novel analysis of association between microbiome and treatment infection in AML (5R01AI143886-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10229577. Licensed CC0.

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