BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data

AbstractHigh-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through use of pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provides an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on both synthetic data and a published single-cell gene expression hematopoiesis study. The method requires prior information about pseudotime and global cellular branching for each cell but the probabilistic nature of the method means that it is robust to errors in these global branch labels and can be used to discover early branching genes which diverge before the inferred global cell branching. The code is open-source and available at https://github.com/ManchesterBioinference/BranchedGP.

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Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

10.1101/036533 ◽

2016 ◽

Author(s):

Gregory Giecold ◽

Eugenio Marco ◽

Lorenzo Trippa ◽

Guo-Cheng Yuan

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Quantitative Estimate ◽

Cell Lineage ◽

Computational Method ◽

Expression Data ◽

Cell Gene Expression ◽

Cell Data ◽

Cell Gene

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR, a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.

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scDesign2: an interpretable simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

10.1101/2020.11.17.387795 ◽

2020 ◽

Author(s):

Tianyi Sun ◽

Dongyuan Song ◽

Wei Vivian Li ◽

Jingyi Jessica Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

Computational Methods ◽

Probabilistic Models ◽

Synthetic Data ◽

Cell Number ◽

High Fidelity ◽

Rna Seq ◽

Cell Gene Expression ◽

Cell Gene

AbstractIn the burgeoning field of single-cell transcriptomics, a pressing challenge is to benchmark various experimental protocols and numerous computational methods in an unbiased manner. Although dozens of simulators have been developed for single-cell RNA-seq (scRNA-seq) data, they lack the capacity to simultaneously achieve all the three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill in this gap, here we propose scDesign2, an interpretable simulator that achieves all the three goals and generates high-fidelity synthetic data for multiple scRNA-seq protocols and other single-cell gene expression count-based technologies. Compared with existing simulators, scDesign2 is advantageous in its transparent use of probabilistic models and is unique in its ability to capture gene correlations via copula. We verify that scDesign2 generates more realistic synthetic data for four scRNA-seq protocols (10x Genomics, CEL-Seq2, Fluidigm C1, and Smart-Seq2) and two single-cell spatial transcriptomics protocols (MERFISH and pciSeq) than existing simulators do. Under two typical computational tasks, cell clustering and rare cell type detection, we demonstrate that scDesign2 provides informative guidance on deciding the optimal sequencing depth and cell number in single-cell RNA-seq experimental design, and that scDesign2 can effectively benchmark computational methods under varying sequencing depths and cell numbers. With these advantages, scDesign2 is a powerful tool for single-cell researchers to design experiments, develop computational methods, and choose appropriate methods for specific data analysis needs.

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Gene regulatory network inference from single-cell data using multivariate information measures

10.1101/082099 ◽

2016 ◽

Cited By ~ 4

Author(s):

Thalia E. Chan ◽

Michael P.H. Stumpf ◽

Ann C. Babtie

Keyword(s):

Gene Expression ◽

Information Theory ◽

Single Cell ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Functional Relationships ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Data ◽

Cell Gene

AbstractWhile single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here:https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

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scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

Genome Biology ◽

10.1186/s13059-021-02367-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tianyi Sun ◽

Dongyuan Song ◽

Wei Vivian Li ◽

Jingyi Jessica Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

Count Data ◽

Probabilistic Models ◽

Synthetic Data ◽

High Fidelity ◽

Cell Gene Expression ◽

Experimental Protocols ◽

Cell Gene ◽

Number Of Cells

AbstractA pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas.

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SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions

10.1101/124693 ◽

2017 ◽

Cited By ~ 1

Author(s):

Tao Peng ◽

Qing Nie

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Single Cells ◽

High Dimensional ◽

Expression Data ◽

Rna Seq ◽

Cell Gene Expression ◽

Cell Data ◽

Cell Gene

AbstractMeasurement of gene expression levels for multiple genes in single cells provides a powerful approach to study heterogeneity of cell populations and cellular plasticity. While the expression levels of multiple genes in each cell are available in such data, the potential connections among the cells (e.g. the cellular state transition relationship) are not directly evident from the measurement. Classifying the cellular states, identifying their transitions among those states, and extracting the pseudotime ordering of cells are challenging due to the noise in the data and the high-dimensionality in the number of genes in the data. In this paper we adapt the classical self-organizing-map (SOM) approach for single-cell gene expression data (SOMSC), such as those based on single cell qPCR and single cell RNA-seq. In SOMSC, a cellular state map (CSM) is derived and employed to identify cellular states inherited in the population of the measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers among the basins in CSM provide information on transitions among the cellular states. A cellular state transitions path (e.g. differentiation) and a temporal ordering of the measured single cells are consequently obtained. In addition, SOMSC could estimate the cellular state replication probability and transition probabilities. Applied to a set of synthetic data, one single-cell qPCR data set on mouse early embryonic development and two single-cell RNA-seq data sets, SOMSC shows effectiveness in capturing cellular states and their transitions presented in the high-dimensional single-cell data. This approach will have broader applications to analyzing cellular fate specification and cell lineages using single cell gene expression data

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