scholarly journals Transcriptome-wide splicing quantification in single cells

2017 ◽  
Author(s):  
Yuanhua Huang ◽  
Guido Sanguinetti
Keyword(s):  
Rna Processing ◽  
Single Cells ◽  
Bayesian Hierarchical Model ◽  
Bayesian Regression ◽  
Data Sets ◽  
Rna Seq ◽  
Bayesian Hierarchical ◽  
Informative Prior Distribution ◽  
Comprehensive Measurement ◽  
Isoform Quantification

AbstractSingle cell RNA-seq (scRNA-seq) has revolutionised our understanding of transcriptome variability, with profound implications both fundamental and translational. While scRNA-seq provides a comprehensive measurement of stochasticity in transcription, the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here we present BRIE (Bayesian Regression for Isoform Estimation), a Bayesian hierarchical model which resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE therefore expands the scope of scRNA-seq experiments to probe the stochasticity of RNA-processing.

scholarly journals LTMG: A novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

2018 ◽  
Author(s):  
Changlin Wan ◽  
Wennan Chang ◽  
Yu Zhang ◽  
Fenil Shah ◽  
Xiaoyu Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
R Package ◽  
Data Sets ◽  
Rna Seq ◽  
Cell Functions ◽  
Transcriptional Regulatory ◽  
A Cell

ABSTRACTA key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a gene’s diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

scholarly journals dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data

2016 ◽  
Author(s):  
Sergi Sayols ◽  
Denise Scherzinger ◽  
Holger Klein
Keyword(s):  
Single Cells ◽  
Data Sets ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Pcr Duplicates ◽  
Highly Expressed Genes ◽  
Control Step ◽  
Important Quality ◽  
Easy Integration ◽  
Ngs Data

AbstractBackgroundPCR clonal artefacts originating from NGS library preparation can affect both genomic as well as RNA-Seq applications when protocols are pushed to their limits. In RNA-Seq however the artifactual reads are not easy to tell apart from normal read duplication due to natural over-sequencing of highly expressed genes. Especially when working with little input material or single cells assessing the fraction of duplicate reads is an important quality control step for NGS data sets. Up to now there are only tools to calculate the global duplication rates that do not take into account the effect of gene expression levels which leaves them of limited use for RNA-Seq data.ResultsHere we present the tool dupRadar, which provides an easy means to distinguish artefactual from natural duplicate reads in RNA-Seq data. dupRadar assesses the fraction of duplicate reads per gene dependent on the expression level. Apart from the Bioconductor package dupRadar we provide shell scripts for easy integration into processing pipelines.ConclusionsThe Bioconductor package dupRadar offers straight-forward methods to assess RNA-Seq datasets for quality issues with PCR duplicates. It is aimed towards simple integration into standard analysis pipelines as a default QC metric that is especially useful for low-input and single cell RNA-Seq data sets.

A Bayesian hierarchical model for the estimation of two incomplete surveillance data sets

2008 ◽  
Vol 27 (17) ◽  
pp. 3269-3285 ◽  
Author(s):  
Joan Buenconsejo ◽  
Durland Fish ◽  
James E. Childs ◽  
Theodore R. Holford
Keyword(s):  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Surveillance Data ◽  
Data Sets ◽  
Bayesian Hierarchical

scholarly journals Bayesian Hierarchical Model for Differential Gene Expression Using RNA-Seq Data

2013 ◽  
Vol 7 (1) ◽  
pp. 48-67 ◽  
Author(s):  
Juhee Lee ◽  
Yuan Ji ◽  
Shoudan Liang ◽  
Guoshuai Cai ◽  
Peter Müller
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Rna Seq ◽  
Bayesian Hierarchical ◽  
Differential Gene

scholarly journals LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

2019 ◽  
Vol 47 (18) ◽  
pp. e111-e111 ◽  
Author(s):  
Changlin Wan ◽  
Wennan Chang ◽  
Yu Zhang ◽  
Fenil Shah ◽  
Xiaoyu Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
R Package ◽  
Data Sets ◽  
Rna Seq ◽  
Mrna Metabolism ◽  
Transcriptional Regulatory ◽  
User Friendly

Abstract A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

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

2017 ◽  
Author(s):  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Embryo Development ◽  
Single Cells ◽  
High Dimensional ◽  
Data Sets ◽  
Rna Seq ◽  
Expression Levels ◽  
Cell Lineages ◽  
Cell Data

Measurements of gene expression levels for multiple genes in single cells provide 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 lineage relationship) are not directly evident from the measurement. Classifying cellular states and identifying transitions among those states are challenging due to many factors, including the small number of cells versus the large number of genes collected in the data. In this paper we adapt a classical self-organizing-map approach to single-cell gene expression data, such as those based on qPCR and RNA-seq. In this method (SOMSC), a cellular state map (CSM) is derived and employed to identify cellular states inherited in a population of measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers between the basins provide information on transitions among the cellular states. Consequently, paths of cellular state transitions (e.g. differentiation) and a temporal ordering of the measured single cells are obtained. Applied to a set of synthetic data, two single-cell qPCR data sets and two single-cell RNA-seq data sets for a simulated model of cell differentiation, and systems on the early embryo development, haematopoietic cell lineages, human preimplanation embryo development, and human skeletal muscle myoblasts differentiation, the SOMSC shows good capabilities in identifying cellular states and their transitions in the high-dimensional single-cell data. This approach will have broad applications in studying cell lineages and cellular fate specification.

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