Naught all zeros in sequence count data are the same

Silverman, Justin D.; Roche, Kimberly; Mukherjee, Sayan; David, Lawrence A.

doi:10.1016/j.csbj.2020.09.014

Cited by 98 publications

(101 citation statements)

References 84 publications

(116 reference statements)

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“…S15-S18). Our observation is consistent with another recent study [ 42 ], whose authors observed that “zero-inflated models lead to higher false-negative rates than identical non-zero-inflated models.” Hence, our real data analysis results are based on NB-GAM. However, realizing the complexity of biological systems and scRNA-seq protocols, we leave the choice between NB-GAM and ZINB-GAM as an option for users of PseudotimeDE, and we encourage users to plot their known DE genes as in Additional file 1 : Figs.…”

Section: Discussionsupporting

confidence: 93%

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

Song

2021

Genome Biol

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To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

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Section: Discussionsupporting

confidence: 93%

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

Song

2021

Genome Biol

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“…S15–S18). Our observation is consistent with another recent study [42], whose authors observed that “zero-inflated models lead to higher false-negative rates than identical non-zero-inflated models.” Hence, our real data analysis results are based on NB-GAM. However, realizing the complexity of biological systems and scRNA-seq protocols, we leave the choice between NB-GAM and ZINB-GAM as an option for users of PseudotimeDE, and we encourage users to plot their known DE genes as in Figs.…”

Section: Discussionsupporting

confidence: 93%

“…The zero inflation, or “dropout” issue, remains perplexing and controversial in the single-cell field [40–42]. The controversy is regarding whether excess zeros that cannot be explained by Poisson or negative binomial distributions are biological meaningful or not.…”

Section: Discussionmentioning

confidence: 99%

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

Song¹,

Li²

2020

Preprint

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In the investigation of molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along a continuous cell trajectory, which can be estimated by pseudotime inference from single-cell RNA-sequencing (scRNA-seq) data. However, existing methods that identify DE genes based on inferred pseudotime do not account for the uncertainty in pseudotime inference. Also, they either have ill-posed p-values that hinder the control of false discovery rate (FDR) or have restrictive models that reduce the power of DE gene identification. To overcome these drawbacks, we propose PseudotimeDE, a robust method that accounts for the uncertainty in pseudotime inference and thus identifies DE genes along cell pseudotime with well-calibrated p-values. PseudotimeDE is flexible in allowing users to specify the pseudotime inference method and to choose the appropriate model for scRNA-seq data. Comprehensive simulations and real-data applications verify that PseudotimeDE provides well-calibrated p-values essential for controlling FDR and downstream analysis and that PseudotimeDE is more powerful than existing methods to identify DE genes.

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“…Generally, microbiome omic data are complex, as it is (i) compositional, e.g., provided as relative abundances, which require specific considerations when selecting statistical analyses ( Gloor et al, 2017 ), (ii) highly sparse, such that the interpretation of zero-values generated from sampling, biological, or technical processes heavily affects data-derived conclusions ( Silverman et al, 2020 ), and (iii) high dimensional, which increases modeling difficulty due to the influence of feature selection that heavily affect potential predictions ( Bolón-Canedo et al, 2016 ). Furthermore, multi-omic studies may contain gaps within the omic spectrum, such that certain samples may not be represented within a certain omic layer ( Lloyd-Price et al, 2019 ).…”

Section: Analysis Of Community Characteristics and Dynamicsmentioning

confidence: 99%

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

et al. 2021

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In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

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Naught all zeros in sequence count data are the same

Abstract: Graphical abstract

Cited by 98 publications

References 84 publications

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

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