Biomedical research now commonly integrates diverse data types or views from
the same individuals to better understand the pathobiology of complex diseases,
but the challenge lies in meaningfully integrating these diverse views.
Existing methods often require the same type of data from all views
(cross-sectional data only or longitudinal data only) or do not consider any
class outcome in the integration method, presenting limitations. To overcome
these limitations, we have developed a pipeline that harnesses the power of
statistical and deep learning methods to integrate cross-sectional and
longitudinal data from multiple sources. Additionally, it identifies key
variables contributing to the association between views and the separation
among classes, providing deeper biological insights. This pipeline includes
variable selection/ranking using linear and nonlinear methods, feature
extraction using functional principal component analysis and Euler
characteristics, and joint integration and classification using dense
feed-forward networks and recurrent neural networks. We applied this pipeline
to cross-sectional and longitudinal multi-omics data (metagenomics,
transcriptomics, and metabolomics) from an inflammatory bowel disease (IBD)
study and we identified microbial pathways, metabolites, and genes that
discriminate by IBD status, providing information on the etiology of IBD. We
conducted simulations to compare the two feature extraction methods. The
proposed pipeline is available from the following GitHub repository:
this https URL

该研究提出了一种使用统计和深度学习方法集成多个来源的横断面和纵向数据、识别相关变量并提供深入生物学见解的流程，并将其应用于炎症性肠病的多组学数据，发现了可区分炎症性肠病状态的微生物通路、代谢物和基因，为炎症性肠病的病因提供了信息。

跨断面和长期多视角数据整合的深度学习流水线

A deep learning pipeline for cross-sectional and longitudinal multiview  data integration

We introduce an optimal transport-based model for learning a metric tensor
from cross-sectional samples of evolving probability measures on a common
Riemannian manifold. We neurally parametrize the metric as a spatially-varying
matrix field and efficiently optimize our model's objective using a simple
alternating scheme. Using this learned metric, we can nonlinearly interpolate
between probability measures and compute geodesics on the manifold. We show
that metrics learned using our method improve the quality of trajectory
inference on scRNA and bird migration data at the cost of little additional
cross-sectional data.

该研究提出了一个基于最优传输模型的算法，用于从共同的黎曼流形上的横截面概率分布样本中学习度量张量，并证明所提出的算法可以提高 scRNA 和鸟类迁徙数据上的轨迹推断质量。

通过最优输运进行黎曼度量学习

Riemannian Metric Learning via Optimal Transport

Modeling how individuals evolve over time is a fundamental problem in the
natural and social sciences. However, existing datasets are often
cross-sectional with each individual observed only once, making it impossible
to apply traditional time-series methods. Motivated by the study of human
aging, we present an interpretable latent-variable model that learns temporal
dynamics from cross-sectional data. Our model represents each individual's
features over time as a nonlinear function of a low-dimensional,
linearly-evolving latent state. We prove that when this nonlinear function is
constrained to be order-isomorphic, the model family is identifiable solely
from cross-sectional data provided the distribution of time-independent
variation is known. On the UK Biobank human health dataset, our model
reconstructs the observed data while learning interpretable rates of aging
associated with diseases, mortality, and aging risk factors.

通过新的可解释的潜变量模型，研究了关于交叉数据的时间动态演化问题，以人体衰老为动机，探索了与疾病、死亡率和老化风险因素相关的可解释的衰老速率。