A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate...

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Veröffentlicht in:Nature communications Jg. 15; H. 1; S. 5229 - 12
Hauptverfasser: Chen, Minhui, Dahl, Andy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.06.2024
Nature Publishing Group
Nature Portfolio
Schlagworte:
38
631/114/2415
631/1647/2217/2218
631/61/212/2019
Biology
Cell differentiation
Cell Differentiation - genetics
Endoderm
Endoderm - cytology
Endoderm - metabolism
Gene expression
Gene Expression Profiling - methods
Gene sequencing
Genes
Humanities and Social Sciences
Humans
Induced Pluripotent Stem Cells - cytology
Induced Pluripotent Stem Cells - metabolism
multidisciplinary
Pluripotency
Ribonucleic acid
RNA
RNA-Seq - methods
Science
Science (multidisciplinary)
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Statistical analysis
Stem cells
Transcriptome
Transcriptomes
Transcriptomics
Variability
Variation
ISSN:2041-1723, 2041-1723
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Zusammenfassung:Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq. Single-cell RNA sequencing (scRNA-seq) is widely used to characterize cell types based on their average gene expression profiles, however most studies do not consider cell type-specific variation across individuals. Here the authors introduce a model to study cell type-specificity of inter-individual variation in scRNA-seq data and show that it can identify biologically meaningful signals missed by conventional differential expression tests.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-49242-9