Power Analysis of Single Cell RNA-Sequencing Experiments

By Valentine Svensson, K. Natarajan, Lam-Ha Ly, Ricardo J. Miragaia, C. Labalette, I. Macaulay, A. Cvejic, Sarah A. Teichmann. Nature Methods, 2016

532 citations recorded by the source corpus.

Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/893d91b4a527865c25d64abaedd2dfb5008aacd7

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Citations in the corpus

The Human Cell Atlas (2017, methodology): “Power analyses provides a framework for comparing the sensitivity and accuracy of these approaches (Svensson et al., 2016; Ziegenhain et al., 2017).”
Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, background): “Recent benchmarking studies of diverse scRNA-seq 51,52 technologies have consistently demonstrated that no single method is uniformly superior, but rather, that each has individual strengths and weaknesses, further highlighting the potential value of data integration.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “A more detailed explanation and comparison of different protocols can be found in Ziegenhain et al (2017); Macosko et al (2015); Svensson et al (2017).”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, unknown): “By integrating our Cj7 mES Microwell-seq data with the published comparative analysis for different single-cell mRNA-seq methods (Svensson et al., 2017; Ziegenhain et al., 2017), we observed a sensitivity and accuracy that were comparable with those of other available methodologies (Figures…”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “com/vals/umis) for handling raw read data [51], the tree-inference tool ECLAIR (https:// github.”
Comparison and evaluation of statistical error models for scRNA-seq (2021, background): “Sample name Technology Tissue Datatype Raw data Reference TechnicalControl1 (ChromiumV1) ChromiumV1 TechnicalControl technical-control link (8, 51) TechnicalControl2 (ChromiumV1) ChromiumV1 TechnicalControl technical-control link (8, 51) TechnicalControl (inDrops) inDrops TechnicalControl technical-”
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2018, unknown): “This is likely a consequence of the fact that the capture efficiency of molecules in droplet-based experiments varies by as much as an order of magnitude [17].”
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, unknown): “In line with previous ndings [8, 14], the saturation curve of Exon+Intron counting runs parallel to the one for Exon counting, both indicating that a sequencing depth of one million reads per cell is su cient for these libraries.”