Phylogenetic tree-based microbiome association test

2019 ◽  
Author(s):  
Kang Jin Kim ◽  
Jaehyun Park ◽  
Sang-Chul Park ◽  
Sungho Won
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Statistical Power ◽  
False Negative ◽  
Amplicon Sequencing ◽  
R Package ◽  
Chronic Fatigue ◽  
Association Test ◽  
Supplementary Information ◽  
Association Analyses

Abstract Motivation Ecological patterns of the human microbiota exhibit high inter-subject variation, with few operational taxonomic units (OTUs) shared across individuals. To overcome these issues, non-parametric approaches, such as the Mann–Whitney U-test and Wilcoxon rank-sum test, have often been used to identify OTUs associated with host diseases. However, these approaches only use the ranks of observed relative abundances, leading to information loss, and are associated with high false-negative rates. In this study, we propose a phylogenetic tree-based microbiome association test (TMAT) to analyze the associations between microbiome OTU abundances and disease phenotypes. Phylogenetic trees illustrate patterns of similarity among different OTUs, and TMAT provides an efficient method for utilizing such information for association analyses. The proposed TMAT provides test statistics for each node, which are combined to identify mutations associated with host diseases. Results Power estimates of TMAT were compared with existing methods using extensive simulations based on real absolute abundances. Simulation studies showed that TMAT preserves the nominal type-1 error rate, and estimates of its statistical power generally outperformed existing methods in the considered scenarios. Furthermore, TMAT can be used to detect phylogenetic mutations associated with host diseases, providing more in-depth insight into bacterial pathology. Availability and implementation The 16S rRNA amplicon sequencing metagenomics datasets for colorectal carcinoma and myalgic encephalomyelitis/chronic fatigue syndrome are available from the European Nucleotide Archive (ENA) database under project accession number PRJEB6070 and PRJEB13092, respectively. TMAT was implemented in the R package. Detailed information is available at http://healthstat.snu.ac.kr/software/tmat. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data

2019 ◽  
Vol 37 (2) ◽  
pp. 599-603 ◽  
Author(s):  
Li-Gen Wang ◽  
Tommy Tsan-Yuk Lam ◽  
Shuangbin Xu ◽  
Zehan Dai ◽  
Lang Zhou ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
R Package ◽  
External Data ◽  
Input And Output ◽  
Evolutionary Context ◽  
Tree Data ◽  
Downstream Analysis ◽  
Different Sources ◽  
Associated Data

Abstract Phylogenetic trees and data are often stored in incompatible and inconsistent formats. The outputs of software tools that contain trees with analysis findings are often not compatible with each other, making it hard to integrate the results of different analyses in a comparative study. The treeio package is designed to connect phylogenetic tree input and output. It supports extracting phylogenetic trees as well as the outputs of commonly used analytical software. It can link external data to phylogenies and merge tree data obtained from different sources, enabling analyses of phylogeny-associated data from different disciplines in an evolutionary context. Treeio also supports export of a phylogenetic tree with heterogeneous-associated data to a single tree file, including BEAST compatible NEXUS and jtree formats; these facilitate data sharing as well as file format conversion for downstream analysis. The treeio package is designed to work with the tidytree and ggtree packages. Tree data can be processed using the tidy interface with tidytree and visualized by ggtree. The treeio package is released within the Bioconductor and rOpenSci projects. It is available at https://www.bioconductor.org/packages/treeio/.

scholarly journals Phylogeny-Guided Microbiome OTU-Specific Association Test (POST)

2021 ◽  
Author(s):  
Caizhi Huang ◽  
Benjamin John Callahan ◽  
Michael C Wu ◽  
Shannon T. Holloway ◽  
Hayden Brochu ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Real Data ◽  
R Package ◽  
Association Test ◽  
Public Access ◽  
Phylogenetic Distance ◽  
Phylogenetic Information ◽  
Phylogenic Tree ◽  
Kernel Machine ◽  
Specific Association

Abstract Background: The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high-dimensionality and sparsity of typical microbiome profiles. Incorporating phylogenetic information is often used to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly. However, this assumption may not always be valid due to the complex effect of microbes, and phylogenetic information should be incorporated in a data-supervised fashion. Results: In this work, we propose a local collapsing test called Phylogeny-guided microbiome OTU-Specific association Test (POST). In POST, whether or not to borrow information and how much information to borrow from the neighboring OTUs in the phylogenic tree are supervised by phylogenetic distance and the outcome-OTU association. POST is constructed under the kernel machine framework to accommodate complex OTU effects and extends kernel machine microbiome tests from community-level to OTU-level. Using simulation studies, we showed that when the phylogenetic tree is informative, POST has better performance than existing OTU-level association tests. When the phylogenetic tree is not informative, POST achieves similar performance as existing methods. Finally, we show that POST can identify more outcome-associated OTUs that are of biological relevance in real data applications on bacterial vaginosis and on preterm birth. Conclusions: Using POST, we show that the power of detecting associated microbiome features can be enhanced by adaptively leveraging the phylogenetic information when testing for a target OTU. We developed an user friendly R package POSTm which is now available at CRAN (https://CRAN.R-project.org/package=POSTm) for public access.

scholarly journals pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis

2019 ◽  
Vol 35 (19) ◽  
pp. 3567-3575 ◽  
Author(s):  
Anna M Plantinga ◽  
Jun Chen ◽  
Robert R Jenq ◽  
Michael C Wu
Keyword(s):  
Statistical Power ◽  
Human Microbiome ◽  
R Package ◽  
Supplementary Information ◽  
Microbiome Composition ◽  
Type 1 Error ◽  
Wide Range ◽  
Subject Variability ◽  
Ordination Analysis

Abstract Motivation The human microbiome is notoriously variable across individuals, with a wide range of ‘healthy’ microbiomes. Paired and longitudinal studies of the microbiome have become increasingly popular as a way to reduce unmeasured confounding and to increase statistical power by reducing large inter-subject variability. Statistical methods for analyzing such datasets are scarce. Results We introduce a paired UniFrac dissimilarity that summarizes within-individual (or within-pair) shifts in microbiome composition and then compares these compositional shifts across individuals (or pairs). This dissimilarity depends on a novel transformation of relative abundances, which we then extend to more than two time points and incorporate into several phylogenetic and non-phylogenetic dissimilarities. The data transformation and resulting dissimilarities may be used in a wide variety of downstream analyses, including ordination analysis and distance-based hypothesis testing. Simulations demonstrate that tests based on these dissimilarities retain appropriate type 1 error and high power. We apply the method in two real datasets. Availability and implementation The R package pldist is available on GitHub at https://github.com/aplantin/pldist. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals qgg: an R package for large-scale quantitative genetic analyses

2019 ◽  
Vol 36 (8) ◽  
pp. 2614-2615 ◽  
Author(s):  
Palle Duun Rohde ◽  
Izel Fourie Sørensen ◽  
Peter Sørensen
Keyword(s):  
Quantitative Traits ◽  
Large Scale ◽  
Genetic Parameters ◽  
R Package ◽  
Supplementary Information ◽  
Genetic Analyses ◽  
Association Analyses ◽  
Main Page ◽  
Quantitative Genetic ◽  
Efficient Processing

Abstract Summary Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, and performing single and multiple marker association analyses and genomic-based predictions of phenotypes. Availability and implementation The qgg package is freely available. For the latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg. The current release is available from CRAN (https://CRAN.R-project.org/package=qgg) for all major operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees

2020 ◽  
Vol 36 (20) ◽  
pp. 5007-5013 ◽  
Author(s):  
Martin R Smith
Keyword(s):  
Phylogenetic Trees ◽  
R Package ◽  
Similarity Score ◽  
Supplementary Information ◽  
Information Theoretic ◽  
Tree Comparison ◽  
New Information ◽  
Alternative Measures ◽  
Tree Similarity ◽  
Probabilistic Measures

Abstract Motivation The Robinson–Foulds (RF) metric is widely used by biologists, linguists and chemists to quantify similarity between pairs of phylogenetic trees. The measure tallies the number of bipartition splits that occur in both trees—but this conservative approach ignores potential similarities between almost-identical splits, with undesirable consequences. ‘Generalized’ RF metrics address this shortcoming by pairing splits in one tree with similar splits in the other. Each pair is assigned a similarity score, the sum of which enumerates the similarity between two trees. The challenge lies in quantifying split similarity: existing definitions lack a principled statistical underpinning, resulting in misleading tree distances that are difficult to interpret. Here, I propose probabilistic measures of split similarity, which allow tree similarity to be measured in natural units (bits). Results My new information-theoretic metrics outperform alternative measures of tree similarity when evaluated against a broad suite of criteria, even though they do not account for the non-independence of splits within a single tree. Mutual clustering information exhibits none of the undesirable properties that characterize other tree comparison metrics, and should be preferred to the RF metric. Availability and implementation The methods discussed in this article are implemented in the R package ‘TreeDist’, archived at https://dx.doi.org/10.5281/zenodo.3528123. Supplementary information Supplementary data are available at Bioinformatics online.

DoubleRecViz: a web-based tool for visualizing transcript–gene–species tree reconciliation

2020 ◽  
Author(s):  
Esaie Kuitche ◽  
Yanchun Qi ◽  
Nadia Tahiri ◽  
Jack Parmer ◽  
Aïda Ouangraoua
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Source Code ◽  
Species Tree ◽  
Supplementary Information ◽  
Dynamic Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Tree Reconciliation ◽  
Transcript Gene

Abstract Motivation A phylogenetic tree reconciliation is a mapping of one phylogenetic tree onto another which represents the co-evolution of two sets of taxa (e.g. parasite–host co-evolution, gene–species co-evolution). The reconciliation framework was extended to allow modeling the co-evolution of three sets of taxa such as transcript–gene–species co-evolutions. Several web-based tools have been developed for the display and manipulation of phylogenetic trees and co-phylogenetic trees involving two trees, but there currently exists no tool for visualizing the joint reconciliation between three phylogenetic trees. Results Here, we present DoubleRecViz, a web-based tool for visualizing double reconciliations between phylogenetic trees at three levels: transcript, gene and species. DoubleRecViz extends the RecPhyloXML model—developed for gene–species tree reconciliation—to represent joint transcript–gene and gene–species tree reconciliations. It is implemented using the Dash library, which is a toolbox that provides dynamic visualization functionalities for web data visualization in Python. Availability and implementation DoubleRecViz is available through a web server at https://doublerecviz.cobius.usherbrooke.ca. The source code and information about installation procedures are also available at https://github.com/UdeS-CoBIUS/DoubleRecViz. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MetaCycle: an integrated R package to evaluate periodicity in large scale data

2016 ◽  
Author(s):  
Gang Wu ◽  
Ron C Anafi ◽  
Michael E Hughes ◽  
Karl Kornacker ◽  
John B Hogenesch
Keyword(s):  
Statistical Power ◽  
Large Scale ◽  
Time Series Data ◽  
R Package ◽  
Ease Of Use ◽  
Data Availability ◽  
Supplementary Information ◽  
Series Data ◽  
Large Scale Data ◽  
Scale Data

Summary: Detecting periodicity in large scale data remains a challenge. Different algorithms offer strengths and weaknesses in statistical power, sensitivity to outliers, ease of use, and sampling requirements. While efforts have been made to identify best of breed algorithms, relatively little research has gone into integrating these methods in a generalizable method. Here we present MetaCycle, an R package that incorporates ARSER, JTK_CYCLE, and Lomb-Scargle to conveniently evaluate periodicity in time-series data. Availability and implementation: MetaCycle package is available on the CRAN repository (https://cran.r-project.org/web/packages/MetaCycle/index.html) and GitHub (https://github.com/gangwug/MetaCycle). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Significance tests for analyzing gene expression data with small sample sizes

2019 ◽  
Vol 35 (20) ◽  
pp. 3996-4003
Author(s):  
Insha Ullah ◽  
Sudhir Paul ◽  
Zhenjie Hong ◽  
You-Gan Wang
Keyword(s):  
Gene Expression ◽  
Statistical Power ◽  
Degrees Of Freedom ◽  
Lymphoblastic Leukemia ◽  
R Package ◽  
Small Sample ◽  
Supplementary Information ◽  
Equal Variance ◽  
Small Sample Sizes ◽  
T Distribution

Abstract Motivation Under two biologically different conditions, we are often interested in identifying differentially expressed genes. It is usually the case that the assumption of equal variances on the two groups is violated for many genes where a large number of them are required to be filtered or ranked. In these cases, exact tests are unavailable and the Welch’s approximate test is most reliable one. The Welch’s test involves two layers of approximations: approximating the distribution of the statistic by a t-distribution, which in turn depends on approximate degrees of freedom. This study attempts to improve upon Welch’s approximate test by avoiding one layer of approximation. Results We introduce a new distribution that generalizes the t-distribution and propose a Monte Carlo based test that uses only one layer of approximation for statistical inferences. Experimental results based on extensive simulation studies show that the Monte Carol based tests enhance the statistical power and performs better than Welch’s t-approximation, especially when the equal variance assumption is not met and the sample size of the sample with a larger variance is smaller. We analyzed two gene-expression datasets, namely the childhood acute lymphoblastic leukemia gene-expression dataset with 22 283 genes and Golden Spike dataset produced by a controlled experiment with 13 966 genes. The new test identified additional genes of interest in both datasets. Some of these genes have been proven to play important roles in medical literature. Availability and implementation R scripts and the R package mcBFtest is available in CRAN and to reproduce all reported results are available at the GitHub repository, https://github.com/iullah1980/MCTcodes. Supplementary information Supplementary data is available at Bioinformatics online.

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

2021 ◽  
Author(s):  
Darawan Rinchai ◽  
Jessica Roelands ◽  
Mohammed Toufiq ◽  
Wouter Hendrickx ◽  
Matthew C Altman ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
R Package ◽  
Supplementary Information ◽  
Illustrative Case ◽  
Bioinformatic Tools ◽  
Transcriptional Module ◽  
Wide Range ◽  
Downstream Analysis ◽  
Computing Module ◽  
Parallel Workflow

Abstract Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

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