Integrating single-cell transcriptomic data across different conditions, technologies, and species

Abstract

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy

“…The Seurat pipeline was applied to combined datasets(Butler et al, 2018). Genes that were expressed in less than 3 cells and cells that expressed less than 400 and more than 3500 genes, were excluded.…”

Inflammatory Cytokine TNFα Promotes the Long-Term Expansion of Primary Hepatocytes in 3D Culture

“…We performed single-cell RNA-seq on 1192 hepatocytes (derived from one male and one female mice) and jointly analyzed these data by Principal Component Analysis (PCA). The top 10 PCs were used for clustering analysis (Butler et al, 2018) and the resulting clusters were visualized in t-distributed stochastic neighboring embedding (t-SNE) projections (Figure 4A). …”

“…Data obtained from GEO database was reprocessed using Cell Ranger version 2.1.1 with mm10 as the reference genome. Single cell data was analyzed using standard workflow on Seurat R Package (Butler et al, 2018). Female-derived cells could be distinguished from male-derived cells based on the Xist gene expression.…”

Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems

“…We use the Seruat package (http://satijalab.org/seurat/) (Butler et al, 2018) for PCA and tSNE analysis. We randomly picked 500 cells from each sample.…”

“…Based on the large data of wholetranscriptome, scRNA-seq has provided comprehensive landscapes of gene expression and their regulatory interactions at the finest resolution, enabling accurate and precise depiction of cell types and cell states (Grun and van Oudenaarden, 2015;Tanay and Regev, 2017;Wu et al, 2017). In the past decade, the sensitivity and precision of mRNA quantification through scRNA-seq has been greatly improved (Hashimshony et al, 2016;Picelli et al, 2014), bringing revolutionary discoveries to many fields such as cell type deconstruction in various tissues or organs (Jaitin et al, 2014;Lake et al, 2016;Papalexi and Satija, 2018;Treutlein et al, 2014;Villani et al, 2017), tracing cell lineage and fate commitment in embryonic development and cell differentiation (Olsson et al, 2016;Semrau et al, 2017;Tirosh et al, 2016;Yan et al, 2013), inferring transcription dynamics and regulatory networks (Deng et al, 2014;Dixit et al, 2016) and identifying the development, 3 evolution and heterogeneity in tumors (Patel et al, 2014;Treutlein et al, 2014;Venteicher et al, 2017) .…”