The Experts below are selected from a list of 229380 Experts worldwide ranked by ideXlab platform
Carmen J Marsit - One of the best experts on this subject based on the ideXlab platform.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
BMC Bioinformatics, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:Background Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying Cell types.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
BMC Bioinformatics, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying Cell types. We propose a simple method for reference-free deconvolution that provides both proportions of putative Cell types defined by their underlying methylomes, the number of these constituent Cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of Cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return Cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent Cell types. Our methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by Cell Composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
bioRxiv, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:We propose a simple method for reference-free deconvolution that provides both proportions of putative Cell types defined by their underlying methylomes, the number of these constituent Cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of Cells. We have demonstrated these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return Cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent Cell types. Thus the methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by Cell Composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.
Karl T Kelsey - One of the best experts on this subject based on the ideXlab platform.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
BMC Bioinformatics, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:Background Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying Cell types.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
BMC Bioinformatics, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying Cell types. We propose a simple method for reference-free deconvolution that provides both proportions of putative Cell types defined by their underlying methylomes, the number of these constituent Cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of Cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return Cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent Cell types. Our methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by Cell Composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.
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improving Cell mixture deconvolution by identifying optimal dna methylation libraries idol
BMC Bioinformatics, 2016Co-Authors: Devin C Koestler, Meaghan J Jones, Joseph Usset, Brock C Christensen, Rondi A Butler, Michael S Kobor, John K Wiencke, Karl T KelseyAbstract:Confounding due to Cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the Cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of Cell-specific methylation markers to find libraries that optimize the accuracy of Cell fraction estimates obtained from Cell mixture deconvolution. Application of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of Cell Composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune Cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of Cell Composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all Cell-specific R 2>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all Cell-specific R 2>0.90 and R M S E<4.00 %. In simulation studies, adjustments for Cell Composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets. Despite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for Cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of Cell mixture deconvolution.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
bioRxiv, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:We propose a simple method for reference-free deconvolution that provides both proportions of putative Cell types defined by their underlying methylomes, the number of these constituent Cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of Cells. We have demonstrated these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return Cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent Cell types. Thus the methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by Cell Composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.
Andres E Houseman - One of the best experts on this subject based on the ideXlab platform.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
BMC Bioinformatics, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:Background Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying Cell types.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
BMC Bioinformatics, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying Cell types. We propose a simple method for reference-free deconvolution that provides both proportions of putative Cell types defined by their underlying methylomes, the number of these constituent Cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of Cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return Cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent Cell types. Our methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by Cell Composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.
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reference free deconvolution of dna methylation data and mediation by Cell Composition effects
bioRxiv, 2016Co-Authors: Andres E Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J MarsitAbstract:We propose a simple method for reference-free deconvolution that provides both proportions of putative Cell types defined by their underlying methylomes, the number of these constituent Cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of Cells. We have demonstrated these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return Cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent Cell types. Thus the methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by Cell Composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.
Eran Halperin - One of the best experts on this subject based on the ideXlab platform.
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publisher correction accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
Nature Communications, 2020Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Kristina M Garske, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:Author(s): Jew, Brandon; Alvarez, Marcus; Rahmani, Elior; Miao, Zong; Ko, Arthur; Garske, Kristina M; Sul, Jae Hoon; Pietilainen, Kirsi H; Pajukanta, Paivi; Halperin, Eran | Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
Nature Communications, 2020Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Kristina M Garske, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:We present Bisque, a tool for estimating Cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-Cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve deComposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between Cell type proportions and measured phenotypes across abundant and rare Cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.
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accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
Nature Communications, 2020Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Kristina M Garske, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:We present Bisque, a tool for estimating Cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-Cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve deComposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between Cell type proportions and measured phenotypes across abundant and rare Cell types. We further propose an additional mode of operation that merely requires a set of known marker genes. Traditional methods for determining Cell type Composition lack scalability, while single-Cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure Cellular heterogeneity in bulk expression through robust integration of single-Cell information.
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accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
bioRxiv, 2019Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:Abstract We present Bisque, a tool for estimating Cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-Cell RNA-seq (scRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve deComposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and single-nucleus RNA-seq (snRNA-seq) data, Bisque was able to replicate previously reported associations between Cell type proportions and measured phenotypes across abundant and rare Cell types. Bisque requires a single-Cell reference dataset that reflects physiological Cell type Composition and can further leverage datasets that includes both bulk and single Cell measurements over the same samples for improved accuracy. We further propose an additional mode of operation that merely requires a set of known marker genes. Bisque is available as an R package at: https://github.com/cozygene/bisque.
Kirsi H Pietilainen - One of the best experts on this subject based on the ideXlab platform.
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publisher correction accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
Nature Communications, 2020Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Kristina M Garske, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:Author(s): Jew, Brandon; Alvarez, Marcus; Rahmani, Elior; Miao, Zong; Ko, Arthur; Garske, Kristina M; Sul, Jae Hoon; Pietilainen, Kirsi H; Pajukanta, Paivi; Halperin, Eran | Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
Nature Communications, 2020Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Kristina M Garske, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:We present Bisque, a tool for estimating Cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-Cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve deComposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between Cell type proportions and measured phenotypes across abundant and rare Cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.
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accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
Nature Communications, 2020Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Kristina M Garske, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:We present Bisque, a tool for estimating Cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-Cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve deComposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between Cell type proportions and measured phenotypes across abundant and rare Cell types. We further propose an additional mode of operation that merely requires a set of known marker genes. Traditional methods for determining Cell type Composition lack scalability, while single-Cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure Cellular heterogeneity in bulk expression through robust integration of single-Cell information.
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accurate estimation of Cell Composition in bulk expression through robust integration of single Cell information
bioRxiv, 2019Co-Authors: Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Jae Hoon Sul, Kirsi H Pietilainen, Paivi Pajukanta, Eran HalperinAbstract:Abstract We present Bisque, a tool for estimating Cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-Cell RNA-seq (scRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve deComposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and single-nucleus RNA-seq (snRNA-seq) data, Bisque was able to replicate previously reported associations between Cell type proportions and measured phenotypes across abundant and rare Cell types. Bisque requires a single-Cell reference dataset that reflects physiological Cell type Composition and can further leverage datasets that includes both bulk and single Cell measurements over the same samples for improved accuracy. We further propose an additional mode of operation that merely requires a set of known marker genes. Bisque is available as an R package at: https://github.com/cozygene/bisque.