The Experts below are selected from a list of 343440 Experts worldwide ranked by ideXlab platform
Gordon K. Smyth - One of the best experts on this subject based on the ideXlab platform.
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voom precision weights unlock Linear model analysis tools for rna seq read counts
Genome Biology, 2014Co-Authors: Charity W Law, Yunshun Chen, Gordon K. Smyth, Wei ShiAbstract:New normal Linear Modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of Linear Modeling and gene set testing methods.
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limmaGUI: A graphical user interface for Linear Modeling of microarray data
Bioinformatics, 2004Co-Authors: James M. Wettenhall, Gordon K. SmythAbstract:SUMMARY: limmaGUI is a graphical user interface (GUI) based on R-Tcl/Tk for the exploration and Linear Modeling of data from two-color spotted microarray experiments, especially the assessment of differential expression in complex experiments. limmaGUI provides an interface to the statistical methods of the limma package for R, and is itself implemented as an R package. The software provides point and click access to a range of methods for background correction, graphical display, normalization, and analysis of microarray data. Arbitrarily complex microarray experiments involving multiple RNA sources can be accomodated using Linear models and contrasts. Empirical Bayes shrinkage of the gene-wise residual variances is provided to ensure stable results even when the number of arrays is small. Integrated support is provided for quantitative spot quality weights, control spots, within-array replicate spots and multiple testing. limmaGUI is available for most platforms on the which R runs including Windows, Mac and most flavors of Unix. AVAILABILITY: http://bioinf.wehi.edu.au/limmaGUI.
Charity W Law - One of the best experts on this subject based on the ideXlab platform.
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voom precision weights unlock Linear model analysis tools for rna seq read counts
Genome Biology, 2014Co-Authors: Charity W Law, Yunshun Chen, Gordon K. Smyth, Wei ShiAbstract:New normal Linear Modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of Linear Modeling and gene set testing methods.
Olaf Dimigen - One of the best experts on this subject based on the ideXlab platform.
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unfold an integrated toolbox for overlap correction non Linear Modeling and regression based eeg analysis
PeerJ, 2019Co-Authors: Benedikt V Ehinger, Olaf DimigenAbstract:: Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a Linear or non-Linear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined electroencephalogram (EEG)/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even "traditional," highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g., from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate Modeling ("regression-ERPs"), Linear deconvolution Modeling, and non-Linear Modeling with the generalized additive model into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g., Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses. It is available as open-source software at http://www.unfoldtoolbox.org.
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unfold an integrated toolbox for overlap correction non Linear Modeling and regression based eeg analysis
bioRxiv, 2018Co-Authors: Benedikt V Ehinger, Olaf DimigenAbstract:Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a Linear or nonLinear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined EEG/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even "traditional", highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g. from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate Modeling ("regression ERPs"), Linear deconvolution Modeling, and non-Linear Modeling with the generalized additive model (GAM) into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g. Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses. It is available as open-source software at http://www.unfoldtoolbox.org.
Yunshun Chen - One of the best experts on this subject based on the ideXlab platform.
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voom precision weights unlock Linear model analysis tools for rna seq read counts
Genome Biology, 2014Co-Authors: Charity W Law, Yunshun Chen, Gordon K. Smyth, Wei ShiAbstract:New normal Linear Modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of Linear Modeling and gene set testing methods.
Wei Shi - One of the best experts on this subject based on the ideXlab platform.
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voom precision weights unlock Linear model analysis tools for rna seq read counts
Genome Biology, 2014Co-Authors: Charity W Law, Yunshun Chen, Gordon K. Smyth, Wei ShiAbstract:New normal Linear Modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of Linear Modeling and gene set testing methods.
