Papers on Grappling With Technical Variation in Single Cell RNA-seq Analysis

Thanks to the advances in amplification technologies, Single-Cell sequencing is on the rise.  As the use of single-cell sequencing has started coming out of exclusive labs to a broader audience, Nature Methods chose Single-Cell Sequencing as the Method of the year 2013,

One of the biggest challenges, like any new genome technology, is making sense of technical aspects of single-cell sequencing and its effect on data. Here is a mind-boggling statistic, if you are in any doubt of the technical challenges in using single cell sequencing data. A recent paper using single cell transcriptomics to study random mono-allelic expression found that “technical losses of RNA contributed as much as 66% of mono-allelic expression observed” [our blog post on the paper].

We are seeing a slew of interesting papers presenting methods to understand technical and biological variations associated with single cell RNA-seq data.  The most recent being a paper in Nature Methods presenting a Bayesian approach to do single-cell differential analysis.  And  about a month ago, the team behind Cufflinks presented a new method and software Monocle for analyzing single-cell RNA-seq data from developmental stages.

Here is the list of interesting papers that came out recently and presented methods to analyze single-cell RNA-seq data.  Hoping to catch up with some of these papers and write a summary here.

May 2014: Bayesian approach to single-cell differential expression analysis

by Peter V Kharchenko, Lev Silberstein & David T Scadden, Nature Methods, (2014) doi:10.1038/nmeth.2967

Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.

April 2014: Validation of noise models for single-cell transcriptomics.

by Dominic Grün, Lennart Kester & Alexander van Oudenaarden. Nature Methods (2014) doi:10.1038/nmeth.2930

Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.

March 2014: The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells,

by Cole Trapnell, Davide Cacchiarelli, Jonna Grimsby, Prapti Pokharel, Shuqiang Li, Michael Morse, Niall J Lennon, Kenneth J Livak, Tarjei S Mikkelsen & John L Rinn, Nature Biotechnology, 32, 381–386 (2014)

Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers1. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.

September 2013: Accounting for technical noise in single-cell RNA-seq experiments.

by Philip Brennecke, Simon Anders,Jong Kyoung Kim, Aleksandra A Ko?odziejczyk, Xiuwei Zhang, Valentina Proserpio, Bianka Baying, Vladimir Benes, Sarah A Teichmann, John C Marioni & Marcus G Heisler. Nature Methods, 10, 1093–1095,(2013)

Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.

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