Sparse semiparametric canonical correlation analysis for data of mixed types

Yoon, Grace; Carroll, Raymond J.; Gaynanova, Irina

doi:10.1093/biomet/asaa007

Cited by 31 publications

(70 citation statements)

References 39 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, truncation to zero effects for low sequencing read counts likely obstruct unbiased estimation of negative correlations, and in turn, proportionality. A possible remedy for this data-induced artifact is the application of more advanced semi-parametric correlation estimators that infer latent correlations under data truncation assumptions (21,46). A detailed investigation of semi-parametric and other estimators may provide a promising avenue for future research.…”

Section: Discussionmentioning

confidence: 99%

“…Our computational data analysis workflow, available on GitHub and as synapse project (see Data Availability), is fully reproducible, provides all novel shrinkage estimators introduced here, and allows easy extension and comparison to additional data normalization, estimation, and downstream analysis tasks. For instance, future work could include the integration of more advanced zero-replacement strategies (51,52), application of popular data normalization schemes from single-cell data analysis (53) or the application of other correlation (21,46) or proportionality estimators, including those available in the propr package (23). Here, rather than using universal thresholding for sparsifying associations, more advanced selection strategies that control false discovery rates (as available in the propr package (23)) may improve the consistency of the microbial association inference workflows.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Shrinkage improves estimation of microbial associations under different normalization methods

Badri

Kurtz²,

Bonneau

et al. 2018

Preprint

View full text Add to dashboard Cite

Consistent estimation of associations in microbial genomic survey count data is fundamental to microbiome research. Technical limitations, including compositionality, low sample sizes, and technical variability, obstruct standard application of association measures and require data normalization prior to estimating associations. Here, we investigate the interplay between data normalization and microbial association estimation by a comprehensive analysis of statistical consistency. Leveraging the large sample size of the American Gut Project (AGP), we assess the consistency of the two prominent linear association estimators, correlation and proportionality, under di↵erent sample scenarios and data normalization schemes, including RNA-seq analysis work flows and log-ratio transformations. We show that shrinkage estimation, a standard technique in high-dimensional statistics, can universally improve the quality of association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as examples of exploratory data analysis, we show that variance-stabilizing and log-ratio approaches provide for the most consistent estimation of taxonomic and structural coherence. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Shrinkage improves estimation of microbial associations under different normalization methods

Badri

Kurtz²,

Bonneau

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…• Σ is estimated using a semi-parametric rank based approach relying on a truncated Gaussian copula model [50], which can deal with zeros in the data…”

Section: • R Packagementioning

confidence: 99%

NetCoMi: Network Construction and Comparison for Microbiome Data in R

Peschel

Müller

Mutius

et al. 2020

Preprint

View full text Add to dashboard Cite

Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization, and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g.~between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analyzing, and comparing microbial association networks from high-throughput sequencing data. Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analyzing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa, or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi's wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children's rooms between samples from two study centers (Ulm and Munich).

show abstract

“…For quantitative microbiome data, however, correlation and inverse correlation estimators are not yet available. In this work we propose to take a different approach relying on the recently proposed truncated Gaussian copula framework (Yoon et al, 2018).…”

Section: Semi-parametric Rank-based Correlation and Partial Correlmentioning

confidence: 99%

“…To this end, we first revisit a novel semi-parametric rank-based (SPR) approach to correlation estimation that can naturally deal with the large number of zeros in the data. The SPR estimator is easy to compute and can readily replace the naïve Pearson or rank-based sample correlation estimator which are often used as a first step in downstream statistical analysis tasks, including principal component analysis, principle coordinate analysis, discriminant analysis, or canonical correlation analysis (Yoon et al, 2018). Here we use the semi-parametric rank-based estimator as a starting point for sparse partial correlation estimation and introduce the Semi-Parametric Rank-based approach for INference in Graphical model (SPRING).…”

Section: Introductionmentioning

confidence: 99%

Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data

2019

Self Cite

View full text Add to dashboard Cite

High-throughput microbial sequencing techniques, such as targeted amplicon-based and metagenomic profiling, provide low-cost genomic survey data of microbial communities in their natural environment, ranging from marine ecosystems to host-associated habitats. While standard microbiome profiling data can provide sparse relative abundances of operational taxonomic units or genes, recent advances in experimental protocols give a more quantitative picture of microbial communities by pairing sequencing-based techniques with orthogonal measurements of microbial cell counts from the same sample. These tandem measurements provide absolute microbial count data albeit with a large excess of zeros due to limited sequencing depth. In this contribution we consider the fundamental statistical problem of estimating correlations and partial correlations from such quantitative microbiome data. To this end, we propose a semi-parametric rank-based approach to correlation estimation that can naturally deal with the excess zeros in the data. Combining this estimator with sparse graphical modeling techniques leads to the Semi-Parametric Rank-based approach for INference in Graphical model (SPRING). SPRING enables inference of statistical microbial association networks from quantitative microbiome data which can serve as high-level statistical summary of the underlying microbial ecosystem and can provide testable hypotheses for functional species-species interactions. Due to the absence of verified microbial associations we also introduce a novel quantitative microbiome data generation mechanism which mimics empirical marginal distributions of measured count data while simultaneously allowing user-specified dependencies among the variables. SPRING shows superior network recovery performance on a wide range of realistic benchmark problems with varying network topologies and is robust to misspecifications of the total cell count estimate. To highlight SPRING's broad applicability we infer taxon-taxon associations from the American Gut Project data and genus-genus associations from a recent quantitative gut microbiome dataset. We believe that, as quantitative microbiome profiling data will become increasingly available, the semi-parametric estimators for correlation and partial correlation estimation introduced here provide an important tool for reliable statistical analysis of quantitative microbiome data.

show abstract

Sparse semiparametric canonical correlation analysis for data of mixed types

Cited by 31 publications

References 39 publications

Shrinkage improves estimation of microbial associations under different normalization methods

Shrinkage improves estimation of microbial associations under different normalization methods

NetCoMi: Network Construction and Comparison for Microbiome Data in R

Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data

Contact Info

Product

Resources

About