Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments

By Stephanie C. Hicks, F. W. Townes, Mingxiang Teng, Rafael A. Irizarry. bioRxiv, 2017

92 citations recorded by the source corpus.

Until recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-Seq and scRNA-seq data are markedly different. In particular, unlike RNA-Seq, the majority of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, gene expressing RNA, but not at a sufficient level to detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4e70d92bcf05a14abb6ae2df8a965171ae883274

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, unknown): “All of these factors result in batch effects [5] [6], where the expression of genes in one batch differs systematically from those in another batch.”
Orchestrating single-cell analysis with Bioconductor (2019, background): “[25] Stephanie C Hicks, F William Townes, Mingxiang Teng, and Rafael A Irizarry.”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, background): “These factors would introduce systematic error and confound the technical and biological variability, leading to that the gene expression proﬁle in one batch systematically diﬀers from that in another (Leek et al., 2010; Hicks et al., 2018).”
Identification of cell types in a mouse brain single-cell atlas using low sampling coverage (2018, methodology): “To account for potentially pervasive batch effects in single-cell datasets [11, 12], each subset was randomly selected from the libraries represented in the original dataset, and Louvain-Jaccard clustering generated clusters that each represented many of these libraries [7] (Additional file 1: Figur”
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis (2020, background): “A primary challenge in scRNA-seq analysis is analyzing the ever increasing number of cells, which can be thousands to millions in large projects such as the Human Cell Atlas 1 .”
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics (2018, background): “Similarly, technical and “batch” effects have been demonstrated to mask biological signal in the integrated analysis of scRNA-seq experiments [12], necessitating experimental solutions to mitigate these challenges.”
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, methodology): “Focusing on the entire negative control datasets, we applied PCA to the log transformed CPMs and observed a strong correlation between the first principal component (PC) and the fraction of zeros, consistent with [28].”
Embedding to reference t-SNE space addresses batch effects in single-cell classification (2019, background): “It has been well documented that even small differences in the experimental setup of cell-dissociation, handling protocols, librarypreparation technologies, or sequencing platforms can significantly affect the resulting gene-expression measurements (Tung et al., 2017; Hicks et al., 2018).”