The Experts below are selected from a list of 269061 Experts worldwide ranked by ideXlab platform
Nir Yosef - One of the best experts on this subject based on the ideXlab platform.
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impulse model based Differential Expression analysis of time course sequencing data
Nucleic Acids Research, 2018Co-Authors: David S Fischer, Fabian J Theis, Nir YosefAbstract:Temporal changes to the concentration of molecular species such as mRNA, which take place in response to various environmental cues, can often be modeled as simple continuous functions such as a single pulse (impulse) model. The simplicity of such functional representations can provide an improved performance on fundamental tasks such as noise reduction, imputation and Differential Expression analysis. However, temporal gene Expression profiles are often studied with models that treat time as a categorical variable, neglecting the dependence between time points. Here, we present ImpulseDE2, a framework for Differential Expression analysis that combines the power of the impulse model as a continuous representation of temporal responses along with a noise model tailored specifically to sequencing data. We compare the simple categorical models to ImpulseDE2 and to other continuous models based on natural cubic splines and demonstrate the utility of the continuous approach for studying Differential Expression in time course sequencing experiments. A unique feature of ImpulseDE2 is the ability to distinguish permanently from transiently up- or down-regulated genes. Using an in vitro differentiation dataset, we demonstrate that this gene classification scheme can be used to highlight distinct transcriptional programs that are associated with different phases of the differentiation process.
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impulse model based Differential Expression analysis of time course sequencing data
bioRxiv, 2017Co-Authors: David S Fischer, Fabian J Theis, Nir YosefAbstract:The global gene Expression trajectories of cellular systems in response to developmental or environmental stimuli often follow the prototypic single-pulse or state-transition patterns which can be modeled with the impulse model. Here we combine the continuous impulse Expression model with a sequencing data noise model in ImpulseDE2, a Differential Expression algorithm for time course sequencing experiments such as RNA-seq, ATAC-seq and ChIP-seq. We show that ImpulseDE2 outperforms currently used Differential Expression algorithms on data sets with sufficiently many sampled time points. ImpulseDE2 is capable of differentiating between transiently and monotonously changing Expression trajectories. This classification separates genes which are responsible for the initial and final cell state phenotypes from genes which drive or are driven by the cell state transition and identifies down-regulation of oxidative-phosphorylation as a molecular signature which can drive human embryonic stem cell differentiation.
Ramana V Davuluri - One of the best experts on this subject based on the ideXlab platform.
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npebseq nonparametric empirical bayesian based procedure for Differential Expression analysis of rna seq data
BMC Bioinformatics, 2013Co-Authors: Ramana V DavuluriAbstract:Background RNA-seq, a massive parallel-sequencing-based transcriptome profiling method, provides digital data in the form of aligned sequence read counts. The comparative analyses of the data require appropriate statistical methods to estimate the Differential Expression of transcript variants across different cell/tissue types and disease conditions.
David S Fischer - One of the best experts on this subject based on the ideXlab platform.
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impulse model based Differential Expression analysis of time course sequencing data
Nucleic Acids Research, 2018Co-Authors: David S Fischer, Fabian J Theis, Nir YosefAbstract:Temporal changes to the concentration of molecular species such as mRNA, which take place in response to various environmental cues, can often be modeled as simple continuous functions such as a single pulse (impulse) model. The simplicity of such functional representations can provide an improved performance on fundamental tasks such as noise reduction, imputation and Differential Expression analysis. However, temporal gene Expression profiles are often studied with models that treat time as a categorical variable, neglecting the dependence between time points. Here, we present ImpulseDE2, a framework for Differential Expression analysis that combines the power of the impulse model as a continuous representation of temporal responses along with a noise model tailored specifically to sequencing data. We compare the simple categorical models to ImpulseDE2 and to other continuous models based on natural cubic splines and demonstrate the utility of the continuous approach for studying Differential Expression in time course sequencing experiments. A unique feature of ImpulseDE2 is the ability to distinguish permanently from transiently up- or down-regulated genes. Using an in vitro differentiation dataset, we demonstrate that this gene classification scheme can be used to highlight distinct transcriptional programs that are associated with different phases of the differentiation process.
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impulse model based Differential Expression analysis of time course sequencing data
bioRxiv, 2017Co-Authors: David S Fischer, Fabian J Theis, Nir YosefAbstract:The global gene Expression trajectories of cellular systems in response to developmental or environmental stimuli often follow the prototypic single-pulse or state-transition patterns which can be modeled with the impulse model. Here we combine the continuous impulse Expression model with a sequencing data noise model in ImpulseDE2, a Differential Expression algorithm for time course sequencing experiments such as RNA-seq, ATAC-seq and ChIP-seq. We show that ImpulseDE2 outperforms currently used Differential Expression algorithms on data sets with sufficiently many sampled time points. ImpulseDE2 is capable of differentiating between transiently and monotonously changing Expression trajectories. This classification separates genes which are responsible for the initial and final cell state phenotypes from genes which drive or are driven by the cell state transition and identifies down-regulation of oxidative-phosphorylation as a molecular signature which can drive human embryonic stem cell differentiation.
Mauro Delorenzi - One of the best experts on this subject based on the ideXlab platform.
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a comparison of methods for Differential Expression analysis of rna seq data
BMC Bioinformatics, 2013Co-Authors: Charlotte Soneson, Mauro DelorenziAbstract:Finding genes that are Differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for Differential Expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for Differential Expression analysis of RNA-seq data. We conducted an extensive comparison of eleven methods for Differential Expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for Differential Expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.
Mingyuan Zhou - One of the best experts on this subject based on the ideXlab platform.
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bnp seq bayesian nonparametric Differential Expression analysis of sequencing count data
Journal of the American Statistical Association, 2018Co-Authors: Siamak Zamani Dadaneh, Xiaoning Qian, Mingyuan ZhouAbstract:ABSTRACTWe perform Differential Expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (beta) negative binomial process, which takes into account different sequencing depths using sample-specific negative binomial probability (dispersion) parameters, to detect Differentially expressed genes by comparing the posterior distributions of gene-specific negative binomial dispersion (probability) parameters. These model parameters are inferred by borrowing statistical strength across both the genes and samples. Extensive experiments on both simulated and real-world RNA sequencing count data show that the proposed Differential Expression analysis algorithms clearly outperform previously proposed ones in terms of the areas under both the receiver operating characteristic and precision-recall curves. Supplementary materials for this article are avai...
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bnp seq bayesian nonparametric Differential Expression analysis of sequencing count data
arXiv: Applications, 2016Co-Authors: Siamak Zamani Dadaneh, Xiaoning Qian, Mingyuan ZhouAbstract:We perform Differential Expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad-hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (beta) negative binomial process, which takes into account different sequencing depths using sample-specific negative binomial probability (dispersion) parameters, to detect Differentially expressed genes by comparing the posterior distributions of gene-specific negative binomial dispersion (probability) parameters. These model parameters are inferred by borrowing statistical strength across both the genes and samples. Extensive experiments on both simulated and real-world RNA sequencing count data show that the proposed Differential Expression analysis algorithms clearly outperform previously proposed ones in terms of the areas under both the receiver operating characteristic and precision-recall curves.
