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MINI REVIEW article

Front. Genet., 21 September 2016
Sec. Genomic Assay Technology
This article is part of the Research Topic Single Cell Omics View all 11 articles

Single-Cell Transcriptomics Bioinformatics and Computational Challenges

\r\nOlivier B. Poirion&#x;Olivier B. Poirion1Xun Zhu,&#x;Xun Zhu1,2Travers Ching,Travers Ching1,2Lana Garmire*Lana Garmire1*
  • 1Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
  • 2Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA

The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.

Introduction

Characterization of genomic signatures in individual patients is a key step toward the realization of precision medicine. Recently, next-generation sequencing (NGS) based RNA expression profiling (RNA-seq) has made broad impacts on biomedical fields. However, population-averaged RNA-seq has limited discovery power, and it can also mask the presence of rare subpopulations of cells (such as cancer stem cells) and thus may overlook important biological insights. The emerging single-cell RNA-Seq (scRNA-Seq) technology is designed to overcome these limitations by investigating expression profiles at the cell level. In just a few years, the number scRNA-Seq experiments has grown beyond exponentially. This new approach offers the potential to revolutionize our understanding of diseases and associated biological processes, with the capacity to reveal the intercellular heterogeneity within a specific tissue at an unprecedented resolution (Yan et al., 2013; Trapnell et al., 2014). Using single-cell level features, we can infer cell lineages (Treutlein et al., 2014), identify subpopulations (Trapnell et al., 2014) and highlight cell-specific biological characteristics (Tang et al., 2010). Moreover, single-cell analyses have already demonstrated their utilities in the clinical applications, ranging from characterizing cancer cells subpopulations (Navin et al., 2011; Patel et al., 2014; Ting et al., 2014), highlighting specific resistance mechanisms (Kim, K. T. et al., 2015; Miyamoto et al., 2015) to being used as diagnostic tools (Ramsköld et al., 2012; Kvastad et al., 2015).

Despite the expansion of scRNA-Seq studies and rapid maturing of experimental methods, major analytical challenges remain as the consequences of experimentation. One major challenge is that scRNA-Seq datasets present a very high level of noise (Brennecke et al., 2013; Kharchenko et al., 2014). Much of the noise is due to the nature of single-cell technologies. Because of the extremely low amount of starting biological material in the single cell, amplification processes are required. These procedures are prone to distortion and contamination (Leng et al., 2015). To tackle these issues, rigorous efforts have been made to develop analytical methods for scRNA-Seq data. Here, we summarize current state-of-the-art bioinformatics analysis tools and methods for scRNA-Seq (Figure 1 and Table 1), and address some critical analytical challenges that we are facing. The first section describes specific pre-processing steps for noise removal of scRNA-Seq datasets. The second section reviews specific scRNA-Seq bioinformatics analysis procedures with emphasis on subpopulation detection. The third section focuses on microevolution analysis for scRNA-Seq data. In the last section, we highlight the challenges to be addressed and work to be accomplished in scRNA-Seq bioinformatics field.

FIGURE 1
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Figure 1. General workflow of Single-cell analysis.

TABLE 1
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Table 1. List of single-cell analytical tools mentioned in this chapter.

Data Preprocessing and Noise Removal

Quality Control

scRNA-Seq experiments generate FASTQ files from the sequencing machine, which contain millions of reads composed of RNA sequences and add-on sequences (UMI tag and the cell tag etc). These reads need to be pre-processed before being aligned back to the reference genome. For scRNA-seq, pre-processing and quality control (QC) analyses similar to bulk RNA-seq are used. Cutadapt (Martin, 2011) is a tool that removes adapter sequences, and Trimmomatic (Bolger et al., 2014) performs quality-based trimming in addition to removing adapter sequence. These tools are commonly used in scRNA-seq experiments (Treutlein et al., 2014; Handel et al., 2016; Hou et al., 2016). Other generic quality control tools such as FASTQC or HTQC (Yang et al., 2013) might also be useful to produce quality metrics. Finally, it is worth noting that platform-specific QC tools such as SolexaQA (Cox et al., 2010) provide QC pipelines specific for Illumina sequencing, with trimming and quality-based filtering.

Other QC procedures for scRNA-seq involve the analysis of the expression of housekeeping genes (Ting et al., 2014; Treutlein et al., 2014), overall gene expression patterns (Zeisel et al., 2015) and the number of genes or reads detected per cell (Kumar et al., 2014). However, one issue of these approaches is that the thresholds chosen for filtering are arbitrary and should differ according to the dataset (Jiang, P. et al., 2016). SinQC (Jiang, P. et al., 2016) and SCell (Diaz et al., 2016) are two QC tools specifically designed for scRNA-seq data. SinQC uses sequencing library quality to confirm gene expression outliers. It computes different quality metrics (e.g., total number of mapped reads, mapping rate and library complexity) to identify a user-specified fraction of the dataset as noise. SCell is a versatile tool that allows for outlier detection. It estimates genes that are expressed at the background level using Gini index, which measures statistical dispersion, and removes samples whose background fraction is significantly higher than the average. Recently, a new mapping and quality assessment pipeline Celloline detects low quality cells from expression profiles, using curated biological and technical features (Ilicic et al., 2016).

Alignment

To our knowledge, there are currently no specific aligners dedicated to scRNA-seq, and scRNA-seq studies use existing aligners made for bulk RNA-Seq. Tophat is one of the most popular aligners capable of detecting novel splice (Trapnell et al., 2009; Kim et al., 2013), and it is widely used in scRNA-seq studies (Treutlein et al., 2014; Fan et al., 2016; Freeman et al., 2016; Handel et al., 2016; Hou et al., 2016). RNA-Seq by Expectation Maximization, or RSEM, is a popular framework that includes an aligner (Li and Dewey, 2011). It is also used in some scRNA-seq studies (Gao et al., 2016; Kimmerling et al., 2016; Meyer et al., 2016). Other aligners used in scRNA-Seq studies include MapSplice (Wang et al., 2010), GSNAP (Brennecke et al., 2013; Buettner et al., 2015; Wu et al., 2016), and STAR (Dobin and Gingeras, 2015; Moignard et al., 2015; Petropoulos et al., 2016). Among these aligners, TopHat and STAR were found to be about one to two magnitudes faster than GSNAP and MapSplice (Engström et al., 2013). More recently developed aligners include Kallisto (Bray et al., 2016) and HISAT (Kim, D. et al., 2015). Kallisto uses pseudo-alignment with hashing de Bruijn graphs and avoids alignment altogether, which drastically improves the speed of expression quantification. HISAT (hierarchical indexing for spliced alignment of transcripts) seems also promising in term of the speed and accuracy. It is worth mentioning that some major scRNA-Seq methods do not get enough coverage across the gene to measure alternative splicing, therefore algorithms for isoform measurements are not as critical in scRNA-Seq, at least at this stage.

Feature Quantification

Feature quantification is the process of converting alignment results into a gene expression profile. An expression profile is conventionally represented as a numeric matrix where rows are genes and columns are cells. Each entry in the matrix is the abundance of a particular gene or transcript in a particular sample. Just as is the case for aligners, most scRNA-Seq studies use canonical feature quantification methods applied to bulk RNA-Seq.

Quantification methods for gene expression differ dramatically. The simplest approach, employed by programs such as HTSeq (Anders et al., 2014) and FeatureCounts (Liao et al., 2013), is to count the number of reads located within the boundaries of a gene (Liao et al., 2013; Anders et al., 2014). These programs have simple but flexible parameters for determining read counts in the case of overlapping genes, and were used in some scRNA-Seq studies (Brennecke et al., 2013; Moignard et al., 2015; Fan et al., 2016; Handel et al., 2016). More sophisticated approaches calculate probabilistic estimates of gene expression. For example, RSEM and Cufflinks both employ a maximum likelihood approach (Trapnell et al., 2010; Li and Dewey, 2011). These programs are based on statistical models where reads in a RNA-Seq sample are observed random variables predicted from the latent variables, such as the transcript sequence, strand and length. The new Kallisto pipeline (Bray et al., 2016) as described before, is shown to have up to two orders of magnitude speed improvement over previous aligner-quantifier combinations (Ntranos et al., 2016). Interestingly, while probabilistic approaches are conceptually more refined, simple counting programs such as HTSeq and FeatureCounts showed comparable or even stronger performance (Chandramohan et al., 2013; Fonseca et al., 2014), suggesting that these probabilistic models are yet to be improved.

Given the uncertainties of quantifying fragments post-amplification, a new technique was shown to reduce amplification noise by introducing random sequences called unique molecular identifiers, or UMIs (Islam et al., 2014). UMIs are tagged on individual RNA molecules before amplification and used for tracking transcripts directly rather than using sophisticated statistical modeling. This approach may lead to a different workflow than conventional fragment-based quantification methods (e.g., gene filtering and normalization).

Gene Filtering

Due to the high level of noise in scRNA-Seq datasets, it is necessary to filter out low quality genes and samples. Various practices have been made to filter out genes that are expressed in too few samples (Brennecke et al., 2013; Treutlein et al., 2014; Petropoulos et al., 2016). Usually, a gene is defined as “expressed” by a minimal expression level threshold. For experiments that quantify gene expression with fragment counting, an FPKM (Fragment per Kilobase per Million Reads) threshold is appropriate. Common FPKM thresholds are 1 (Freeman et al., 2016) and 10 (Petropoulos et al., 2016). Other studies also set the threshold by Transcript Per Million (TPM) instead of FPKM (Meyer et al., 2016). Yet better filtering reference could come from External RNA Controls Consortium (ERCC) spike-ins added to the experiment, which provides calibration of the relative amount of starting material (Brennecke et al., 2013; Treutlein et al., 2014).

Recently, specific methods have been developed to filter genes from scRNA-seq dataset. OEFinder is designed to identify artifact genes from scRNA-seq experiments using the Fluidigm C1 platform for cell capture (Leng et al., 2016). For experiments that quantify gene expression with UMI counting, one can directly set up a molecule number threshold, e.g., 25 (Zeisel et al., 2015). It is also recommended to remove UMIs that have reads <1/100 of average non-zero UMI reads, in order to avoid erroneous UMIs generated during amplification.

Removal of Confounding Factors

When the entire data set consists of several runs of experiments with potentially varied conditions, systematic variations called batch effects might be introduced. These artifacts may pose substantial problems to downstream statistical analysis, or even mask biological signals. For studies concerning over-dispersion of gene expression, it is necessary to factor out the extra variance caused by the systematic differences between batches (Fan et al., 2016). The appropriate way to compensate for batch effect depends on the quantification method as well as the downstream analysis. For most studies batch effects can be eliminated by using down-sampling methods, however the complexity is reduced (Wang et al., 2012; Dey et al., 2015; Grün and van Oudenaarden, 2015). For studies that use traditional fragment counting, COMBAT (Johnson et al., 2007) is a batch effect eliminating method based on empirical Bayes frameworks and purports to be robust to outliers for small sample sizes. It was originally designed for microarray data but was used in scRNA-Seq experiments (Kim, K. T. et al., 2015). Although unsupervised batch effect detection or removal methods exist (Leek, 2014), the batches called by such methods often correlate highly with subpopulations detected by other scRNA-Seq methods (Finak et al., 2015). Since it is usually desirable to consider subpopulations for valuable biological insights, unsupervised batch effect removal methods should be used with discretion in single-cell experiments.

Besides batch-effect removal, it is also important to remove technical variability within the noise. The technical noise level of a genes correlates with its average expression level. Thus, a probabilistic model can be built to fit this correlation using technical spike-ins and further infer the biological variability of each gene (Brennecke et al., 2013). For most studies, it is also desirable to avoid the ubiquitous cell-cycle induced variation to mask other interesting biological variations. scLVM is a package that tries to introduce a cell-cycle factor removal step before subpopulations detection (Buettner et al., 2015). Recently, a new package called ccRemover was developed to remove the principal components that are identified as cell-cycle affected, which claimed to perform better than scLVM in several simulated and real datasets (Barron and Li, 2016).

Normalization

In scRNA-seq experiments, technical factors such as read depth, cell capture efficiency, 3′ bias or full sequence coverage due to particular library prep methods, might differ among different scRNA-Seq data sets. Thus, raw read counts should be normalized before downstream analyses. This procedure maximally ensures that the difference between the values in the matrix correctly reflects the abundance difference of transcripts or genes between the cells. When experiments are designed with ERCC spike-ins, ERCC can be used as internal controls and serve as anchors for normalization. GRM is a scRNA-seq normalization tool fitting a Gamma Regression Model between the reads (FPKM, RPKM, TPM) and spike-ins (Ding et al., 2015). The trained model is then used to estimate gene expression from the reads. BASICS, another recent workflow, provides a Bayesian model allowing to infer cell-specific normalization factor (Vallejos et al., 2015). This workflow estimates the technical variability using spike-ins. Finally, SAMstrt (Katayama et al., 2013) is an earlier algorithm that applies the resampling normalization procedure of the SAMseq algorithm to spike-ins, which was originally developed for bulk RNA-seq (Li and Tibshirani, 2013).

For experiments without spike-ins, if the quantification is count-based, one can normalize the expression profile by the scaling methods used in DESeq and edgeR etc. (Love et al., 2014). A new specific scRNA-seq procedure proposes a de-convolution approach on the pooled counts of gene expression for multiple cells, thus allows to infer the size factor for individual cells without using spike-ins (Aaron et al., 2016). The authors claimed that their approach improved the accuracy of the normalization compared with existing methods. However, experiments designed with UMIs as mentioned earlier quantify gene expression on an absolute basis and thus they do not need computational normalization.

Differential Expression

Differential expression (DE) analysis is the process of calling gene expression that show statistically significant difference between pre-specified groups of samples. Although DE is typically not the main objective of a single-cell experiment design, as it requires pre-defined grouping information among cells of interest, it is nevertheless common in scRNA-Seq experiments. Simple statistical methods such as t-test and Wilcoxon rank sum test are used in scRNA-Seq workflows such as SINCERA (Guo et al., 2015). Interestingly, EdgeR and DESeq2, two DE methods developed for bulk RNA-Seq, gave the best results for some scRNA-Seq data (Schurch et al., 2016).

The dropout event is a unique type of noise of scRNA-Seq that rarely occurs in bulk RNA-Seq experiments. It refers to the phenomenon that a gene is shown expressed abundantly in one cell but not detectable in another cell, as a consequence of the transcript loss in the reverse-transcription step. To account for frequent dropout events and biological variability within cell population, more sophisticated algorithms have been developed for scRNA-Seq data. Single-Cell Differential Expression (SCDE) is a package developed specifically for single-cell differential expression (Kharchenko et al., 2014). The model assumes that observed expression levels in scRNA-Seq data follow a mixture of negative binomial distribution for amplified genes, as proposed before (Anders and Huber, 2010); and a low-mean poisson distribution for dropout genes, as is observed in transcriptionally silenced genes. This model is then fit using Expectation Maximization (EM) algorithm (Kharchenko et al., 2014). It claimed higher sensitivity of differentially expressed genes compared to DESeq and CuffDiff. More recently, PAGODA improved upon SCDE's method in several aspects, including optimization of the computational process and a refined model for better fitting (Fan et al., 2016). MAST is another scRNA-Seq differential expression detection method that uses a two-part generalized linear model and adjusts for the fraction of cells that express a certain gene (Finak et al., 2015).

Another challenge unique to scRNA-Seq is that some genes may exhibit bimodality, meaning that the expression levels across a group of cells concentrate around two modes instead of one. A beta-Poisson distribution was proposed in order to provide a more accurate differential expression analysis that captures bimodality (Vu et al., 2016). Another tool Monocle (Trapnell et al., 2014) also has a module for differential expression, which fits the data with a non-parametric generalized additive model. Finally, the workflow of BASICS as described earlier, provides an criterion to detect high- or low-variable genes within the single cells dataset (Vallejos et al., 2015). However, it is not clear which methods have generally superior performance.

Subpopulation and Module Detection

General Machine-Learning Approaches

Different classical unsupervised approaches have been used to highlight single cell subgroups among a population. Principal Component Analysis (PCA) and its variants (e.g., Robust PCA and Kernel PCA) have been used in different single cell studies (Amir et al., 2013; Yan et al., 2013; Pollen et al., 2014; Trapnell et al., 2014; Treutlein et al., 2014; Satija et al., 2015; Fan et al., 2016; Ilicic et al., 2016). K-means and other distance based clustering algorithms such as hierarchical clustering or WARD are also widely used (Yan et al., 2013; Jaitin et al., 2014; Kharchenko et al., 2014; Lohr et al., 2014; Marco et al., 2014; Pollen et al., 2014; Shin et al., 2015). For example, Jaitin et al. combined hierarchical clustering and probabilistic mixture models to classify single cells from different tissues (Jaitin et al., 2014). A refined clustering method called pcaReduce (Zurauskiene and Yau, 2015) was designed for scRNA-Seq. It iteratively uses PCA combined with K-means to produce the hierarchical tree of the cells. For distance metrics employed by these methods, Euclidean distance, Pearson and Spearman correlation coefficients have been popular (though may not be optimal) choices (Pollen et al., 2014; Rotem et al., 2015).

Machine-Learning Approaches Tailored for scRNA-Seq Analysis

More sophisticated machine-learning algorithms have great potentials to overcome some issues of scRNA-Seq functional analysis. A main issue of scRNA-Seq analysis is that gene expression data cannot be expressed as a linear combination of the relationships between two cells in general (Buettner and Theis, 2012; Bendall et al., 2014; Levine et al., 2015). Also classical similarities (such as cosine or Euclidean distances) are less meaningful as the dimensionality increases (Beyer et al., 1999), and may not be appropriate for scRNA-Seq (Xu and Su, 2015). Possible irrelevant associations may arise with inappropriate metrics, while searching for the nearest neighbors on noisy data (Balasubramanian and Schwartz, 2002). Adequate analytical methods for scRNA-Seq data should also be able to highlight “rare events,” such as the small fraction of metastatic cancer cells amongst a large cell population (Bose et al., 2015; Shin et al., 2015). We describe the scRNA-Seq specific algorithms below in the order of dimension reduction, clustering, and other clustering variant methods. The datasets that were used to test these algorithms are listed in Table 2.

TABLE 2
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Table 2. Description of the main datasets for subpopulation and module detection analysis.

Among the dimension reduction methods, Zero-inflated factor analysis (ZIFA) algorithm is a new method that includes dropout events by representing the probability of gene dropout as an exponential function of its mean expression (Pierson and Yau, 2015). Using a latent variable model based on factor analysis, ZIFA reduces the dimension of scRNA-Seq dataset and allows the probability of each gene expression to be zero. Experiments in the original study suggest that ZIFA is a more robust alternative to PCA. As mentioned earlier, scLVM is another method for identifying cell subpopulations, which features removal of confounding factor like cell-cycle effects (Buettner et al., 2015). It first computes cell-to-cell covariance using a set of marker genes related to biological hidden factors of interest (such as the cell cycle). Another approach, PAGODA as mentioned before, uses a weighted PCA to characterize multiple aspects of heterogeneity in mouse neuronal progenitors (Fan et al., 2016). PAGODA evaluates over-dispersion of individual genes using error models.

SIMLR is a new clustering method designed to learn a distance metric that best fits the structure of the data. It infers a distance function as a linear combination of several distance metrics (Wang et al., 2016). It is designed to tackle the heterogeneity observed amongst single-cell datasets related to both technological difference across platforms as well as biological difference across studies. In another single-cell clustering approach named analysis of scRNA-seq based on transcript-compatibility counts (AscTC), read counts from scRNA-Seq dataset are transformed into probabilities using transcript-compatibility counts, rather than the conventional transcript abundance (Ntranos et al., 2016). Individual cells are clustered using an affinity propagation algorithm, a derivative of spectral clustering.

A few other hierarchical clustering approaches are worth mentioning. Geneteam is a multi-level recursive clustering method that searches for bipartitions of cells sharing exclusive expression profiles for a subset of genes (Harris et al., 2015). Similarly, Backspin is another hierarchical dividing clustering algorithm, allowing to cluster both genes and cells (Zeisel et al., 2015). It uses the SPIN algorithm (Tsafrir et al., 2005) at each iteration to sort the expression matrix and then separates genes (rows) and cells (columns) into two groups by a specific splitting criterion. Alternatively, BISCUIT is a new iterative normalization and clustering procedure based on Dirichlet Process, which was designed to correct technical variation in scRNA-seq together with cell clustering (Prabhakaran et al., 2016).

Graph Approaches beyond Clustering

Traditional clustering methods lack the function of inferring the inherent lineage between cells. Common approaches for cell lineage inferences require the creation of a graph or a tree, where single cells are represented as nodes and edges between the cells indicate their similarities. The lengths of the edges are computed from a similarity matrix based on a given metric. Before constructing the graph, a de-noising procedure is necessary. A useful de-noising procedure is to compute the k-Nearest-Neighbor graph (kNNG; Bendall et al., 2014; Levine et al., 2015; Xu and Su, 2015). Samples from the kNNG could then be compared using the geodesic distance, defined as the shortest path between two nodes (Bendall et al., 2014). Such an approach can remove “shortcuts” between irrelevant pairs of samples due to the curse of high dimensionality (Tenenbaum et al., 2000). Clustering analysis can then be performed on the graph using community detection algorithms (Fortunato, 2010). Xu and Su first used Euclidean distance to compute Shared Nearest-Neighbor (SNN) graph, then searched for quasi-cliques to obtain clusters of cells (Xu and Su, 2015). Quasi-cliques are communities of nodes, densely but not necessarily fully connected. Highly Connected Sub-graph (HPC) is another community detection algorithm that showed very similar performances as SNN (Hartuv and Shamir, 2000).

Microevolution of Single Cells

Inference without Spatial and Temporal Information

scRNA-Seq data are also informative to reveal single-cell microevolution. Different algorithms have been specifically designed for scRNA-Seq to infer a pseudo temporal ordering of single cells. Moncole is the first scRNA-Seq bioinformatics tool to infer the temporal ordering of single cells (Trapnell et al., 2014). It first uses Independent Component Analysis (ICA) to reduce the dimension, then computes a Minimum Spanning Tree (MST) on the graph constructed by Euclidean distance between cell pairs. MST connects all nodes of a graph using edges with a minimal total weighting, based on the hypothesis that the longest path through the MST corresponds to the longest series of transcriptionally similar cells. Another similar method, Waterfall, uses PCA coupled with k-means to produce clusters, then connects the cluster centroids with MST (Shin et al., 2015). Similar to Waterfall, TSCAN is a new approach based on MST. Cells are first clustered using a model-based approach before constructing an MST, allowing the reduction of the tree space complexity (Ji and Ji, 2016).

Embeddr is a method that uses the correlation metric between cells to construct kNNG, then projects the samples into a low-dimensional embedding using Laplacian eigen maps. The pseudo time order is then fitted using the principal curves (Campbell et al., 2015). Embeddr aims to tackle the drawbacks of Monocle, where gene expression is modeled as a linear combination and the result is highly sensitive to outliers. This scheme is also used in the workflow of SLICER, a recent algorithm using Locally Linear Embedding (LLE) to project the dataset and to construct a kNNG among cells (Welch et al., 2016).

Since visualization is key in understanding reconstructed single-cell trajectories, better visualization algorithms are as important as methods to reconstruct the single-cell microevolution. t-SNE is a popular method to visualize single cells, as part of a more complex workflow (Jiang, L. et al., 2016; Petropoulos et al., 2016). Another approach derived from diffusion map was developed, allowing one to visualize a clear bifurcation event among the cells which may be missed by independent component analysis (ICA) or t-SNE (Haghverdi et al., 2015; Moignard et al., 2015).

Modeling Microevolution with Spatial and Temporal Information

Cell subpopulations can also be characterized by different temporal and/or spatial gene expressions. Several approaches have been designed to exploit datasets with explicit temporal information. SCUBA is a method to detect bifurcation events using time course data (Marco et al., 2014). It assumes that the switch between cell states is a stochastic punctual process. To infer cellular hierarchy, it iteratively divides cells using k-means algorithm and uses a gap statistic to determine if a bifurcation event should occur. This process creates a binary tree, which can then be used to model gene expression dynamics (Marco et al., 2014). However, one drawback of SCUBA is that it requires data with temporal features. Free from such a requirement, Oscope is another method to infer oscillatory genes among single cells collected from a single tissue (Leng et al., 2015). It hypothesizes that these cells represent distinct states according to an oscillatory process. Oscope fits a two-dimensional sinusoidal function for each pair of genes, clusters gene pairs by frequency and reconstructs the order of the cells in a cyclic fashion. However, Oscope is unable to infer bifurcation events.

Other models also consider the spatial organization of cells in a tissue. Seurat is an approach that infers the spatial localization of single cells by integrating RNA-Seq with in situ RNA patterns (Satija et al., 2015). Seurat divides a cellular tissue into distinct spatial bins, linked by the expression of landmark genes per RNA in-situ hybridization. Within each bin, it builds a mixture model using expression values among correlated genes. The posterior probability is generated for each cell and assigned to a given bin. Another approach models the tissue as a 3D map and assumes that cells spatially close share common scRNA-Seq profiles (Pettit et al., 2014). This method uses a hidden markov random field to assign each bin of the map to a given cluster. Similar to Seurat, it takes the input of spatial gene expression measurement using whole mount in situ Hybridizations (WiSH) technology, a confocal microscopic approach that detects the presence of mRNA linked to a fluorescent probe.

Challenges and Future Work

Compared to bulk-cell analysis, single-cell genomics has the advantage of exploring cellular processes with a more accurate resolution, but it is more vulnerable to disturbances. Besides perfecting the experimental protocols to deal with issues such as dropouts in gene expression and biases in amplification, deriving new analytical methods to reveal the complexity in scRNA-Seq data is just as challenging. In this review, we have listed the different bioinformatics algorithms dedicated to single-cell analysis. Although the initial few steps of workflow for scRNA-Seq analysis are similar to bulk-cell analysis (data pre-processing, batch removal, alignment, quality check, and normalization), the subsequent analyses are largely unique for single cells, such as subpopulations detection, and microevolution characterization (Figure 1). With the increasing popularity of single-cell assays and ever increasing number of computational methods developed, these methods need to be more accessible to research groups without bioinformatics expertise. Moreover, datasets where cell classes have already been previously charaterized should be identified as benchmark data, in order to accurately assess the performance of new bioinformatics methods.

Although this review focuses on scRNA-Seq analyses, with the rapid development of technologies, coupled DNA-based genomics data can be obtained from the same cell, in parallel with scRNA-Seq data (Han et al., 2014; Dey et al., 2015; Kim, K. T. et al., 2015; Macaulay et al., 2015). This will further increase the analytical challenges. Previous multi-omics bioinformatics tools applied to bulk samples could be leveraged. The use of graphs and tensor approaches that integrate heterogeneous features in bulk samples may be good starting points for multi-dimensional single cell data (Li et al., 2009; Levine et al., 2015; Katrib et al., 2016; Zhu et al., 2016). Efforts should also be made toward developing computational methods to make use of spatial information (possibly guided by imaging) in combination of scRNA-Seq (Pettit et al., 2014; Satija et al., 2015). Also most emphasis in scRNA-Seq by far has been made on protein coding genes, and the dynamics and roles of non-coding RNAs such as lncRNAs (Travers et al., 2015; Ching et al., 2016) and micro-RNAs are poorly explored. Finally, a large number of single-cells (n = 4645) in a single data set was reported recently (Tirosh et al., 2016), and the scRNA-Seq data volume is expected to continue growing exponentially. Foreseeably, this poses a large spectrum of challenges from developing more efficient aligners to better data storage and data sharing solutions.

Author Contributions

LG envisioned this project, OP, XZ, TC, and LG wrote the manuscript, all authors have read and agreed on the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This research was supported by grants K01ES025434 awarded by NIEHS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov), P20 COBRE GM103457 awarded by NIH/NIGMS, 1R01LM012373 awarded by NLM, and Hawaii Community Foundation Medical Research Grant 14ADVC-64566 to LG.

References

Aaron, T. L. L., Bach, K., and Marioni, J. C. (2016). Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17:75. doi: 10.1186/s13059-016-0947-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Amir, E. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., and Bendall, S. C. (2013). viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552. doi: 10.1038/nbt.2594

PubMed Abstract | CrossRef Full Text | Google Scholar

Anders, S., and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol. 11:R106. doi: 10.1186/gb-2010-11-10-r106

PubMed Abstract | CrossRef Full Text | Google Scholar

Anders, S., Pyl, P. T., and Huber, W. (2014). HTSeq—a python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. doi: 10.1093/bioinformatics/btu638

PubMed Abstract | CrossRef Full Text | Google Scholar

Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Balasubramanian, M., and Schwartz, E. L. (2002). The isomap algorithm and topological stability. Science 295:7. doi: 10.1126/science.295.5552.7a

PubMed Abstract | CrossRef Full Text | Google Scholar

Barron, M., and Li, J. (2016). Identifying and removing the cell-cycle effect from single-cell rna-sequencing data. arXiv:1605.04492.

Google Scholar

Bendall, S. C., Davis, K. L., Amir, el-D, Tadmor, M. D., Simonds, E. F., Chen, T. J., et al. (2014). Single-cell trajectory detection uncovers progression and regulatory coordination in human b cell development. Cell 157, 714–725. doi: 10.1016/j.cell.2014.04.005

PubMed Abstract | CrossRef Full Text

Beyer, K., Goldstein, J., Ramakrishnan, R., and Shaft, U. (1999). “When Is ‘Nearest Neighbor’ Meaningful?,” in DATABASE Theory–ICDT'99 (Jerusalem: Springer), 217–235.

Google Scholar

Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30, 2114–2120. doi: 10.1093/bioinformatics/btu170

PubMed Abstract | CrossRef Full Text | Google Scholar

Bose, S., Wan, Z., Carr, A., Rizvi, A. H., Vieira, G., Pe'er, D., et al. (2015). Scalable microfluidics for single cell rna printing and sequencing. Genome Biol. 16:120. doi: 10.1186/s13059-015-0684-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Bray, N. L., Pimentel, H., Melsted, P., and Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527. doi: 10.1038/nbt.3519

PubMed Abstract | CrossRef Full Text | Google Scholar

Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., et al. (2013). Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095. doi: 10.1038/nmeth.2645

PubMed Abstract | CrossRef Full Text | Google Scholar

Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., et al. (2015). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 55–160. doi: 10.1038/nbt.3102

PubMed Abstract | CrossRef Full Text | Google Scholar

Buettner, F., and Theis, F. J. (2012). A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst. Bioinformatics 28, i626–i632. doi: 10.1093/bioinformatics/bts385

PubMed Abstract | CrossRef Full Text | Google Scholar

Campbell, K., Ponting, C. P., and Webber, C. (2015). Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell rna-seq profiles. bioRxiv 27219. doi: 10.1101/027219

CrossRef Full Text | Google Scholar

Chandramohan, R., Wu, P.-Y., Phan, J. H., and Wang, M. D. (2013). “Benchmarking RNA-Seq quantification tools,” in Engineering In Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE (Osaka), 647–650.

PubMed Abstract | Google Scholar

Ching, T., Peplowska, K., Huang, S., Zhu, X., Shen, Y., Molnar, J., et al. (2016). Pan-Cancer analyses reveal long intergenic non-coding rnas relevant to tumor diagnosis, subtyping and prognosis. EBioMedicine 7, 62–72. doi: 10.1016/j.ebiom.2016.03.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Cox, M. P., Peterson, D. A., and Biggs, P. J. (2010). SolexaQA: at-a-glance quality assessment of illumina second-generation sequencing data. BMC Bioinformatics 11:485. doi: 10.1186/1471-2105-11-485

PubMed Abstract | CrossRef Full Text | Google Scholar

der Maaten, L., and Hinton, G. (2008). Visualizing data using T-SNE. J. Mach. Learn. Res. 9, 2579–2605. Available online at: https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf

Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., and van Oudenaarden, A. (2015). Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289. doi: 10.1038/nbt.3129

PubMed Abstract | CrossRef Full Text | Google Scholar

Diaz, A., Liu, S. J., Sandoval, C., Pollen, A., Nowakowski, T. J., Lim, D. A., et al. (2016). SCell: integrated analysis of single-cell RNA-Seq data. Bioinformatics 32, 2219–2220. doi: 10.1093/bioinformatics/btw201

PubMed Abstract | CrossRef Full Text | Google Scholar

Ding, B., Zheng, L., Zhu, Y., Li, N., Jia, H., Ai, R., et al. (2015). Normalization and noise reduction for single cell RNA-Seq experiments. Bioinformatics 31, 2225–2227. doi: 10.1093/bioinformatics/btv122

PubMed Abstract | CrossRef Full Text | Google Scholar

Dobin, A., and Gingeras, T. R. (2015). Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinform. 51, 11.14.1–11.14.19. doi: 10.1002/0471250953.bi1114s51

PubMed Abstract | CrossRef Full Text | Google Scholar

Engström, P. G., Steijger, T., Sipos, B., Grant, G. R., Kahles, A., Rätsch, G., et al. (2013). Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191. doi: 10.1038/nmeth.2722

PubMed Abstract | CrossRef Full Text | Google Scholar

Fan, J.-B., Jean, J. J.-B., Salathia, N., Liu, R., Kaeser, G. E., Yung, Y. C., et al. (2016). Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244. doi: 10.1038/nmeth.3734

PubMed Abstract | CrossRef Full Text | Google Scholar

Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., et al. (2015). MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16:278. doi: 10.1186/s13059-015-0844-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Fonseca, N. A., Marioni, J., and Brazma, A. (2014). RNA-Seq gene profiling-a systematic empirical comparison. PloS ONE 9:e107026. doi: 10.1371/journal.pone.0107026

PubMed Abstract | CrossRef Full Text | Google Scholar

Fortunato, S. (2010). Community detection in graphs. Phys. Rep. 486, 75–174. doi: 10.1016/j.physrep.2009.11.002

CrossRef Full Text | Google Scholar

Freeman, B. T., Jung, J. P., and Ogle, B. M. (2016). Single-Cell RNA-seq reveals activation of unique gene groups as a consequence of stem cell-parenchymal cell fusion. Sci. Rep. 6:23270. doi: 10.1038/srep23270

PubMed Abstract | CrossRef Full Text | Google Scholar

Gao, Y., Wang, F., Eisinger, B. E., Kelnhofer, L. E., Jobe, E. M., and Zhao, X. (2016). Integrative single-cell transcriptomics reveals molecular networks defining neuronal maturation during postnatal neurogenesis. Cereb. Cortex. [Epub ahead of print]. doi: 10.1093/cercor/bhw040

PubMed Abstract | CrossRef Full Text | Google Scholar

Grün, D., and van Oudenaarden, A. (2015). Design and analysis of single-cell sequencing experiments. Cell 163, 799–810. doi: 10.1016/j.cell.2015.10.039

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, M., Wang, H., Potter, S. S., Whitsett, J. A., and Xu, Y. (2015). SINCERA: a Pipeline for Single-Cell RNA-Seq profiling analysis. PLoS Comput. Biol. 11:e1004575. doi: 10.1371/journal.pcbi.1004575

PubMed Abstract | CrossRef Full Text | Google Scholar

Haghverdi, L., Buettner, F., and Theis, F. J. (2015). Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998. doi: 10.1093/bioinformatics/btv325

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, L., Zi, X., Garmire, L. X., Wu, Y., Weissman, S. M., Pan, X., et al. (2014). Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci. Rep. 4:6485. doi: 10.1038/srep06485

PubMed Abstract | CrossRef Full Text | Google Scholar

Handel, A. E., Chintawar, S., Lalic, T., Whiteley, E., Vowles, J., Giustacchini, A., et al. (2016). Assessing similarity to primary tissue and cortical layer identity in induced pluripotent stem cell-derived cortical neurons through single-cell transcriptomics. Hum. Mol. Genet. 25, 989–1000. doi: 10.1093/hmg/ddv637

PubMed Abstract | CrossRef Full Text | Google Scholar

Harris, K., Magno, L., Katona, L., Lönnerberg, P., Muñoz Manchado, A. B., Somogyi, P., et al. (2015). Molecular organization of CA1 interneuron classes. bioRxiv 34595. doi: 10.1101/034595

CrossRef Full Text | Google Scholar

Hartuv, E., and Shamir, R. (2000). A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76, 175–181. doi: 10.1016/S0020-0190(00)00142-3

CrossRef Full Text | Google Scholar

Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., et al. (2016). Single-Cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319. doi: 10.1038/cr.2016.23

PubMed Abstract | CrossRef Full Text | Google Scholar

Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., et al. (2016). Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17:29. doi: 10.1186/s13059-016-0888-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., et al. (2014). Quantitative single-Cell RNA-Seq with unique molecular identifiers. Nat. Methods 11, 163–166. doi: 10.1038/nmeth.2772

PubMed Abstract | CrossRef Full Text | Google Scholar

Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., et al. (2014). Massively parallel Single-Cell RNA-Seq for marker-free decomposition of tissues into cell types. Science 343, 776–779. doi: 10.1126/science.1247651

PubMed Abstract | CrossRef Full Text | Google Scholar

Ji, Z., and Ji, H. (2016). TSCAN: pseudo-time reconstruction and evaluation in Single-Cell RNA-Seq analysis. Nucl. Acids Res. 44:e117. doi: 10.1093/nar/gkw430

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiang, L., Chen, H., Pinello, L., and Yuan, G.-C. (2016). GiniClust: detecting rare cell types from single-cell gene expression data with gini index. Genome Biol. 17:144. doi: 10.1186/s13059-016-1010-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiang, P., Thomson, J. A, and Stewart, R. (2016). Quality control of Single-Cell RNA-seq by SinQC. Bioinformatics. [Epub ahead of print]. doi: 10.1093/bioinformatics/btw176

PubMed Abstract | CrossRef Full Text | Google Scholar

Johnson, W. E., Li, C., and Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8, 118–127. doi: 10.1093/biostatistics/kxj037

PubMed Abstract | CrossRef Full Text | Google Scholar

Katayama, S., Töhönen, V., Linnarsson, S., and Kere, J. (2013). SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization. Bioinformatics 29, 2943–2945. doi: 10.1093/bioinformatics/btt511

PubMed Abstract | CrossRef Full Text | Google Scholar

Katrib, A., Hsu, W., Bui, A., and Xing, Y. (2016). Radiotranscriptomics: a synergy of imaging and transcriptomics in clinical assessment. Quant. Biol. 4, 1–12. doi: 10.1007/s40484-016-0061-6

CrossRef Full Text | Google Scholar

Kharchenko, P. V., Silberstein, L., and Scadden, D. T. (2014). Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742. doi: 10.1038/nmeth.2967

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, D., Langmead, B., and Salzberg, S. L. (2015). HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360. doi: 10.1038/nmeth.3317

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., Salzberg, S. L., et al. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14:R36. doi: 10.1186/gb-2013-14-4-r36

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, K. T., Lee, H. W., Lee, H. O., Kim, S. C., Seo, Y. J., Chung, W., et al. (2015). Single-Cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 16:127. doi: 10.1186/s13059-015-0692-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Kimmerling, R. J., Szeto, G. L., Li, J. W., Genshaft, A. S., Kazer, S. W., Payer, K. R., et al. (2016). A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun. 7:10220. doi: 10.1038/ncomms10220

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumar, R. M., Cahan, P., Shalek, A. K., Satija, R., DaleyKeyser, A. J., Li, H., et al. (2014). Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56–61. doi: 10.1038/nature13920

PubMed Abstract | CrossRef Full Text | Google Scholar

Kvastad, L., Solnestam, B. W., Johansson, E., Nygren, A. O., Laddach, N., Sahlén, P., et al. (2015). Single cell analysis of cancer cells using an improved RT-MLPA method has potential for cancer diagnosis and monitoring. Sci. Rep. 5:16519. doi: 10.1038/srep16519

PubMed Abstract | CrossRef Full Text | Google Scholar

Leek, J. T. (2014). Svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42. doi: 10.1093/nar/gku864

PubMed Abstract | CrossRef Full Text | Google Scholar

Leng, N., Choi, J., Chu, L. F., Thomson, J. A., Kendziorski, C., and Stewart, R. (2016). OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data. Bioinformatics 32, 1408–1410. doi: 10.1093/bioinformatics/btw004

PubMed Abstract | CrossRef Full Text | Google Scholar

Leng, N., Chu, L. F., Barry, C., Li, Y., Choi, J., Li, X., et al. (2015). Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments. Nat. Methods 12, 947–950. doi: 10.1038/nmeth.3549

PubMed Abstract | CrossRef Full Text | Google Scholar

Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir el, A. D., Tadmor, M. D., et al. (2015). Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197. doi: 10.1016/j.cell.2015.05.047

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, B., and Dewey, C. N. (2011). RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12:323. doi: 10.1186/1471-2105-12-323

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al. (2009). The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079. doi: 10.1093/bioinformatics/btp352

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, J., and Tibshirani, R. (2013). Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-seq data. Stat. Methods Med. Res. 22, 519–536. doi: 10.1177/0962280211428386

PubMed Abstract | CrossRef Full Text | Google Scholar

Liao, Y., Smyth, G. K., and Shi, W. (2013). featurecounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. doi: 10.1093/bioinformatics/btt656

PubMed Abstract | CrossRef Full Text | Google Scholar

Lohr, J. G., Adalsteinsson, V. A., Cibulskis, K., Choudhury, A. D., Rosenberg, M., Cruz-Gordillo, P., et al. (2014). Whole exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer. Nat. Biotechnol. 32:479. doi: 10.1038/nbt.2892

PubMed Abstract | CrossRef Full Text | Google Scholar

Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21. doi: 10.1101/002832

PubMed Abstract | CrossRef Full Text | Google Scholar

Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., et al. (2015). G&T-Seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522. doi: 10.1038/nmeth.3370

PubMed Abstract | CrossRef Full Text | Google Scholar

Marco, M., Karp, R. L., Guo, G., Robson, P., Hart, A. H., Trippa, L., et al. (2014). Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl. Acad. Sci. 111, E5643–E5650. doi: 10.1073/pnas.1408993111

PubMed Abstract | CrossRef Full Text | Google Scholar

Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17:10. doi: 10.14806/ej.17.1.200

CrossRef Full Text | Google Scholar

Meyer, S. E., Qin, T., Muench, D. E., Masuda, K., Venkatasubramanian, M., Orr, E., et al. (2016). Dnmt3a haploinsufficiency transforms Flt3-ITD myeloproliferative disease into a rapid, spontaneous, and fully-penetrant acute myeloid leukemia. Cancer Discov. 6, 501–515. doi: 10.1158/2159-8290.CD-16-0008

PubMed Abstract | CrossRef Full Text | Google Scholar

Miyamoto, D. T., Zheng, Y., Wittner, B. S., Lee, R. J., Zhu, H., Broderick, K. T., et al. (2015). RNA-seq of single prostate CTCs implicates noncanonical wnt signaling in antiandrogen resistance. Science 349, 1351–1356. doi: 10.1126/science.aab0917

PubMed Abstract | CrossRef Full Text | Google Scholar

Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., et al. (2015). Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276. doi: 10.1038/nbt.3154

PubMed Abstract | CrossRef Full Text | Google Scholar

Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., et al. (2011). Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94. doi: 10.1038/nature09807

PubMed Abstract | CrossRef Full Text | Google Scholar

Ntranos, V., Kamath, G. M., Zhang, J. M., Pachter, L., and Tse, D. N. (2016). Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts. bioRxiv. 17:112. doi: 10.1186/s13059-016-0970-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., et al. (2014). Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401. doi: 10.1126/science.1254257

PubMed Abstract | CrossRef Full Text | Google Scholar

Petropoulos, S., Edsgärd, D., Reinius, B., Deng, Q., Panula, S. P, Codeluppi, S., et al. (2016). Single-cell RNA-seq reveals lineage and x chromosome dynamics in human preimplantation embryos. Cell 165, 1012–1026. doi: 10.1016/j.cell.2016.03.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Pettit, J.-B., Tomer, R, Achim, K, Richardson, S, Azizi, L., and Marioni, J. (2014). Identifying cell types from spatially referenced single-cell expression datasets. PLoS Comput Biol 10:e1003824. doi: 10.1371/journal.pcbi.1003824

PubMed Abstract | CrossRef Full Text | Google Scholar

Pierson, E., and Yau, C. (2015). ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 16, 1–10. doi: 10.1186/s13059-015-0805-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A, Lui, J. H., et al. (2014). Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058. doi: 10.1038/nbt.2967

PubMed Abstract | CrossRef Full Text | Google Scholar

Prabhakaran, S., Azizi, E., and Pe'er, D. (2016). “Dirichlet process mixture model for correcting technical variation in single-cell gene expression data.” in Proceedings of The 33rd International Conference on Machine Learning (New York, NY), 1070–1079.

Google Scholar

Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., et al. (2012). Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782. doi: 10.1038/nbt.2282

PubMed Abstract | CrossRef Full Text | Google Scholar

Robinson, M. D., McCarthy, D. J, and Smyth, G. K. (2010). edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. doi: 10.1093/bioinformatics/btp616

PubMed Abstract | CrossRef Full Text | Google Scholar

Rotem, A., Ram, O., Shoresh, N., Sperling, R. A., Goren, A., Weitz, D. A., et al. (2015). Single-Cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172. doi: 10.1038/nbt.3383

PubMed Abstract | CrossRef Full Text | Google Scholar

Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., and Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502. doi: 10.1038/nbt.3192

PubMed Abstract | CrossRef Full Text | Google Scholar

Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., et al. (2016). How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22, 839–851. doi: 10.1261/rna.053959.115

PubMed Abstract | CrossRef Full Text | Google Scholar

Shekhar, K., Brodin, P., Davis, M. M., and Chakraborty, A. K. (2014). Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proc. Natl. Acad. Sci.U.S.A. 111, 202–207. doi: 10.1073/pnas.1321405111

PubMed Abstract | CrossRef Full Text | Google Scholar

Shin, J., Berg, D. A., Zhu, Y., Shin, J. Y., Song, J., Bonaguidi, M. A., et al. (2015). Single-Cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372. doi: 10.1016/j.stem.2015.07.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Tang, F., Barbacioru, C., Bao, S., Lee, C., Nordman, E., Wang, X., et al. (2010). Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-seq analysis. Cell Stem Cell 6, 468–478. doi: 10.1016/j.stem.2010.03.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Tenenbaum, J. B., De Silva, V., and Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323. doi: 10.1126/science.290.5500.2319

PubMed Abstract | CrossRef Full Text | Google Scholar

Ting, D. T., Wittner, B. S., Ligorio, M., Jordan, N. V, Shah, A. M., Miyamoto, D. T., et al. (2014). Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep. 8, 1905–1918. doi: 10.1016/j.celrep.2014.08.029

PubMed Abstract | CrossRef Full Text | Google Scholar

Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., et al. (2016). Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196. doi: 10.1126/science.aad0501

PubMed Abstract | CrossRef Full Text | Google Scholar

Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., et al. (2014). Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions. Nat. Biotechnol. 32, 381. doi: 10.1038/nbt.2859

PubMed Abstract | CrossRef Full Text | Google Scholar

Trapnell, C., Pachter, L., and Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111. doi: 10.1093/bioinformatics/btp120

PubMed Abstract | CrossRef Full Text | Google Scholar

Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., Van Baren, M. J., et al. (2010). Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515. doi: 10.1038/nbt.1621

PubMed Abstract | CrossRef Full Text | Google Scholar

Travers, C., Masaki, J., Weirather, J., Garmire, L. X., Ching, T., Masaki, J., et al. (2015). Non-coding yet non-trivial: a review on the computational genomics of lincRNAs. BioData Min. 8:44. doi: 10.1186/s13040-015-0075-z

CrossRef Full Text

Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., et al. (2014). Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375. doi: 10.1038/nature13173

PubMed Abstract | CrossRef Full Text | Google Scholar

Tsafrir, D., Tsafrir, I., Ein-Dor, L., Zuk, O., Notterman, D. A., and Domany, E. (2005). Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices. Bioinformatics 21, 2301–2308. doi: 10.1093/bioinformatics/bti329

PubMed Abstract | CrossRef Full Text | Google Scholar

Vallejos, C. A., Marioni, J. C., and Richardson, S. (2015). BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput. Biol. 11:e1004333. doi: 10.1371/journal.pcbi.1004333

PubMed Abstract | CrossRef Full Text | Google Scholar

Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M., et al. (2016). Beta-poisson model for single-cell RNA-seq data analyses. Bioinformatics 32, 2128–2135. doi: 10.1093/bioinformatics/btw202

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, B., Zhu, J., Pierson, E., and Batzoglou, S. (2016). Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. bioRxiv. 52225. doi: 10.1101/052225

CrossRef Full Text | Google Scholar

Wang, J.-Y., Bensmail, H., and Gao, X. (2012). Multiple graph regularized protein domain ranking. BMC Bioinformatics 13:307. doi: 10.1186/1471-2105-13-307

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, K., Singh, D., Zeng, Z., Coleman, S. J., Huang, Y., Savich, G. L., et al. (2010). MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38:e178. doi: 10.1093/nar/gkq622

PubMed Abstract | CrossRef Full Text | Google Scholar

Welch, J. D., Hartemink, A. J., and Prins, J. F. (2016). SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 17:106. doi: 10.1186/s13059-016-0975-3

CrossRef Full Text

Wu, T. D., Reeder, J., Lawrence, M., Becker, G., and Brauer, M. J. (2016). GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy, and functionality. Stat. Genomics Methods Protoc. 1418, 283–334. doi: 10.1007/978-1-4939-3578-9_15

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, C., and Su, Z. (2015). Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980. doi: 10.1093/bioinformatics/btv088

PubMed Abstract | CrossRef Full Text | Google Scholar

Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., et al. (2013). Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1131–1139. doi: 10.1038/nsmb.2660

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, X., Liu, D., Liu, F., Wu, J., Zou, J., Xiao, X., et al. (2013). HTQC: a fast quality control toolkit for illumina sequencing data. BMC Bioinformatics 14:33. doi: 10.1186/1471-2105-14-33

CrossRef Full Text

Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., Manno, G. L., Juréus, A., et al. (2015). Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142. doi: 10.1126/science.aaa1934

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, T., Luo, Y., Liu, K., Pan, L., Zhang, B., Yu, J., et al. (2011). BIGpre: a quality assessment package for next-generation sequencing data. Genomics, Proteomics Bioinformatics 9, 238–244. doi: 10.1016/S1672-0229(11)60027-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhu, Z., Chen, Z., Zhang, K., Wang, M., Medovoy, D., Whitaker, J. W., et al. (2016). Constructing 3D interaction maps from 1D epigenomes. Nat. Commun. 7:10812. doi: 10.1038/ncomms10812

PubMed Abstract | CrossRef Full Text | Google Scholar

Zurauskiene, J., and Yau, C. (2015). pcaReduce: hierarchical clustering of single cell transcriptional profiles. bioRxiv.. 26385. doi: 10.1186/s12859-016-0984-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: single-cell genomics, single-cell analysis, bioinformatics, heterogeneity, microevolution

Citation: Poirion OB, Zhu X, Ching T and Garmire L (2016) Single-Cell Transcriptomics Bioinformatics and Computational Challenges. Front. Genet. 7:163. doi: 10.3389/fgene.2016.00163

Received: 01 May 2016; Accepted: 02 September 2016;
Published: 21 September 2016.

Edited by:

H. Steven Wiley, Pacific Northwest National Laboratory, USA

Reviewed by:

Seth G. N. Grant, University of Edinburgh, UK
Milind Ratnaparkhe, Indian Institute of Soybean Research (ICAR), India

Copyright © 2016 Poirion, Zhu, Ching and Garmire. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lana Garmire, lgarmire@cc.hawaii.edu

These authors have contributed equally to this work.

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.