Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq.

Dylan Kotliar,Douglas A Melton,Eran Hodis,Adrian Veres,Pardis C Sabeti,M Aurel Nagy,Shervin Tabrizi

doi:10.7554/elife.43803

Abstract

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell's expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis.

Highlights

Genes act in concert to maintain a cell’s identity as a specific cell type, to respond to external signals, and to carry out complex cellular activities such as replication and metabolism
We evaluated this in simulated data of 15,000 cells composed of 13 cell types, one cellular activity program that is active to varying extents in a subset of cells of four cell types, and a 6% doublet rate (Figure 2A)
We found that consensus non-negative matrix factorization (cNMF) was most accurate at inferring genes in the activity program, with a sensitivity of 61% at a false discovery rate (FDR) of 5% (Figure 2e). cICA and the ground-truth clustering were the most accurate with 57% and 56% sensitivity at a 5% FDR, respectively. cNMF performed the best at inferring identity gene expression program (GEP) of the 4 cell types that expressed the activity (Figure 2— figure supplement 5)

Summary

Introduction

Genes act in concert to maintain a cell’s identity as a specific cell type, to respond to external signals, and to carry out complex cellular activities such as replication and metabolism. Coordinating the necessary genes for these functions is frequently achieved through transcriptional co-regulation, where genes are induced together as a gene expression program (GEP) in response to the appropriate internal or external signal (Eisen et al, 1998; Segal et al, 2003). Single-cell RNA-Seq (scRNA-Seq) has greatly enhanced our potential to resolve GEPs by making it possible to observe variation in gene expression over many individual cells. Even so, inferring GEPs remains challenging as scRNA-Seq data is noisy and high-dimensional, requiring computational approaches to uncover the underlying patterns. Methodological advances in dimensionality reduction, clustering, lineage trajectory tracing, and differential expression analysis have helped overcome some of these issues (Amir et al, 2013; Kharchenko et al, 2014; Satija et al, 2015; Trapnell et al, 2014)

Methods

Results

Conclusion