 METHOD
 Open Access
 Published:
A statistical approach for identifying differential distributions in singlecell RNAseq experiments
Genome Biology volume 17, Article number: 222 (2016)
Abstract
The ability to quantify cellular heterogeneity is a major advantage of singlecell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.
Background
Coordinated gene expression is fundamental to an organism’s development and maintenance, and aberrations are common in disease. Consequently, experiments to measure expression on a genomewide scale are pervasive. The most common experiment involves the quantification of mRNA transcript abundance averaged over a population of thousands or millions of cells. These socalled traditional, or bulk, RNAseq experiments have proven useful in a multitude of studies. However, because bulk RNAseq does not provide a measure of cellspecific expression, many important signals go unobserved. A gene that appears to be expressed at a relatively constant level in a bulk RNAseq experiment, for example, may actually be expressed in subgroups of cells at levels that vary substantially (see Fig. 1).
Singlecell RNAseq (scRNAseq) facilitates the measurement of genomewide mRNA abundance in individual cells, and as a result, provides the opportunity to study the extent of genespecific expression heterogeneity within a biological condition, and the impact of changes across conditions. Doing so is required for discovering novel cell types [1, 2], for elucidating how gene expression changes contribute to development [3–5], for understanding the role of cell heterogeneity on the immune response [6, 7] and cancer progression [6, 8–10], and for predicting the response to chemotherapeutic agents [11–13]. Unfortunately, the statistical methods available for characterizing genespecific expression within a condition and for identifying differences across conditions in scRNAseq are greatly limited, largely because they do not fully accommodate the cellular heterogeneity that is prevalent in singlecell data.
To identify genes with expression that varies across biological conditions in an scRNAseq experiment, a number of early studies used methods from bulk RNAseq [4, 10, 12, 14, 15]. In general, the methods assume that each gene has a latent level of expression within a biological condition, and that measurements fluctuate around that level due to biological and technical sources of variability. In other words, they assume that genespecific expression is well characterized by a unimodal distribution within a condition. Further, tests for differences in expression to identify socalled differentially expressed (DE) genes amount to tests for shifts in the unimodal distributions across conditions. A major drawback of these approaches in the singlecell setting is that, due to both biological and technical celltocell variability, there is often an abundance of cells for which a given gene’s expression is unobserved [7, 16, 17] and, consequently, unimodal distributions are insufficient.
To address this, a number of statistical methods have been developed recently to accommodate bimodality in scRNAseq data [17, 18]. In these mixturemodel based approaches, one component distribution accommodates unobserved, or dropout, measurements (which include zero and, optionally, thresholded lowmagnitude observations) and a second unimodal component describes gene expression in cells where expression is observed. Although these approaches provide an advance over unimodal models used in bulk, they are insufficient for characterizing multimodal expression data, which is common in scRNAseq experiments (see Fig. 2).
Specifically, a number of studies have shown that many types of heterogeneity can give rise to multiple expression modes within a given gene [19–23]. For example, there are often multiple states among expressed genes [19, 20, 22] (a schematic is shown in Fig. 1). The transition between cell states may be primarily stochastic in nature and result from expression bursts [24, 25], or result from positive feedback signals [19, 23, 26]. Beyond the existence of multiple stable states, multiple modes in the distribution of expression levels in a population of cells may also arise when the gene is either oscillatory and unsynchronized, or oscillatory with cellular heterogeneity in frequency, phase, and amplitude [21, 23].
Figure 3 illustrates common multimodal distributions within and across biological conditions. When the overall mean expression level for a given gene is shifted across conditions, then bulk methods, or recent methods for scRNAseq [17, 18, 27, 28], may be able to identify the gene as showing some change. However, as we show here, they would be relatively underpowered to do so, and they would be unable to characterize the change, which is often of interest in an scRNAseq experiment. For example, the gene in Fig. 3 c shows a differential number of modes (DM), while the gene in Fig. 3 b shows a differential proportion (DP) of cells at each expression level across conditions. Differentiating between DM and DP is important since the former suggests the presence of a distinct cell type in one condition, but not the other, while the latter suggests a change in splicing patterns among individual cells [7] or cellspecific responses to signaling [29].
Here we develop a Bayesian modeling framework, scDD, to facilitate the characterization of expression within a biological condition, and to identify genes with differential distributions (DDs) across conditions in an scRNAseq experiment. A DD gene may be classified as DE, DM, DP, or both DM and differential means of expression states (abbreviated DB). Figure 3 provides an overview of each pattern. Simulation studies suggest that the approach provides improved power and precision for identifying differentially distributed genes. Additional advantages are demonstrated in a case study of human embryonic stem cells (hESCs).
Results and discussion
Human embryonic stem cell data
scRNAseq data were generated in the James Thomson Lab at the Morgridge Institute for Research (see “Methods” and [30] for details). Here we analyze data from two undifferentiated hESC lines: the male H1 line (78 cells) and the female H9 line (87 cells). In addition, we include data from two differentiated cell types that are both derived from H1: definitive endoderm cells (DECs, 64 cells) and neuronal progenitor cells (NPCs, 86 cells). The relationship between these four cell types is summarized by the diagram in Fig. 4. As discussed in the case study results, it is of interest to characterize the differences in distributions of gene expression among these four cell types to gain insight into the genes that regulate the differentiation process.
Publicly available human myoblast and mouse embryonic stem cell data
We also apply our method to two publicly available scRNAseq datasets to determine which genes are differentially distributed following stimulation or inhibition of differentiation via a specialized growth medium. Using data from [31], we compare gene expression of human myoblast cells cultured in standard growth medium (T0, 96 cells) with those treated with differentiationinducing medium for 72 hours (T72, 84 cells). Additionally, we use data from [32] to compare the gene expression of mouse embryonic stem cells (mESCs) cultured in standard medium (Serum + LIF, 93 cells) with those cultured on differentiationinhibiting medium (2i + LIF, 94 cells).
Simulated data
We evaluate model performance using logtransformed count data simulated from mixtures of negative binomial distributions. The analysis of logtransformed counts from bulk RNAseq has been shown to perform as well as utilizing countbased modeling assumptions [33, 34]. Recent scRNAseq analyses have also assumed the normality of logtransformed nonzero measurements [7, 18]. For each simulated dataset, 10,000 genes were simulated for two conditions with four different sample size settings (50, 75, 100, and 500 cells in each condition). The majority of the genes (8000) were simulated out of the same model in each condition, and the other 2000 represent genes with the four types of DD outlined in Fig. 3. The 2000 DD genes were split equally into the following four categories:

DE: single component with a different mean in each condition

DP: two components in each condition with equal component means across conditions; the proportion in the low mode is 0.33 for condition 1 and 0.66 for condition 2

DM: single component in condition 1; two components in condition 2 with one overlapping component. Half of the condition 2 cells belong to each mode

DB: single component in condition 1; two components in condition 2 with no overlapping components. The mean of condition 1 is halfway between the means in condition 2. Half of the cells in condition 2 belong to each mode
Here a component represents the distribution of expression values at a particular expression level (or mode), and different biological groups of interest are referred to as conditions. Of the 8000 null genes, 4000 were generated from a single negative binomial component (EE, or equivalent expression) and the other 4000 from a twocomponent negative binomial mixture (EP, or equivalent proportions of cells belonging to each component). The parameters of the negative binomial distributions for the unimodal genes were chosen to be representative of the observed means and variances in the H1 dataset. Foldchanges for DE genes were chosen to be representative of those observed in the H1 and DEC comparison. Distances between (logscale) component means Δ _{ μ } σ (referred to as component mean distance) in the multimodal genes were varied, with an equal proportion of genes at each setting of Δ _{ μ }∈{2,3,4,5,6}, where σ is the withincomponent standard deviation on the logscale (simulated to be common across components for a given gene and condition). More details are provided in “Methods”.
The scDD modeling framework
Let Y _{ g }=(y _{ g1},…,y _{ g J }) be the logtransformed nonzero expression measurements of gene g in a collection of J cells from two biological conditions. We assume that measurements have been normalized to adjust for technical sources of variation including amplification bias and sequencing depth. Under the null hypothesis of equivalent distributions (i.e., no dependence on condition), we let Y _{ g } be modeled by a conjugate Dirichlet process mixture (DPM) of normals (see “Methods” for more details). Gene g may also have expression measurements of zero in some cells; these are modeled as a separate distributional component (see “Differential proportion of zeroes” for more details).
Ultimately, we would like to calculate a Bayes factor for the evidence that the data arises from two independent conditionspecific models (DDs) versus one overall model that ignores condition (equivalent distributions or EDs). Let \(\mathcal {M}_{\text {DD}}\) denote the DD hypothesis, and \(\mathcal {M}_{\text {ED}}\) denote the equivalent distribution hypothesis. A Bayes factor in this context for gene g would be:
where \(f(Y_{g}  \mathcal {M})\) denotes the predictive distribution of the observations from gene g under the given hypothesis. In general, there is no analytical solution for this distribution under the DPM model framework. However, under the product partition model (PPM) formulation (see “Methods” for more details), we can get a closed form solution for \(f(Y_{g}, Z_{g}  \mathcal {M})\), where Z _{ g } represents a partition (or clustering) of samples to mixture components. As the partition Z _{ g } cannot be integrated out, we introduce an approximate Bayes factor score:
where C1 and C2 denote conditions 1 and 2, respectively, and the score is evaluated at the partition estimate \(\hat {Z_{g}}\). A high value of this score presents evidence that a given gene is differentially distributed. The significance of the score is assessed via a permutation test. Specifically, condition labels are permuted and partition estimates are obtained within the new conditions. For each permuted dataset, the Bayes factor score is calculated; the default in scDD is 1000 permutations. For each gene, an empirical p value is calculated, and the false discovery rate (FDR) is controlled for a given target value using the method of [35].
If covariates are available, instead of permuting the observed values, the relationship between the clustering and covariates can be preserved by permuting the residuals of a linear model that includes the covariate and using the fitted values [36]. As pointed out by [18], the cellular detection rate is a potential confounder variable, so the permutation procedure in the case studies is adjusted in this manner. If other known confounders exist and are measured, these can also be incorporated in the same manner. Note that while this procedure adjusts for covariates that affect mean expression levels, it does not adjust for covariatespecific effects on variance. The sensitivity of the approach to various levels of nonlinear confounding effects is evaluated in a simulation study presented in Additional file 1: Section 2.3.
Classification of significant DD genes
For genes that are identified as DD by the Bayes factor score, of interest is classifying them into four categories that represent the distinct DD patterns shown in Fig. 3. To classify the DD genes into these patterns (DE, DM, DP, and DB), scDD utilizes the conditional posterior distribution of the componentspecific mean parameters given in Eq. 6 (see “Methods”). Posterior sampling is carried out to investigate the overlap of components across conditions. Let c _{1} be the number of components in condition 1, c _{2} the number of components in condition 2, and c _{OA} the number of components overall (when pooling conditions 1 and 2). Only components containing at least three cells are considered to minimize the impact of outlier cells. Note that for interpretability, a DD gene must satisfy: c _{1}+c _{2}≥c _{OA}≥ min(c _{1},c _{2}). These bounds on the overall number of components represent the two extreme cases: condition 1 does not overlap with condition 2 at all, versus one condition completely overlaps with the other. Any cases outside of these boundaries are not readily interpretable in this context. The actions to take for all other possible combinations of c _{1}, c _{2}, and c _{OA} are detailed in “Methods”.
Differential proportion of zeroes
For those genes that do not show DDs in the nonzero values, scDD allows a user to evaluate whether the proportion of zeroes differs significantly between the two conditions. This evaluation is carried out using logistic regression adjusted for the proportion of genes detected in each cell as in [18]. Genes with a χ ^{2} test p value of less than 0.025 (after adjustment for multiple comparisons using the method of [35]) are considered to have a differential proportion of zeroes (DZ).
Simulation study
A simulation study was conducted to assess the performance of scDD in identifying DD genes, and to classify them as DE, DP, DM, or DB. Model performance on the simulated data was assessed based on (1) the ability to estimate the correct number of components, (2) the ability to detect significantly DD genes, and (3) the ability to classify DD genes into their correct categories. These three criteria are explored in the next three sections, respectively. Existing methods for DE analysis are also evaluated for the second criterion.
Estimation of the number of components
We first examine the ability of scDD to detect the correct number of components. Table 1 displays the proportion of bimodal and unimodal simulated genes where the correct number of components was identified. For bimodal genes, results are stratified by component mean distance. It is clear that the ability of the algorithm to identify the correct number of components in bimodal genes improves as the component mean distance or sample size increases. The results for unimodal genes are not as sensitive to sample size; however, the proportion of genes identified as bimodal increases slightly with more samples. We conclude that the partition estimate is able to detect reliably the true number of components for reasonable sample and effect sizes.
Detection of DD genes
Next, we examine the ability of scDD to identify the nonnull genes as significantly DD, and compare it to existing methods, SCDE [17] and MAST [18]. For each method, the target FDR was set at 5 % (see “Methods” for details). The power to detect each gene pattern as DD for all three methods is shown in Table 2. Note that the calculations here are taken before the classification step for scDD, so power is defined as the proportion of genes from each simulated category that are detected as DD. In general, the power to detect DD genes improves with increased sample size for all three methods. Our approach has comparable power to SCDE and MAST for DE and DP genes, but higher overall power to detect DM and DB genes. Interestingly, SCDE has very low power to detect DP genes, whereas MAST shows very low power to detect DB genes. We note that SCDE and MAST do not aim to detect genes with no change in the overall mean level in expressed cells (as in the case of DB genes), so it is expected that scDD will outperform other methods at detecting genes in this category.
Classification of DD genes
Next, we examine the ability of scDD to classify each DD gene into its corresponding category. Table 3 shows the correct classification rate in each category for DD genes that were correctly identified during the detection step (calculated as the proportion of true positive genes detected as DD for a given category that were classified into the correct category). The classification rates do not depend strongly on sample size, with the exception of DP, which decreases with increasing sample size. This decrease results from an increase in the DD detection rate of DP genes with small component mean distance, which have a lower correct classification rate (as shown below).
Since the ability toclassify a DD gene correctly depends on the ability to detect the correct number of components (see classification algorithm in “Methods”), we also examine how the correct classification rate varies with component mean distance for the categories that contain bimodal genes (DP, DM, and DB). As shown in Table 4, the classification rates improve as Δ _{ μ } increases. This pattern mirrors the trend in Table 1, and suggests that misclassification events occur largely due to incorrect estimation of the number of components. Performance generally increases with sample size, especially at lower values of Δ _{ μ }. In general, the ability of the algorithm to classify detected DD genes into their true category is robust when components are well separated and improves with increasing sample size.
Case study: identifying DD genes between hESC types
The comprehensive characterization of transcriptional dynamics across hESC lines and derived cell types aims to provide insight into the gene regulatory processes governing pluripotency and differentiation [37–39]. Previous work utilizing microarrays and bulk RNAseq largely focused on identifying genes with changes in average expression level across a population of cells. By examining transcriptional changes at the singlecell level, we can uncover global changes that are undetectable when averaging over the population. In addition, we gain the ability to assess the level of heterogeneity of key differentiation regulators, which may lead to the ability to assess variation in pluripotency [40] or the differentiation potential of individual cells.
The number of significant DD genes for each cell type comparison is shown in Table 5 for scDD, SCDE, and MAST. Note that the comparison of H1 and H9 detects the fewest number of DD genes for all three methods, a finding that is consistent with that both of these are undifferentiated hESC lines and it is expected that they are the most similar among the comparisons. In all four comparisons, the number of genes identified by our method is greater than that for SCDE and similar to that for MAST.
Figure 5 a displays topranked genes for each category that are not identified by MAST or SCDE for the H1 versus DEC comparison. Among the genes identified exclusively by scDD for the H1 versus DEC comparison are CHEK2, a cellcycle checkpoint kinase [41], and CDK7, a cyclindependent kinase that plays a key role in cellcycle regulation through the activation of other cyclindependent kinases [42]. It has been shown that embryonic stem cells express cyclin genes constitutively, whereas in differentiated cells, cyclin levels are oscillatory [43]. This finding is consistent with the differential modality of the CDK7 gene shown in Fig. 5 b. Similarly, scDD identifies several genes involved in the regulation of pluripotency that are not identified by the other two methods (Fig. 5 c). For example, FOXP1 exhibits alternative splicing activity in hESCs, stimulating expression of several key regulators of pluripotency [44]. The PSMD12 gene encodes a subunit of the proteasome complex that is vital to the maintenance of pluripotency and has shown decreased expression in differentiating hESCs [45]. Both of these genes are also differentially distributed between H1 and the other differentiated cell type, NPC.
In general, the vast majority of the genes found exclusively by scDD are categorized as something other than DE (ranging from 98.3 to 100 % in the three case studies, see Additional file 1: Table S6), which suggests that they are predominantly characterized by differences that are more complex than the traditional DE pattern. The genes identified by MAST but not scDD are overwhelmingly characterized as those with a weak signal in both the nonzero and zero components (see Additional file 1: Figure S9), which can be difficult to interpret (see Additional file 1: Section 3 for more details).
Additional case studies
We also applied scDD and MAST to two additional case studies (the numbers of significant DD genes for each comparison are displayed in Table 6). SCDE was not used to analyze these datasets since it is intended for use on raw count data and the processed data made available by the authors of [31, 32] were already normalized by FPKM and TPM, respectively. Like the results of the hESC case study, MAST and scDD identify similar numbers of significant genes. The genes that scDD finds exclusively are predominantly characterized by something other than a mean shift, a result which is also consistent with the hESC case study (see Additional file 1: Table S7).
Advantages and limitations of the approach
We stress that our approach is inherently different from a method that detects traditional DE, such as [17] and [18], which aim to detect a shift in the mean of the expressed values. In addition to identifying genes that have DDs across conditions, our modeling framework allows us to identify subpopulations within each condition that have differing levels of expression of a given gene (i.e., which cells belong to which component). For such genes, the partition estimates automatically provide an estimate of the proportion of cells in each condition that belong to each subpopulation. We also do not require specification of the total number of components, which can vary for each gene.
When applied to cells at different differentiation stages, this information may provide insight into which genes are responsible for driving phenotypic changes. The gene in Fig. 3 b, for example, shows a DP of cells across conditions, which is important to recognize since DP suggests a change in cellspecific responses to signaling [7, 29]. This is in contrast to the DM gene in Fig. 3 c, which indicates the presence of a distinct cell type in one condition, but not the other. Recent methods for scRNAseq [17, 18, 27, 28, 46] may be able to identify genes such as those shown in Fig. 3 b–d as differing between conditions. However, our simulations suggest that they would be relatively underpowered to do so, and they would be unable to characterize the change as DP, DM, or DB.
We also show through simulation that our approach can accommodate large sample sizes of several hundreds of cells per condition. Note, however, that the real strength in the modeling framework lies in the ability to characterize patterns of DDs. In the presence of extreme sparsity, this will be a challenge, since the number of nonzero observations in a given gene will be small. If the sample size of nonzero measurements is too small, it will be difficult to infer the presence of multiple underlying cell states. In practice, for larger and more sparse datasets, it is recommended to verify that the number of cells expressing a given gene is in the range of the sample sizes considered in this study to utilize fully the available features of scDD.
The approach is limited in that adjustments for covariates are not directly incorporated into the model. In general, when the relationship between a potential confounding variable and the quantification of expression is well known (e.g., increased sequencing depth is generally associated with increased expression measurements), this should be accounted for in a normalization procedure. For other covariates that are not as well characterized (e.g., the cellular detection rate and batch effects), residuals can be used in the permutation procedure, though a more unified approach would be desirable. We also note that more complex confounding variables may be present in scRNAseq experiments that are nonlinear in nature (e.g., covariatespecific effects on variance). We show in Additional file 1: Section 2.3 that when these effects are extreme, care must be taken in interpreting DD genes that are uncategorized.
Additionally, the approach is limited in that only pairwise comparisons across biological conditions are feasible. While an extended Bayes factor score to test for the dependence of a condition on a partition estimation for more than two conditions would be straightforward, the classification into meaningful patterns would be less so, and work is underway in that direction. Finally, we note that while the genes identified by scDD may prove useful in downstream analysis, interpretability is limited as partitions are estimated independently for each gene and consequently do not provide a unified clustering of cells based on global gene expression changes. Extensions in this direction are also underway.
Conclusions
To our knowledge, we have presented the first statistical method to detect differences in scRNAseq experiments that explicitly accounts for potential multimodality of the distribution of expressed cells in each condition. Such multimodal expression patterns are pervasive in scRNAseq data and are of great interest, since they represent biological heterogeneity within otherwise homogeneous cell populations; differences across conditions imply differential regulation or response in the two groups. We have introduced a set of five interesting patterns to summarize the key features that can differ between two conditions. Using simulation studies, we have shown that our method has comparable performance to existing methods when differences (mean shifts) exist between unimodal distributions across conditions, and it outperforms existing approaches when there are more complex differences.
Methods
Software implementations and applications
All analyses were carried out using R version 3.1.1 [47]. The method MAST [18] was implemented using the MAST R package version 0.931, obtained from GitHub at https://github.com/RGLab/MAST. The adjustment for cellular detection rate as recommended in [18] was included in the case study, but not in the simulation study (only the normal component of the test was considered here since no difference in dropout rate was simulated). The method SCDE [17] was implemented using the scde R package version 1.0, obtained from http://pklab.med.harvard.edu/scde/index.html. No adjustment for cellular detection rate was carried out since SCDE cannot accommodate covariates. Since SCDE requires raw integer counts as input, and expected counts are noninteger valued, the ceiling function was applied to the unnormalized counts. For each approach, the target FDR was controlled at 5 %. Specifically, both MAST and SCDE provide genespecific p values and use the method of [35] to control FDR. We followed the same procedure here.
Our method is implemented using version 1.1.0 of the scDD R package, available at https://github.com/kdkorthauer/scDD. The analysis involves a computationally intensive permutation step, which is executed in parallel on multiple cores if available. On a Linux machine using 12 cores and up to 16 gigabytes of memory, this step took approximately 60 minutes for 1000 permutations of 1000 genes in the simulation of 50 samples per condition. Computation time scales approximately linearly with sample size, and this same task takes approximately 90 minutes for 100 samples per condition, and 300 minutes for a sample size of 500 per condition. The computation time to analyze the simulated datasets for SCDE (MAST) ranged from approximately 3 to 30 (0.5 to 5) minutes across the different sample sizes.
hESC culture and differentiation
All cell culture and scRNAseq experiments were conducted as described previously [30, 48]. Briefly, undifferentiated H1 and H9 hESCs were routinely maintained at the undifferentiated state in E8 medium on Matrigel (BD Bioscience) coated tissue culture plates with daily medium feeding [49]. HESCs were passaged every 3 to 4 days with 0.5 mM ethylenediaminetetraacetic acid (EDTA) in phosphatebuffered saline (PBS) at 1:10 to 1:15 ratio for maintenance. H1 were differentiated according to previously established protocols [50, 51]. All the cell cultures performed in our laboratory have been routinely tested as negative for mycoplasma contamination.
For DECs, H1 cells were individualized with Accutase (Life Technologies), seeded in E8 with BMP4 (5 ng/ml), Activin A (25 ng/ml) and CHIR99021 (1 μM) for the first 2 days, then withdraw CHIR99021 for the remaining period of differentiation. DECs were harvested at the end of day 5, and sorted for the CXCR4positive population for scRNAseq experiments. For NPCs, the undifferentiated H1SOX2mCherry reporter line was treated with 0.5 mM EDTA in PBS for 3 to 5 min and seeded in E6 (E8 minus FGF2, minus TGF β1), with 2.5 μg/ml insulin, SB431542 (10 μM) and 100 ng/ml Noggin. NPCs were harvested and enriched at the end of day 7, after sorting for the Cherrypositive population for scRNAseq experiments. All differentiation media were changed daily.
Read mapping, quality control, and normalization
For each of the cell types studied, expected counts were obtained from RSEM [52]. In each condition there are a maximum of 96 cells, but all have fewer than 96 cells due to removal by quality control standards. Some cells were removed due to cell death or doublet cell capture, indicated by a post cell capture image analysis as well as a very low percentage of mapped reads. For more details on read mapping and quality control, see [30, 48]. DESeq normalization [53] was carried out using the MedianNorm function in the EBSeq R package [54] to obtain library sizes. The library sizes were applied to scale the count data. Further, genes with a very low detection rate (detected in fewer than 25 % of cells in either condition) are not considered.
Publicly available scRNAseq datasets
Processed FPKMnormalized data from human myoblast cells [31] were obtained from GEO [55] using accession number GSE52529. In this study, we examined the set of cells cultured on standard growth medium (samples labeled with T0) as well as those treated with differentiationinducing medium for 72 h (samples labeled with T72). Processed TPMnormalized data from mESCs [32] were also obtained from GEO under accession number GSE60749. In this study, we examined the samples labeled as mESC (cultured in standard medium), along with the samples labeled as TwoiLIF (cultured in 2i + LIF differentiationinhibitory medium).
Publicly available bulk RNAseq datasets
The modality of the gene expression distributions in bulk RNAseq was explored using large, publicly available datasets and the results are displayed in Fig. 2. In this figure, the red bars depict the bulk RNAseq results, and datasets are labeled according to their source and sample size. Datasets GE.50, GE.75, and GE.100 are constructed by randomly sampling 50, 75, and 100 samples from GEUVADIS [56] to obtain sample sizes comparable to the singlecell sets under study (obtained from the GEUVADIS consortium data browser at www.ebi.ac.uk/arrayexpress/files/EGEUV1/analysis_results/GD660.GeneQuantCount.txt.gz). Dataset LC consists of 77 normal lung tissue samples from the TCGA lung adenocarcinoma study [57] (obtained from GEO [55] using accession number GSE40419). All datasets were normalized using DESeq normalization [53] except for LC, for which the authors supplied values already normalized by RPKM.
Mixture model formulation
Dirichlet process mixture of normals
Let \({Y^{c}_{g}}=(y^{c}_{g1},\ldots,y^{c}_{g{J_{c}}})\) be the logtransformed nonzero expression measurements of gene g for a collection of J _{ c } cells in condition c out of 2 total conditions. For simplicity of presentation, we drop the dependency on g for now, and let the total number of cells with nonzero measurements be J. We assume that under the null hypothesis of equivalent distributions (i.e., no dependency on condition), Y={Y ^{c}}_{ c=1,2} can be modeled by a conjugate DPM of normals given by
where DP is the Dirichlet process with base distribution G _{0} and precision parameter α, N(μ _{ j },τ _{ j }) is the normal distribution parameterized with mean μ _{ j } and precision τ _{ j } (i.e., with variance \(\tau _{j}^{2}\)), and NG(m _{0},s _{0},a _{0}/2,2/b _{0}) is the normalgamma distribution with mean m _{0}, precision s _{0} τ _{ j }, shape a _{0}/2, and scale 2/b _{0}. Let K denote the number of components [unique values among \((\mu, \tau) = \{\mu _{j}, \tau _{j}\}_{j=1}^{J}\)]. Note that two observations indexed by j and j ^{′} belong to the same component if and only if \((\mu _{j}, \tau _{j})=(\mu _{j^{\prime }}, \phantom {\dot {i}\!}\tau _{j^{\prime }})\).
Product partition models
The posterior distribution of (μ,τ) is intractable even for moderate sample sizes. This is because the number of possible partitions (clusterings) of the data grows extremely rapidly as the sample size increases (according to the Bell number). However, if we let Z=(z _{1},…,z _{ J }) be the vector of component memberships of gene g for all samples, where the number of unique Z values is K, the probability density of Y conditional on Z can be viewed as a PPM [58, 59]. Thus, it can be written as a product over all componentspecific densities:
where y ^{(k)} is the vector of observations belonging to component k and f(y ^{(k)}) is the componentspecific distribution after integrating over all other parameters. In the conjugate normalgamma setting, this has a closed form given by
The posterior for the parameters (μ _{ k },τ _{ k }) conditional on the partition is
The posterior parameters (m _{ k }, s _{ k }, a _{ k }, b _{ k }) also have a closed form due to the conjugacy of the model given by Eq. 1. These parameters are given by
where n ^{(k)} is the number of observations in component k. It follows that the marginal posterior distribution of μ _{ k } conditional on the partition is
where t _{ a }(b,c) denotes the generalized Student’s t distribution with a degrees of freedom, noncentrality parameter b, and scale parameter c. The product partition DPM model can be simplified as follows:
Then we can obtain the joint predictive distribution of the data Y and partition Z by incorporating Eq. 7:
Modelfitting
The fitting of the model given in Eq. 7 involves obtaining an estimate \(\hat {Z}\) of the partition. The goal is to find the partition that yields the highest posterior mass in Eq. 8, referred to as the maximum a posteriori (MAP) partition estimate. Under this modeling framework, the solution for the MAP estimate is not deterministic and several computational procedures have been developed utilizing Polya urn Gibbs sampling [60–62], agglomerative greedy search algorithms [63, 64], or an iterative stochastic search [65].
These procedures generally involve evaluation of the posterior at many different candidate partitions, and as such tend to be computationally intensive. To avoid this challenge, we recognize the relation to the corresponding estimation problem in the finite mixture model framework, where the partition estimate can be obtained by optimizing the Bayesian information criterion (BIC) of the marginal density f(YZ) [66]. In fact, for certain settings of the prior distribution over partitions, the MAP estimate is identical to the estimate obtained by optimizing the BIC [59]. In practice, even when these settings are not invoked, the performance of partition estimates obtained via BIC optimization show comparable performance (see Additional file 1: Section 1). We obtain the partition estimate \(\hat {Z}\) that optimizes the BIC using the Mclust R package [66] and satisfies the criteria for multimodality described in the next section.
The hyperparameters for the componentspecific mean and precision parameters were chosen so as to encode a heavytailed distribution over the parameters. Specifically, the parameters were set to μ _{0}=0, \({\tau _{0}^{2}}=0.01\), a _{0}=0.01, and b _{0}=0.01. The Dirichlet concentration parameter was set to α=0.01, and choosing this is shown in Additional file 1: Section 1 to be robust to many different settings in a sensitivity analysis.
Partition estimation
The partition estimate \(\hat {Z}\) is obtained that optimizes BIC using Mclust [66], in addition to the following filtering criteria. Note that the only constraint imposed on the number of components K in the modeling framework is that K≤J. However, under the sample sizes in this study, we consider only K≤5. The first filtering criterion is based on the notion that a twocomponent mixture model is not necessarily bimodal [67], and relaxes the requirement that the MAP estimate corresponds to the model with the lowest BIC. Specifically, for each candidate model fitted by BIC with K components, a split step (if K=1, obtain a new partition estimate \(\hat {Z}\) with K=2 unique elements) or a merge step (if K≥2, obtain a new partition estimate \(\hat {Z}\) restricted to K−1 unique elements) is carried out to generate a new candidate partition. The candidate partition with the larger value of K becomes the partition estimate only if the component separation suggests multimodality. Component separation between any pair of components is assessed with the bimodality index (BI) [68]:
where the component means μ _{1} and μ _{2} are estimated via maximum likelihood, the common withincomponent standard deviation σ is conservatively estimated with the maximum withincomponent standard deviation among all components, and n _{1} and n _{2} are the number of cells belonging to each component. BI thresholds for the split and merge step were determined empirically and vary by sample size, as multiple modes are more easily detected as sample size increases [68] (for more details see Additional file 1: Section 4).
The second filtering criterion is designed to reduce the impact of outlier cells. Specifically, components with fewer than three cells are not considered, and the merge step is also carried out if one of the components present has an extremely large variance compared to the others (more than 20 times larger than any other component). Likewise, the split step is not carried out if one of the proposed components has a variance more than 10 times larger than any other component.
Simulation details
Component means and variances
Each gene was simulated based on the characteristics of a randomly sampled unimodal gene with at least 25 % nonzero measurements in the H1 dataset. For unimodal genes, the mean and variance were chosen to match the observed mean and variance; for bimodal genes, the component means and variances were selected to be near the observed mean and variance. The proportion of zeroes is chosen to match that observed in the randomly sampled gene, and is not varied by condition. Details are provided in the following sections.
Distances between (logscale) component means Δ _{ μ } σ in the multimodal genes were chosen such that components were separated by a minimum of two and a maximum of six standard deviations, where the standard deviation σ is assumed constant (on the logscale) across components. The specific values of σ used for the simulated genes are empirical estimates of the standard deviations of the unimodal case study genes (assuming a lognormal distribution on the raw scale). In this setting, the component distance can also be thought of as a foldchange within condition (across components), where the ratio of the component means (untransformedscale) is equal to \(\mathrm {e}^{\Delta _{\mu }\hat {\sigma }}\). The ratio of the component standard deviations (raw scale) is also equal to this same foldchange (see Additional file 1: Section 2.1 for more details). The component mean distance values were chosen to represent a range of settings for which the difficulty of detecting multimodality is widely varied, as well as to reflect the range of observed component mean distances detected empirically in the case studies.
Unimodal genes
The parameters of the negative binomial distribution for unimodal genes were estimated from the randomly sampled observed genes using the method of moments. These empirical parameters were used as is to simulate both conditions of EE genes, and condition 1 of DE and DB. Condition 1 of DM was simulated by decreasing the mean by half the value of Δ _{ μ }. The second condition for DE genes was simulated based on condition 1 parameters using randomly sampled foldchanges that were between two and three standard deviations of the observed foldchanges between H1 and DEC.
Bimodal genes
The parameters for the mixture of negative binomial distributions in bimodal genes were also generated using empirically estimated means and variances. The first (lower) component mean was decreased by half the value of Δ _{ μ } and the second (higher) component mean was increased by half the value of Δ _{ μ }.
DD classification algorithm
Genes detected as significantly DD from the permutation test of the Bayes factor score are categorized into patterns of interest. The genes that are not classified as DE, DP, DM, or DB are considered to be no calls, abbreviated NC. These represent patterns that are not of primary interest, such as those that differ only in variance (but not in the number of components or their means). This type of difference may result from cellspecific differences in technical variation [17], which can only be decomposed from biological variation in experimental protocols that allow for independent estimation of technical effects using spikein controls, for example [69].
An additional step to improve the power to detect genes in the DP category was also implemented. This step was motivated by the observation that the Bayes factor score tends to be small when the clustering process within each condition is consistent with that overall, as in the case of DP. Thus, for genes that were not significantly DD by permutation but had the same number of components within condition as overall, Fisher’s exact test was used to test for independence with biological condition. If the p value for that test is less than 0.05, then the gene was added to the DP category (this did not result in the addition of any false positives in the simulation study). In addition, since the Bayes factor score depends on the estimated partition, we increase the robustness of the approach to detect DD genes under possible misspecification of the partition by also assessing evidence of DD in the form of an overall mean shift for genes not significant by the permutation test (using a tstatistic with FDR controlled by [35]). This resulted in the detection of between 121 and 689 additional genes in the hESC comparisons and did not add any false positives in 94 % of simulation replications (with only a single false positive gene in the other 6 % of replications).
Here we present pseudocode for the classification of DD genes into the categories DE, DP, DM, or DB. For every pair of components, we obtain a sample of 10,000 observations from the posterior distribution of the difference in means. The components are considered to overlap if the 100 % credible interval contains 0.
DD classification algorithm
Abbreviations
 BIC:

Bayesian information criterion
 DD:

differential distribution
 DE:

Differential expression
 DEC:

Definitive endoderm cell
 DP:

Differential proportion
 DM:

Differential modality
 DB:

Both differential modality and different component means
 DPM:

Dirichlet process mixture
 DZ:

Differential zeroes
 ED:

Equivalent distribution
 EDTA:

Ethylenediaminetetraacetic acid
 EE:

Equivalent expression
 EP:

Equivalent proportion
 FDR:

False discovery rate
 hESC:

Human embryonic stem cell
 mESC:

Mouse embryonic stem cell
 MAP:

Maximum a posteriori
 NC:

no call
 NPC:

Neuronal progenitor cell
 PBS:

Phosphatebuffered saline
 PPM:

Product partition model
 scDD:

Singlecell differential distributions
 scRNAseq:

Singlecell RNA sequencing
References
 1
Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, et al. Computational analysis of celltocell heterogeneity in singlecell RNAsequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015; 32(2):155–60.
 2
Trombetta JJ, Gennert D, Lu D, Satija R, Shalek AK, Regev A. Preparation of singlecell RNAseq libraries for next generation sequencing. Curr Protoc Mol Biol. 2014; 107(2):4–22. 117.
 3
Tang F, Barbacioru C, Bao S, Lee C, Nordman E, Wang X, et al. Tracing the derivation of embryonic stem cells from the inner cell mass by singlecell RNAseq analysis. Cell Stem Cell. 2010; 6(5):468–78.
 4
Yan L, Yang M, Guo H, Yang L, Wu J, Li R, et al. Singlecell RNAseq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol. 2013; 20(9):1131–9.
 5
Xue Z, Huang K, Cai C, Cai L, Jiang Cy, Feng Y, et al. Genetic programs in human and mouse early embryos revealed by singlecell RNA sequencing. Nature. 2013; 500(7464):593–7.
 6
Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Singlecell RNAseq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344(6190):1396–401.
 7
Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, et al. Singlecell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013; 498(7453):236–40.
 8
Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, et al. Reconstructing lineage hierarchies of the distal lung epithelium using singlecell RNAseq. Nature. 2014; 509(7500):371–5.
 9
Hong S, Chen X, Jin L, Xiong M. Canonical correlation analysis for RNAseq coexpression networks. Nucleic Acids Res. 2013; 41(8):95–5.
 10
Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Fulllength mRNAseq from singlecell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012; 30(8):777–82.
 11
Kim KT, Lee HW, Lee HO, Kim SC, Seo YJ, Chung W, et al. Singlecell mRNA sequencing identifies subclonal heterogeneity in anticancer drug responses of lung adenocarcinoma cells. Genome Biol. 2015; 16(1):127.
 12
Lee MCW, LopezDiaz FJ, Khan SY, Tariq MA, Dayn Y, Vaske CJ, et al. Singlecell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing. Proc Natl Acad Sci. 2014; 111(44):4726–35.
 13
Powell AA, Talasaz AH, Zhang H, Coram MA, Reddy A, Deng G, et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PloS ONE. 2012; 7(5):33788.
 14
Hashimshony T, Wagner F, Sher N, Yanai I. CELseq: singlecell RNAseq by multiplexed linear amplification. Cell Rep. 2012; 2(3):666–73.
 15
Brunskill EW, Park JS, Chung E, Chen F, Magella B, Potter SS. Single cell dissection of early kidney development: multilineage priming. Development. 2014; 141(15):3093–101.
 16
Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From singlecell to cellpool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014; 24(3):496–510.
 17
Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to singlecell differential expression analysis. Nat Methods. 2014; 11(7):740–2.
 18
Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. Mast: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in singlecell RNA sequencing data. Genome Biol. 2015; 16(1):1–13.
 19
Kærn M, Elston TC, Blake WJ, Collins JJ. Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet. 2005; 6(6):451–64.
 20
Birtwistle MR, Rauch J, Kiyatkin A, Aksamitiene E, Dobrzyński M, Hoek JB, et al. Emergence of bimodal cell population responses from the interplay between analog singlecell signaling and protein expression noise. BMC Syst Biol. 2012; 6(1):109.
 21
Dobrzyński M, Fey D, Nguyen LK, Kholodenko BN. Bimodal protein distributions in heterogeneous oscillating systems. In: Computational methods in systems biology. Berlin Heidelberg: Springer: 2012. p. 17–28.
 22
Singer ZS, Yong J, Tischler J, Hackett JA, Altinok A, Surani MA, et al. Dynamic heterogeneity and DNA methylation in embryonic stem cells. Mol Cell. 2014; 55(2):319–31.
 23
Dobrzyński M, Nguyen LK, Birtwistle MR, von Kriegsheim A, Fernández AB, Cheong A, et al. Nonlinear signalling networks and celltocell variability transform external signals into broadly distributed or bimodal responses. J R Soc Interface. 2014; 11(98):20140383.
 24
Ozbudak EM, Thattai M, Kurtser I, Grossman AD, van Oudenaarden A. Regulation of noise in the expression of a single gene. Nat Genet. 2002; 31(1):69–73.
 25
Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 2006; 4(10):309.
 26
Thattai M, Van Oudenaarden A. Intrinsic noise in gene regulatory networks. Proc Natl Acad Sci. 2001; 98(15):8614–19.
 27
Delmans M, Hemberg M. Discrete distributional differential expression (D3E) – a tool for gene expression analysis of singlecell RNAseq data. bioRxiv. 2015. doi:10.1101/020735.
 28
Katayama S, Töhönen V, Linnarsson S, Kere J. SAMstrt: statistical test for differential expression in singlecell transcriptome with spikein normalization. Bioinformatics. 2013; 29(22):2943–5.
 29
Tay S, Hughey JJ, Lee TK, Lipniacki T, Quake SR, Covert MW. Singlecell NF κ B dynamics reveal digital activation and analogue information processing. Nature. 2010; 466(7303):267–71.
 30
Chu LiFang, et al.Singlecell RNAseq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol. 2016; 17(1):173.
 31
Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4):381–6.
 32
Kumar RM, Cahan P, Shalek AK, Satija R, DaleyKeyser AJ, Li H, et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature. 2014; 516(7529):56–61.
 33
Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, et al. Comprehensive evaluation of differential gene expression analysis methods for RNAseq data. Genome Biol. 2013; 14(9):95.
 34
Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNAseq read counts. Genome Biol. 2014; 15(2):29.
 35
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995; 57(1):289–300.
 36
Wagner BD, Zerbe GO, Mexal S, Leonard SS. Permutationbased adjustments for the significance of partial regression coefficients in microarray data analysis. Genet Epidemiol. 2008; 32(1):1–8.
 37
Miura T, Luo Y, Khrebtukova I, Brandenberger R, Zhou D, Scott Thies R, et al. Monitoring early differentiation events in human embryonic stem cells by massively parallel signature sequencing and expressed sequence tag scan. Stem Cells Dev. 2004; 13(6):694–715.
 38
Armstrong L, Hughes O, Yung S, Hyslop L, Stewart R, Wappler I, et al. The role of pi3k/akt, mapk/erk and nf κ β signalling in the maintenance of human embryonic stem cell pluripotency and viability highlighted by transcriptional profiling and functional analysis. Hum Mol Genet. 2006; 15(11):1894–913.
 39
Shi L, Lin YH, Sierant M, Zhu F, Cui S, Guan Y, et al. Developmental transcriptome analysis of humwan erythropoiesis. Hum Mol Genet. 2014; 23(17):4528–42.
 40
Kolodziejczyk AA, Kim JK, Tsang JC, Ilicic T, Henriksson J, Natarajan KN, et al. Single cell RNAsequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell. 2015; 17(4):471–85.
 41
Walworth NC. Cellcycle checkpoint kinases: checking in on the cell cycle. Curr Opin Cell Biol. 2000; 12(6):697–704.
 42
Malumbres M, Barbacid M. Mammalian cyclindependent kinases. Trends Biochem Sci. 2005; 30(11):630–41.
 43
White J, Dalton S. Cell cycle control of embryonic stem cells. Stem Cell Rev. 2005; 1(2):131–8.
 44
Gabut M, SamavarchiTehrani P, Wang X, Slobodeniuc V, O’Hanlon D, Sung HK, et al. An alternative splicing switch regulates embryonic stem cell pluripotency and reprogramming. Cell. 2011; 147(1):132–46.
 45
Atkinson SP, Collin J, Irina N, Anyfantis G, Kyung BK, Lako M, et al. A putative role for the immunoproteasome in the maintenance of pluripotency in human embryonic stem cells. Stem Cells. 2012; 30(7):1373–84.
 46
Kim JK, Marioni JC. Inferring the kinetics of stochastic gene expression from singlecell RNAsequencing data. Genome Biol. 2013; 14(1):7.
 47
R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014. http://www.Rproject.org. R Foundation for Statistical Computing.
 48
Leng N, Chu LF, Barry C, Li Y, Choi J, Li X, et al. Oscope identifies oscillatory genes in unsynchronized singlecell RNAseq experiments. Nat Methods. 2015; 12(10):947–50.
 49
Chen G, Gulbranson DR, Hou Z, Bolin JM, Ruotti V, Probasco MD, et al. Chemically defined conditions for human iPSC derivation and culture. Nat Methods. 2011; 8(5):424–9.
 50
Xie W, Schultz MD, Lister R, Hou Z, Rajagopal N, Ray P, et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell. 2013; 153(5):1134–48.
 51
Schwartz MP, Hou Z, Propson NE, Zhang J, Engstrom CJ, Costa VS, et al. Human pluripotent stem cellderived neural constructs for predicting neural toxicity. Proc Natl Acad Sci. 2015; 112(40):12516–21.
 52
Li B, Dewey CN. Rsem: accurate transcript quantification from RNAseq data with or without a reference genome. BMC Bioinform. 2011; 12(1):323.
 53
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010; 11(10):106.
 54
Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, et al. Ebseq: an empirical Bayes hierarchical model for inference in RNAseq experiments. Bioinformatics. 2013; 29(8):1035–43.
 55
Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002; 30(1):207–10.
 56
Lappalainen T, Sammeth M, Friedländer MR, AC ’t Hoen P, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013; 501(7468):506–11.
 57
Seo JS, Ju YS, Lee WC, Shin JY, Lee JK, Bleazard T, et al. The transcriptional landscape and mutational profile of lung adenocarcinoma. Genome Res. 2012; 22:2109–19.
 58
Hartigan JA. Partition models. Commun Stat Theory Meth. 1990; 19(8):2745–56.
 59
Shotwell MS, Slate EH. Bayesian outlier detection with dirichlet process mixtures. Bayesian Anal. 2011; 6(4):665–90.
 60
MacEachern SN. Estimating normal means with a conjugate style Dirichlet process prior. Commun Stat Simul Comput. 1994; 23(3):727–41.
 61
Bush CA, MacEachern SN. A semiparametric Bayesian model for randomised block designs. Biometrika. 1996; 83(2):275–85.
 62
MacEachern SN, Müller P. Estimating mixture of Dirichlet process models. J Comput Graph Stat. 1998; 7(2):223–38.
 63
Ward Jr JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963; 58(301):236–44.
 64
Wang L, Dunson DB. Fast Bayesian inference in Dirichlet process mixture models. J Comput Graph Stat. 2011; 20(1):196–216.
 65
Shotwell MS. profdpm: An R package for MAP estimation in a class of conjugate product partition models. J Stat Softw. 2013; 53(8):1–18.
 66
Fraley C, Raftery AE, Murphy TB, Scrucca L. MCLUST version 4 for r: Normal mixture modeling for modelbased clustering, classification, and density estimation. University of Washington, Department of Statistics. 2012. Technical report 597.
 67
Tarpey T, Yun D, Petkova E. Model misspecification finite mixture or homogeneous?Stat Model. 2008; 8(2):199–218.
 68
Wang J, Wen S, Symmans WF, Pusztai L, Coombes KR. The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data. Cancer Informat. 2009; 7:199.
 69
Vallejos CA, Richardson S, Marioni JC. Beyond comparisons of means: understanding changes in gene expression at the singlecell level. Genome Biol. 2016; 17(1):1.
Acknowledgments
The authors thank the editorial staff and two anonymous reviewers for insightful comments and suggestions that helped improve the quality of the manuscript.
Funding
This work was supported by National Institutes of Health (NIH) grant GM102756 (CK), NIH grant U54AI117924 (CK), NIH grant 4UH3TR00050603 (JAT), and grant 5U01HL09977306 (JAT).
Availability of data and materials
The hESC data has been deposited in GEO [55] with accession number GSE75748 [30].
Sensitivity analyses, further methodological details, and additional results are provided in a supplement.
Authors’ contributions
CK, LFC, JAT, RMS, and KDK formulated the problem. CK, KDK, and MAN developed the scDD model. KDK implemented the scDD model in R, developed and implemented the simulations, and applied scDD to the hESC case study data. YL assisted with the simulation study. LFC conducted the hESC experiments. CK, LFC, RMS, and KDK interpreted the results. KDK and CK wrote the paper. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Ethics approval and consent to participate
Not applicable.
Author information
Affiliations
Corresponding author
Additional file
Additional file 1
Supplement. Sensitivity analyses of MAP estimation method, further methodological details, and additional results. (PDF 553 kb)
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Korthauer, K.D., Chu, L., Newton, M.A. et al. A statistical approach for identifying differential distributions in singlecell RNAseq experiments. Genome Biol 17, 222 (2016). https://doi.org/10.1186/s130590161077y
Received:
Accepted:
Published:
Keywords
 Singlecell RNAseq
 Differential expression
 Cellular heterogeneity
 Mixture modeling