Identifying Key Microbial Genera Associated with Bacteroides and Bifidobacterium Abundance in Early Infant Gut and Chlorophyll-a Production in Marine Ecosystems Using Deep Learning Inference with Knockoffs

Conceitos Básicos

DeepLINK-T, a novel deep learning inference method using knockoffs, can effectively identify key microbial genera associated with the abundance of Bacteroides and Bifidobacterium in early infant gut, as well as the chlorophyll-a concentration in marine ecosystems, while controlling the false discovery rate.

Resumo

The study introduces DeepLINK-T, a deep learning-based statistical inference framework that combines long short-term memory (LSTM) networks and the model-X knockoffs approach to perform feature selection on high-dimensional longitudinal time series data.

Key highlights:

• DeepLINK-T uses an LSTM autoencoder to generate knockoff variables that capture the serial dependence in time series data, and an LSTM prediction network to construct knockoff statistics for feature importance evaluation.
• Extensive simulation studies demonstrate DeepLINK-T's ability to control the false discovery rate (FDR) effectively while achieving superior feature selection power compared to its non-time series counterpart.
• DeepLINK-T is applied to three real-world metagenomic time series datasets:
  1. Identifying key bacterial genera associated with the abundance of Bacteroides and Bifidobacterium in early infant gut, including Parabacteroides and Rothia.
  2. Detecting Pyramimonas and Heterosigma as primary chlorophyll-a producers in a marine ecosystem.
  3. Revealing Ruminiclostridum and Rothia as significantly correlated with distinct dietary glycan treatments.
• The results showcase DeepLINK-T's practical utility and effectiveness in real-world applications, underscoring its potential in advancing microbiome research.

Estatísticas

The abundance of Bacteroides is positively correlated with the genus Parabacteroides.
The abundance of Bifidobacterium is positively correlated with the genus Rothia.
Pyramimonas and Heterosigma are significantly associated with the concentration of chlorophyll-a in a marine ecosystem.
Ruminiclostridum and Rothia are significantly correlated with two distinct types of dietary glycans treatment.

Citações

"Parabacteroides, identified as the top significant genus associated with Bacteroides in the gut of early infants, constitutes one of the major groups of bacteria that inhabit the human gastrointestinal tract, substantially influencing the microbial diversity and composition there."
"Rothia was found to be positively correlated with Bifidobacterium. Both Rothia and Bifidobacterium are integral components of breast milk, playing crucial roles in maintaining the immune homeostasis of infant gut."
"Pyramimonas and Heterosigma were notably associated with the concentration of chlorophyll-a in a marine metagenomic time series."
"Ruminiclostridum and Rothia were identified to be significantly correlated with two distinct types of dietary glycans treatment."

Principais Insights Extraídos De

DeepLINK-T

by Wenxuan Zuo,... às arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04317.pdf

Perguntas Mais Profundas

How can DeepLINK-T be extended to handle more complex time series structures, such as seasonality or nonstationary trends?

DeepLINK-T can be extended to handle more complex time series structures by incorporating additional components into the model. For handling seasonality, the model can be augmented with seasonal components such as Fourier terms or seasonal dummy variables. These components can capture the periodic patterns in the data and help the model adapt to seasonal variations. For nonstationary trends, techniques like trend filtering or trend decomposition can be integrated into DeepLINK-T to account for the changing trends over time. By incorporating these elements, DeepLINK-T can effectively capture and model more intricate time series structures.

What are the potential limitations of DeepLINK-T in dealing with high-dimensional time series data with a large number of features and a small number of time points?

One potential limitation of DeepLINK-T in dealing with high-dimensional time series data with a large number of features and a small number of time points is the risk of overfitting. With a large number of features and a limited amount of time series data, the model may struggle to generalize well to unseen data, leading to overfitting and reduced predictive performance. Additionally, the interpretability of the model may be compromised in such scenarios, as the complex interactions between numerous features and limited time points can make it challenging to extract meaningful insights from the model.

Can DeepLINK-T be adapted to incorporate additional information, such as spatial or environmental factors, to provide a more comprehensive understanding of microbial community dynamics in real-world ecosystems?

Yes, DeepLINK-T can be adapted to incorporate additional information such as spatial or environmental factors to enhance the understanding of microbial community dynamics in real-world ecosystems. By integrating spatial data, such as geographic coordinates or spatial proximity measures, DeepLINK-T can account for spatial dependencies and spatial patterns in the microbial community dynamics. Similarly, environmental factors like temperature, pH, or nutrient levels can be included as covariates in the model to assess their impact on microbial composition and abundance. By incorporating these additional factors, DeepLINK-T can offer a more comprehensive analysis of microbial community dynamics in real-world ecosystems, providing valuable insights into the interactions between microbes and their environment.