Biology subjects

Nguyen, Q. P.

Publications and source records attributed to Nguyen, Q. P..

3 recordsLinked to original sources

bioRxiv2022

Evaluating trait-based sets for taxonomic enrichment analysis applied to human microbiome data sets

BackgroundSet-based pathway analysis is a powerful tool that allows researchers to summarize complex genomic variables in the form of biologically interpretable sets. Since the microbiome is characterized by a high degree of inter-individual variability in taxonomic compositions, applying enrichment methods using functionally driven taxon sets can increase both the reproducibility and interpretability of microbiome association studies. However, there is still an open question of which knowledge base to utilize for set construction. Here, we evaluate microbial trait databases, which aggregate experimentally determined microbial phenotypes, as a potential avenue for meaningful construction of taxon sets. MethodUsing publicly available microbiome sequencing data sets (both 16S rRNA gene metabarcoding and whole-genome metagenomics), we assessed these trait-based sets on two criteria: first, do they cover the diversity of microbes obtained from a typical data set, and second, do they confer additional predictive power on disease prediction tasks when assessed against measured pathway abundances and PICRUSt2 prediction. ResultsTrait annotations are well annotated to a small number but most abundant taxa within the community, concordant with the concept of the core-peripheral microbiome. This pattern is consistent across all categories of traits and body-sites for whole genome sequencing data, but much more heterogenous and inconsistent in 16S rRNA metabarcoding data due to difficulties in assigning species-level traits to genus. However, trait-set features are well predictive of disease outcomes compared against predicted and measured pathway abundances. Most important trait-set features are more interpreable and reveal interesting insights on the relationship between microbiome, its function, and health outcomes.

bioinformatics↗

bioRxiv2021

cILR: Competitive isometric log-ratio for taxonomicenrichment analysis

Research in human associated microbiomes often involves the analysis of taxonomic count tables generated via high-throughput sequencing. It is difficult to apply statistical tools as the data is high-dimensional, sparse, and compositional. An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. Set-based analysis is ubiquitous in the genomics literature, and has demonstrable impact in improving interpretability and power of downstream analysis. Unfortunately, there is a lack of sophisticated set-based analysis methods specific to microbiome taxonomic data, where current practice often employs abundance summation as a technique for aggregation. This approach prevents comparison across sets of different sizes, does not preserve inter-sample distances, and amplifies protocol bias. Here, we attempt to fill this gap with a new single sample taxon enrichment method that uses a novel log-ratio formulation based on the competitive null hypothesis commonly used in the enrichment analysis literature. Our approach, titled competitive balances for taxonomic enrichment analysis (CBEA), generates sample-specific enrichment scores as the scaled log ratio of the subcomposition defined by taxa within a set and the subcomposition defined by its complement. We provide sample-level significance testing by estimating an empirical null distribution of our test statistic with valid p-values. Herein we demonstrate using both real data applications and simulations that CBEA controls for type I error even under high sparsity and high inter-taxa correlation scenarios. Additionally, it provides informative scores that can be inputs to downstream analyses such as prediction tasks. Author summaryThe study of human associated microbiomes relies on genomic surveys via high-throughput sequencing. However, microbiome taxonomic data is sparse and high dimensional which prevents the application of standard statistical techniques. One approach to address this problem is to perform analyses at the level of taxon sets. Set-based analysis has a long history in the genomics literature, with demonstrable impact in improving both power and interpretability. Unfortunately, there is limited interest in developing new set-based tools tailored for microbiome taxonomic data given its unique features compared to other omics data types. We developed a new tool to generate taxon set enrichment scores at the sample level through a novel log-ratio formulation based on the competitive null hypothesis. Our scores can be used for statistical inference at both the sample and population levels, as well as inputs to other downstream analyses such as prediction models. We demonstrate the performance of our method against competing approaches across both real data analyses and simulation studies.

bioinformatics↗

bioRxiv2020

Functional delineation of tissue-resident CD8 T cell heterogeneity during infection and cancer

Unremitting defense against diverse pathogens and malignancies requires a dynamic and durable immune response. Tissue-resident memory CD8+ T cells (TO_SCPLOWRMC_SCPLOW) afford robust protection against infection and cancer progression through continuous surveillance of non-lymphoid tissues. Here, we provide insight into how TO_SCPLOWRMC_SCPLOW confer potent and persistent immunity through partitioning of distinct cellular subsets differing in longevity, effector function, and multipotency. Antigen-specific CD8+ T cells localized to the epithelium of the small intestine are primarily comprised of a shorter-lived effector population most prominent early following both acute viral and bacterial infections, and a longer-lived Id3hi TO_SCPLOWRMC_SCPLOW population that subsequently accumulates at later memory timepoints. We define regulatory gene-programs driving these distinct TO_SCPLOWRMC_SCPLOW states, and further clarify roles for Blimp1, T-bet, Id2, and Id3 in supporting and maintaining intestinal TO_SCPLOWRMC_SCPLOW heterogeneity during infection. Further, through single-cell RNAseq analysis we demonstrate that tumor-infiltrating lymphocytes broadly differentiate into discrete populations of short-lived and long-lived TO_SCPLOWRMC_SCPLOW-like subsets, which share qualities with terminally-exhausted and progenitor-exhausted cells, respectively. As the clinical relevance of TO_SCPLOWRMC_SCPLOW continues to widen from acute infections to settings of chronic inflammation and malignancy, clarification of the spectrum of phenotypic and functional states exhibited by CD8+ T cells that reside in non-lymphoid tissues will provide a framework for understanding their regulation and identity in diverse pathophysiological contexts.

immunology↗