A novel algorithm for the collective integration of single cell RNA-seq during embryogenesis

Abstract

25Single cell RNA-seq (scRNA-seq) over specified time periods has been widely 26 used to dissect the cell populations during mammalian embryogenesis. 27Integrating such scRNA-seq data from different developmental stages and from 28 different laboratories is critical to comprehensively define and understand the 29 molecular dynamics and systematically reconstruct the lineage trajectories. Here, 30we describe a novel algorithm to integrate heterogenous temporal scRNA-seq 31 datasets and to preserve the global developmental trajectories. We applied this 32 algorithm and approach to integrate 3,387 single cells from seven heterogenous 33 temporal scRNA-seq datasets, and reconstructed the cell atlas of early mouse 34 cardiovascular development from E6.5 to E9.5. Using this integrated atlas, we 35 identified an Etv2 downstream target, Ebf1, as an important transcription factor 36 for mouse endothelial development. 37 38 function based on stochastic neighbor assignment in the transformed space, 129 similar to the neighborhood component analysis (NCA) [23]. It should be noted 130 that although the CLN was evaluated for an individual time point, a single linear 131 transformation applies to the cells from all time points. Thus, unlike our previous 132 work on visualizing temporal scRNA-seq data[10], the cells from different time 133 points will not be confined to the same transformed (low) dimensional space. 134 135

SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks

“…Simulation tools (''simulators'') for single-cell expression data have been reported in various forms. Several studies offering novel analysis tools use in-house simulators to benchmark those tools (Van den Berge et al, 2018;Campbell and Yau, 2018;Chen et al, 2020;Gong et al, 2019;Korthauer et al, 2016;Risso et al, 2018;Wolf et al, 2018), while other studies specifically develop simulators for use by the community (Holm, 2019;Marouf et al, 2020;Papadopoulos et al, 2019;Vieth et al, 2017;Zappia et al, 2017;Zhang et al, 2019). Most of these simulators are geared toward capturing the noise characteristics of technologies such as single-cell RNA-seq (scRNA-seq), by first estimating statistical quantities describing real datasets and then sampling singlecell expression profiles from probability distributions that mirror those quantities.…”

Computational methods for the integrative analysis of single-cell data

“…Specifically, the method named single-cell neighborhood component analysis (scNCA) determines cell context likelihood neighbors by comparing, within each time point, the distances between cells from two batches with a null model. Then, it applies a batch-specific linear transformation (similar to neighborhood component analysis, NCA) that maximizes the closeness of cells with high context likelihood, preserving the local trajectories [ 23 ]. The anchor concept underlies also the approach developed in scMerge [ 24 ].…”

A single-cell expression simulator guided by gene regulatory networks

“…Simulation tools ("simulators") for single-cell expression data have been reported in various forms. Several studies offering novel analysis tools use in-house simulators to benchmark those tools 8,[21][22][23][24][25][26] , while other studies specifically develop simulators for use by the community [27][28][29][30][31][32] . Most of these simulators are geared towards capturing the noise characteristics of technologies such as single-cell RNA-seq (scRNA-seq), by first estimating statistical quantities describing real data sets and then sampling single-cell expression profiles from probability distributions that mirror those quantities.…”