NEBULA: a fast negative binomial mixed model for differential expression and co-expression analyses of large-scale multi-subject single-cell data

He, Liang; Kulminski, Alexander M.

doi:10.1101/2020.09.24.311662

Cited by 6 publications

(13 citation statements)

References 71 publications

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“…All comparisons were formalized by fitting a negative binomial mixed model to account for cell-subject membership associations. All models were implemented using the R package Nebula 54 . The analysis was conducted in the joint data set, including all annotated neuronal cells from the HIP and EC samples.…”

Section: Methodsmentioning

confidence: 99%

“…Gene expression associations were assessed using a fast implementation of a negative binomial mixed model that accounts for both subject-level and cell-level overdispersion 54 . To estimate the association between gene expression and Braak stage groups, while accounting for sex, age of death, postmortem interval.…”

Section: Methodsmentioning

confidence: 99%

“…Only genes having an estimated FDR<0.05 were considered as NFT-responsive. All association models were implemented using the R package Nebula 54 .…”

Section: Methodsmentioning

confidence: 99%

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Single-cell anatomical analysis of human hippocampus and entorhinal cortex uncovers early-stage molecular pathology in Alzheimer’s disease

Dávila-Velderrain

Mathys

Mohammadi

et al. 2021

Preprint

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The human hippocampal formation plays a central role in Alzheimer’s disease (AD) progression, cognitive traits, and the onset of dementia; yet its molecular states in AD remain uncharacterized. Here, we report a comprehensive single-cell transcriptomic dissection of the human hippocampus and entorhinal cortex across 489,558 cells from 65 individuals with varying stages of AD pathology. We transcriptionally characterize major brain cell types and neuronal classes, including 17 glutamatergic and 8 GABAergic neuron subpopulations. Combining evidence from human and mouse tissue-microdissection, neuronal cell isolation and spatial transcriptomics, we show that single-cell expression patterns capture fine-resolution neuronal anatomical topography. By stratifying subjects into early and late pathology groups, we uncover stage-dependent and cell-type specific transcriptional modules altered during AD progression. These include early-stage cell-type specific dysregulation of cellular and cholesterol metabolism, late-stage neuron-glia alterations in neurotransmission, and late-stage signatures of cellular stress, apoptosis, and DNA damage broadly shared across cell types. Late-stage signatures show signs of convergence in hippocampal and cortical cells, while early changes diverge; highlighting the relevance of characterizing molecular pathology across brain regions and AD progression. Finally, we characterize neuron subregion-specific responses to AD pathology and show that CA1 pyramidal neurons are the most transcriptionally altered while CA3 and dentate gyrus granule neurons the least. Our study provides a valuable resource to extend cell type-specific studies of AD to clinically relevant brain regions affected early by pathology in disease progression.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Single-cell anatomical analysis of human hippocampus and entorhinal cortex uncovers early-stage molecular pathology in Alzheimer’s disease

Dávila-Velderrain

Mathys

Mohammadi

et al. 2021

Preprint

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“…The code of NEBULA (v1.1.7) can be downloaded and installed via https://github.com/ lhe17/nebula, and archived in Zenodo 80 . The computational tools used for the data analysis include scCATCH v2.1 (https://github.com/ZJUFanLab/scCATCH), lme4 v1.1-26 (https://cran.r-project.org/web/packages/lme4/index.html), glmmTMB v1.0.2.1 (https://cran.r-project.org/web/packages/glmmTMB/index.html), MASS 7.3-53.1 (https://cran.r-project.org/web/packages/MASS/index.html), INLA v20.04.18 (https:// www.r-inla.org/download-install).…”

Section: Data Availabilitymentioning

confidence: 99%

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

Dávila-Velderrain

Sumida

et al. 2021

Commun Biol

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The increasing availability of single-cell data revolutionizes the understanding of biological mechanisms at cellular resolution. For differential expression analysis in multi-subject single-cell data, negative binomial mixed models account for both subject-level and cell-level overdispersions, but are computationally demanding. Here, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA). The speed gain is achieved by analytically solving high-dimensional integrals instead of using the Laplace approximation. We demonstrate that NEBULA is orders of magnitude faster than existing tools and controls false-positive errors in marker gene identification and co-expression analysis. Using NEBULA in Alzheimer’s disease cohort data sets, we found that the cell-level expression of APOE correlated with that of other genetic risk factors (including CLU, CST3, TREM2, C1q, and ITM2B) in a cell-type-specific pattern and an isoform-dependent manner in microglia. NEBULA opens up a new avenue for the broad application of mixed models to large-scale multi-subject single-cell data.

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“…CR1 is one of the key genes involved in the complement system, and shows the highest expression level in oligodendrocytes followed by microglia among the neural cells in our data. Genes in the complement system are abundantly expressed in microglia, and are co-expressed with APOE (He, 2021). Our findings suggest that oligodendrocytes might play a role in mediating the effect of rs2093760.…”

Section: Discussionmentioning

confidence: 63%

Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer’s disease-associated genetic variants

Loika

Kulminski

2021

Preprint

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Elucidating regulatory effects of Alzheimer's disease (AD)-associated genetic variants is critical for unraveling their causal pathways and understanding the pathology. However, their cell-type-specific regulatory mechanisms in the brain remain largely unclear. Here, we conducted an analysis of allele-specific expression quantitative trait loci (aseQTLs) for 33 AD-associated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and >0.25 million single nuclei. We develop a flexible framework using a hierarchical Poisson mixed model unifying samples in both allelic and genotype-level expression data. We identified 24 AD-associated variants (~73%) that are allele-specific eQTLs (aseQTLs) in at least one brain region. Multiple aseQTLs are region-dependent or exon-specific, such as rs2093760 with CR1, rs7982 with CLU, and rs3865444 with CD33. Notably, the APOE e4 variant reduces APOE expression across all regions, even in healthy controls. In pinpointing the cell types responsible for the observed region-level aseQTLs, we found rs2093760 as an aseQTL of CR1 in oligodendrocytes but not in microglia. Many AD-associated variants are aseQTLs in microglia or monocytes of immune-related genes, including HLA-DQB1, HLA-DQA2, CD33, FCER1G, MS4A6A, SPI1, and BIN1, highlighting the regulatory role of AD-associated variants in the immune response. These findings provide further insights into potential causal pathways and cell types mediating the effects of the AD-associated variants.

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NEBULA: a fast negative binomial mixed model for differential expression and co-expression analyses of large-scale multi-subject single-cell data

Cited by 6 publications

References 71 publications

Single-cell anatomical analysis of human hippocampus and entorhinal cortex uncovers early-stage molecular pathology in Alzheimer’s disease

Single-cell anatomical analysis of human hippocampus and entorhinal cortex uncovers early-stage molecular pathology in Alzheimer’s disease

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer’s disease-associated genetic variants

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