glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data

Ahlmann-Eltze, Constantin; Huber, Wolfgang

doi:10.1093/bioinformatics/btaa1009

Cited by 178 publications

(165 citation statements)

References 18 publications

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“…To identify genes selectively expressed in OB projection neurons, we used the R-package glmGamPoi ( Ahlmann-Eltze and Huber, 2021 ). We combined the average expression levels of the top differentially expressed genes for each cell type and found it to be highly specific for each cluster ( Figure 3A,C ).…”

Section: Resultsmentioning

confidence: 99%

Molecular characterization of projection neuron subtypes in the mouse olfactory bulb

Zeppilli

Ackels

Attey

et al. 2021

eLife

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Projection neurons (PNs) in the mammalian olfactory bulb (OB) receive input from the nose and project to diverse cortical and subcortical areas. Morphological and physiological studies have highlighted functional heterogeneity, yet no molecular markers have been described that delineate PN subtypes. Here, we used viral injections into olfactory cortex and fluorescent nucleus sorting to enrich PNs for high-throughput single nucleus and bulk RNA deep sequencing. Transcriptome analysis and RNA in situ hybridization identified distinct mitral and tufted cell populations with characteristic transcription factor network topology, cell adhesion and excitability-related gene expression. Finally, we describe a new computational approach for integrating bulk and snRNA-seq data, and provide evidence that different mitral cell populations preferentially project to different target regions. Together, we have identified potential molecular and gene regulatory mechanisms underlying PN diversity and provide new molecular entry points into studying the diverse functional roles of mitral and tufted cell subtypes.

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Section: Resultsmentioning

confidence: 99%

Molecular characterization of projection neuron subtypes in the mouse olfactory bulb

Zeppilli

Ackels

Attey

et al. 2021

eLife

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“…Two subsets of all naïve CD4 and CD8 T cells from healthy paediatric donors were reclustered after hypervariable gene selection within each subset to generate the UMAP in Figure 3d. Differential gene expression across age within these subsets was tested by fitting a gamma-poisson generalized linear model on log 2 transformed age and by creating pseudo-bulks of each donor with the glmGamPoi package 91, 92 [https://doi.org/10.1093/bioinformatics/btaa1009].…”

Section: Methodsmentioning

confidence: 99%

The local and systemic response to SARS-CoV-2 infection in children and adults

Yoshida¹,

Worlock²,

Huang³

et al. 2021

Preprint

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While a substantial proportion of adults infected with SARS-CoV-2 progress to develop severe disease, children rarely manifest respiratory complications. Therefore, understanding differences in the local and systemic response to SARS-CoV-2 infection between children and adults may provide important clues about the pathogenesis of SARS-CoV-2 infection. To address this, we first generated a healthy reference multi-omics single cell data set from children (n=30) in whom we have profiled triple matched samples: nasal and tracheal brushings and PBMCs, where we track the developmental changes for 42 airway and 31 blood cell populations from infancy, through childhood to adolescence. This has revealed the presence of naive B and T lymphocytes in neonates and infants with a unique gene expression signature bearing hallmarks of innate immunity. We then contrast the healthy reference with equivalent data from severe paediatric and adult COVID-19 patients (total n=27), from the same three types of samples: upper and lower airways and blood. We found striking differences: children with COVID-19 as opposed to adults had a higher proportion of innate lymphoid and non-clonally expanded naive T cells in peripheral blood, and a limited interferon-response signature. In the airway epithelium, we found the highest viral load in goblet and ciliated cells and describe a novel inflammatory epithelial cell population. These cells represent a transitional regenerative state between secretory and ciliated cells; they were found in healthy children and were enriched in pediatric and adult COVID-19 patients. Epithelial cells display an antiviral and neutrophil-recruiting gene signature that is weaker in severe paediatric versus adult COVID-19. Our matched blood and airway samples allowed us to study the spatial dynamics of infection. Lastly, we provide a user-friendly interface for this data as a highly granular reference for the study of immune responses in airways and blood in children.

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“…In Figure 2A-B, we fit NB GLM to each gene in each dataset, in order to estimate the inverse overdispersion parameter θ. We model the observed counts for each gene using the following model gene_umi ∼ 1, and estimate parameters using glmGamPoi::glm_gp(gene_umi, model, offset=log(total_umi), size_factors=FALSE) using the glmGamPoi package (49). We perform this procedure for all genes where the variance of the observed counts exceeds the mean.…”

Section: Comparing Levels Of Overdispersion Across Datasetsmentioning

confidence: 99%

“…As outlier values can originate from multiple sources including the presence of cell doublets, errors in UMI collapsing, or ambient RNA, we have empirically improved performance when using the geometric mean instead of the arithmetic mean. Although sctransform supports multiple estimators for θ, we recommend the use of glmGamPoi (49), an alternate estimator that is more robust and faster.…”

Section: Modeling Scrna-seq Datasets With Sctransformmentioning

confidence: 99%

Comparison and evaluation of statistical error models for scRNA-seq

Choudhary

Satija

2021

Preprint

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Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate. Here, we analyze 58 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation. Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

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glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data

Cited by 178 publications

References 18 publications

Molecular characterization of projection neuron subtypes in the mouse olfactory bulb

Molecular characterization of projection neuron subtypes in the mouse olfactory bulb

The local and systemic response to SARS-CoV-2 infection in children and adults

Comparison and evaluation of statistical error models for scRNA-seq

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