Classification of Single-Cell Gene Expression Trajectories from Incomplete and Noisy Data

GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression

The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's "internal clock". To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.

Optimal Bayesian Transfer Learning for Count Data

There is often a limited amount of omics data to design predictive models in biomedicine. Knowing that these omics data come from underlying processes that may share common pathways and disease mechanisms, it may be beneficial for designing a more accurate and reliable predictor in a target domain of interest, where there is a lack of labeled data to leverage available data in relevant source domains. Here, we focus on developing Bayesian transfer learning methods for analyzing next-generation sequencing (NGS) data to help improve predictions in the target domain. We formulate transfer learning in a fully Bayesian framework and define the relatedness by a joint prior distribution of the model parameters of the source and target domains. Defining joint priors acts as a bridge across domains, through which the related knowledge of source data is transferred to the target domain. We focus on RNA-seq discrete count data, which are often overdispersed. To appropriately model them, we consider the Negative Binomial model and propose an Optimal Bayesian Transfer Learning (OBTL) classifier that minimizes the expected classification error in the target domain. We evaluate the performance of the OBTL classifier via both synthetic and cancer data from The Cancer Genome Atlas (TCGA).

Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning

Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point, referred to as the immediate cost function, is fully known. In this paper, we employ the Partially-Observed Boolean Dynamical System (POBDS) signal model for a time sequence of noisy expression measurement from a Boolean GRN and develop a Bayesian Inverse Reinforcement Learning (BIRL) approach to address the realistic case in which the only available knowledge regarding the immediate cost function is provided by the sequence of measurements and interventions recorded in an experimental setting by an expert. The Boolean Kalman Smoother (BKS) algorithm is used for optimally mapping the available gene-expression data into a sequence of Boolean states, and then the BIRL method is efficiently combined with the Q-learning algorithm for quantification of the immediate cost function. The performance of the proposed methodology is investigated by applying a state-feedback controller to two GRN models: a melanoma WNT5A Boolean network and a p53-MDM2 negative feedback loop Boolean network, when the cost of the undesirable states, and thus the identity of the undesirable genes, is learned using the proposed methodology.

Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty

Background

Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty.

Results

The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution.

Conclusion

The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression data.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5720-3) contains supplementary material, which is available to authorized users.

Phenotype Classification Using Moment Features of Single-Cell Data

Features for standard expression microarray and RNA-Seq classification are expression averages over collections of cells. Single cell provides expression measurements for individual cells in a collection of cells from a particular tissue sample. Hence, it can yield feature vectors consisting of higher order and mixed moments. This article demonstrates the advantage of using these expression moments in cancer-related classification. We use synthetic data generated from 2 real networks, the mammalian cell cycle network and a melanoma-related pathway network, and real single-cell data generated via fluorescent protein reporters from 2 cell lines, HT-29 and HCT-116. The networks consist of hidden binary regulatory networks with Gaussian observations. The steady-state distributions of both the original and mutated networks are found, and data are drawn from these for moment-based classification using the mean, variance, skewness, and mixed moments. For the real data, we only observe 1 gene at a time, so that only the mean, variance, and skewness are considered, the analysis being done for 2 genes, EGFR and ERRB2. For the synthetic data, classification improves as we move from just the mean to mean, variance, and skewness and then to these plus the mixed moments. Comparisons are done with 3, 4, or 5 features, using feature selection. Sample size effects are considered. For the real data, we only consider mean, variance, and skewness, with results improving when the higher order moments are used as features.

Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks

Background

Expression-based phenotype classification using either microarray or RNA-Seq measurements suffers from a lack of specificity because pathway timing is not revealed and expressions are averaged across groups of cells. This paper studies expression-based classification under the assumption that single-cell measurements are sampled at a sufficient rate to detect regulatory timing. Thus, observations are expression trajectories. In effect, classification is performed on data generated by an underlying gene regulatory network.

Results

Network regulation is modeled via a Boolean network with perturbation, regulation not fully determined owing to inherent biological randomness. The binary assumption is not critical because the resulting Markov chain characterizes expression trajectories. We assume a partially known Gaussian observation model belonging to an uncertainty class of models. We derive the intrinsically Bayesian robust classifier to discriminate between wild-type and mutated networks based on expression trajectories. The classifier minimizes the expected error across the uncertainty class relative to the prior distribution. We test it using a mammalian cell-cycle model, discriminating between the normal network and one in which gene p27 is mutated, thereby producing a cancerous phenotype. Tests examine all model aspects, including trajectory length, perturbation probability, and the hyperparameters governing the prior distribution over the uncertainty class.

Conclusions

Simulations show the rates at which the expected error is diminished by smaller perturbation probability, longer trajectories, and hyperparameters that tighten the prior distribution relative to the unknown true network. For average-expression measurement, methods have been proposed to obtain prior distributions. These should be extended to the more mathematically difficult, but more informative, expression trajectories.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0549-y) contains supplementary material, which is available to authorized users.