Single-cell multi-omic integration compares and contrasts features of brain cell identity

By Joshua D. Welch, Velina Kozareva, Ashley N. Ferreira, C. Vanderburg, Evan Z. Macosko. Cell, 2019

1,066 citations recorded by the source corpus.

Summary Defining cell types requires integrating diverse single-cell measurements from multiple experiments and biological contexts. To flexibly model single-cell datasets, we developed LIGER, an algorithm that delineates shared and dataset-specific features of cell identity. We applied it to four diverse and challenging analyses of human and mouse brain cells. First, we defined region-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis. Second, we analyzed expression in the human substantia nigra, comparing cell states in specific donors and relating cell types to those in the mouse. Third, we integrated in situ and single-cell expression data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we jointly defined mouse cortical cell types using single-cell RNA-seq and DNA methylation profiles, revealing putative mechanisms of cell-type-specific epigenomic regulation. Integrative analyses using LIGER promise to accelerate investigations of cell-type definition, gene regulation, and disease states.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7a5c3cf34340c11b83f770b8f6b8a3e489c2edbc

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Citations in the corpus

Computational principles and challenges in single-cell data integration (2021, neutral): “LIGER performs integrative non-negative matrix factorization (NMF) and disentangles dataset-specific factors from shared factors, followed by construction of a neighborhood graph using only the shared factors.”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, methodology): “For example, mCH had higher expression predictability for longer genes, which was consistent with previous studies 17,41 , while chromatin accessibility contributed more to genes with higher expression variability (Supplementary Fig.”
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “For the spatial distribution of Rprm , the results generated by SpaGE and Seurat had the highest PCC values (PCC = 0.79), followed by SpaOTsc, gimVI, Tangram, and LIGER (PCC = 0.78, 0.71, 0.66, 0.65) (Fig.”
The digestive system is a potential route of 2019-nCov infection: a bioinformatics analysis based on single-cell transcriptomes (2020, methodology): “Colon Dataset Single cell data expression matrix was processed with the R package LIGER[18] and Seurat[17].”
Spatial components of molecular tissue biology (2022, positive): “In contrast to deconvolution approaches, that explicitly model the spatial observation as an aggregate of cells, label projection methods have been developed to map the cellular states identified in scRNA-seq experiments to lower resolution spatial data 86,140,141 .”
Inferring spatial and signaling relationships between cells from single cell transcriptomic data (2020, methodology): “Non-negative matrix factorization can also be used to construct common low-dimensional spaces of multiple data-sets 17 .”
Comprehensive integration of single-cell data (2018, background): “For example, Welch et al. (2019) have recently introduced LIGER, which leverages integrative non-negative matrix factorization (NMF) to identify shared and speciﬁc sources of variation across datasets.”
Methods and applications for single-cell and spatial multi-omics (2023, methodology): “To this end, linear matrix decomposition approaches such as principal component analysis (PCA), canonical correlation analysis (CCA) 139 and non-negative matrix factorization (NMF) 140,141 have proven successful to identify latent factors underlying cellular heterogeneity.”
Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, positive): “We and others have proposed methods to map data-sets across modalities 19–21 , for example, taking the gene body sum of ATAC-seq signal (or the inverse of the DNA methylation levels) as a proxy for transcriptional output.”
Microglia states and nomenclature: A field at its crossroads (2022, positive): “78,79 A practical limitation of solely deﬁning functional states by their transcriptional signature is that mRNA expression may not directly predict protein levels.”