Differential analysis of binarized single-cell RNA sequencing data captures biological variation

Bouland, Gerard A; Mahfouz, Ahmed; Reinders, Marcel J. T.

doi:10.1093/nargab/lqab118

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…An alternative approach such as comparing the proportion of expression across groups could be employed. Although it is beyond the scope of this discussion, there are methods that attempt to perform this type of proportional zero binary expression analysis [ 44 ]. Special attention should be paid to the identities of genes that fall into this category when considering DEG results as their results may be unreliable.…”

Section: Discussionmentioning

confidence: 99%

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Gagnon

Pi²,

Ryals³

et al. 2022

Life

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To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

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Section: Discussionmentioning

confidence: 99%

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Gagnon

Pi²,

Ryals³

et al. 2022

Life

View full text Add to dashboard Cite

show abstract

“…An alternative approach such as comparing the proportion of expression across groups could be employed. Although beyond the scope of this discussion, there are methods that attempt to perform this type of proportional zero binary expression analysis [44]. Special attention should be paid to the identities of genes that fall into this category when considering DEG results as their results may be unreliable.…”

Section: Discussionmentioning

confidence: 99%

Recommendations of Scrna-Seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Gagnon¹,

Pi²,

Ryals³

et al. 2022

Preprint

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To guide analysts to select the right tool and parameters in differential gene expression analysis of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline (https://github.com/interactivereport/scRNAseq_DE) for single cell analysis will enable scientists to better understand cell-type specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations of filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

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“… Qiu (2020) found that “dropout pattern in scRNA-seq data is as informative as the quantitative expression of highly variable genes” and suggested “embracing” dropout by using binarized data rather than transcript counts instead of ignoring it. Bouland et al (2021) found that a similar approach did indeed capture biologically meaningful variation in single cell transcriptomes. Nevertheless ( Jiang et al, 2022 ), criticized methods that use binarized gene expression data, because they ignore information from differential expression of the same genes in different cell types.…”

Section: Categories States and Semaphorantsmentioning

confidence: 96%

Cell types as species: Exploring a metaphor

Doyle

2022

Front. Plant Sci.

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The concept of “cell type,” though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, “species.” In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the “cell types as species” metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

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Differential analysis of binarized single-cell RNA sequencing data captures biological variation

Cited by 12 publications

References 35 publications

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Recommendations of Scrna-Seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Cell types as species: Exploring a metaphor

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