iDESC: Identifying differential expression in single-cell RNA sequencing data with multiple subjects

Liu, Yunqing; Wang, Ningya; Adams, Taylor; Schupp, Jonas C.; Wu, Weimiao; McDonough, John E.; Chupp, Geoffrey; Kaminski, Naftali; Wang, Zuoheng; Yan, Xiting

doi:10.1101/2022.02.07.479293

Cited by 3 publications

(3 citation statements)

References 62 publications

(71 reference statements)

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“…This is because even if the difference in mean expression is insignificant on the cohort level, many cells from a small subset of individuals can yield highly significant differences in mean expression on the cell level. Since previous work has explained the importance of accounting for within-individual variability [27,30,31], we focus on a simulation that illustrates the importance of accounting for the between-individual variability here.…”

Section: Design Of Simulation Studiesmentioning

confidence: 99%

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

Lin,

Qiu,

Roeder

2024

BMC Bioinformatics

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Background Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. Results We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals’ posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. Conclusions eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

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Section: Design Of Simulation Studiesmentioning

confidence: 99%

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

Lin,

Qiu,

Roeder

2024

BMC Bioinformatics

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“…Ultimately, Limma-Voom Consensus Correlation (Limma-VoomCC) was selected for DEG analysis based on the ability to reveal high expressing DEGs amongst diverse gene transcripts expressed 13 . Further, Limma-VoomCC controlled for the inter-correlations of cells from the same animals 14,15 . Overall, Limma-VoomCC analyses of all cell types in experiments 1 and 2 revealed a total of 1436 significant DEGs after adjusting for false discovery ( Supplemental Table 1 ).…”

Section: Sexually Dimorphic Trajectories Of Transcriptional Dysregula...mentioning

confidence: 99%

Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression

Sharifi,

Haghani,

Neier

et al. 2024

Preprint

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Dominant X-linked diseases are uncommon due to female X chromosome inactivation (XCI). While random XCI usually protects females against X-linked mutations, Rett syndrome (RTT) is a female neurodevelopmental disorder caused by heterozygous MECP2 mutation. After 6-18 months of typical neurodevelopment, RTT girls undergo poorly understood regression. We performed longitudinal snRNA-seq on cerebral cortex in a construct-relevant Mecp2e1 mutant mouse model of RTT, revealing transcriptional effects of cell type, mosaicism, and sex on progressive disease phenotypes. Across cell types, we observed sex differences in the number of differentially expressed genes (DEGs) with 6x more DEGs in mutant females than males. Unlike males, female DEGs emerged prior to symptoms, were enriched for homeostatic gene pathways in distinct cell types over time, and correlated with disease phenotypes and human RTT cortical cell transcriptomes. Non-cell-autonomous effects were dynamic across disease progression of Mecp2e1 mutant females, indicating wild-type-expressing cells normalizing transcriptional homeostasis. These results improve understanding of RTT progression and treatment.

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“…This is because even if the difference in mean expression is insignificant on the cohort level, many cells from a small subset of individuals can yield highly significant differences in mean expression on the cell level. Since previous work has explained the importance of accounting for within-individual variability [27,31,30], we focus on a simulation that illustrates the importance of accounting for the between-individual variability here.…”

Section: Design Of Simulation Studiesmentioning

confidence: 99%

eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

Lin,

Qiu,

Roeder

2023

Preprint

View full text Add to dashboard Cite

Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. In this paper, we develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals’ posterior mean distributions via a hierarchical model. We benchmark our method’s accuracy and power increase compared to other DE methods typically repurposed for analyzing cohort-wide differential expression based on simulated data. We then use the eSVD-DE to study the cohort-wide differential expression in idiopathic pulmonary fibrosis, varying severity of ulcerative colitis, and autism spectrum disorder. Altogether, eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction.

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iDESC: Identifying differential expression in single-cell RNA sequencing data with multiple subjects

Cited by 3 publications

References 62 publications

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression

eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

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