Index Catalog // ScholarsArchive@OSU

HousemanReferenceFreeDeconvolutionAdditionalFile3.zip

Creator:: Houseman, E. Andres, Kile, Molly L., Christiani, David C., Ince, Tan A., Kelsey, Karl T., and Marsit, Carmen J.
Abstract:: 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. Results: We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying...

HousemanReferenceFreeDeconvolutionAdditionalFile2.zip

Creator:: Houseman, E. Andres, Kile, Molly L., Christiani, David C., Ince, Tan A., Kelsey, Karl T., and Marsit, Carmen J.
Abstract:: 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. Results: We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying...

HousemanReferenceFreeDeconvolutionAdditionalFile1.pdf

Creator:: Houseman, E. Andres, Kile, Molly L., Christiani, David C., Ince, Tan A., Kelsey, Karl T., and Marsit, Carmen J.
Abstract:: 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. Results: We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying...

HousemanReferenceFreeDeconvolution.pdf

Creator:: Houseman, E. Andres, Kile, Molly L., Christiani, David C., Ince, Tan A., Kelsey, Karl T., and Marsit, Carmen J.
Abstract:: 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. Results: We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying...

HousemanEugenePHHSCell-CompositionEffects_SupplementaryMaterials.pdf

Creator:: Houseman, E. Andres, Kelsey, Karl T., Wiencke, John K., and Marsit, Carmen J.
Abstract:: BACKGROUND: The impact of cell-composition effects in analysis of DNA methylation data is now widely appreciated. With the availability of a reference data set consisting of DNA methylation measurements on isolated cell types, it is possible to impute cell proportions and adjust for them, but there is increasing interest in...

HousemanEugenePHHSCell-CompositionEffects.pdf

Creator:: Houseman, E. Andres, Kelsey, Karl T., Wiencke, John K., and Marsit, Carmen J.
Abstract:: BACKGROUND: The impact of cell-composition effects in analysis of DNA methylation data is now widely appreciated. With the availability of a reference data set consisting of DNA methylation measurements on isolated cell types, it is possible to impute cell proportions and adjust for them, but there is increasing interest in...

HousemanEugenePHHSQuantitativeReconstructionLeukocyte_AdditionalFiles.zip

Creator:: Accomando, William P., Wiencke, John K., Houseman, E. Andres, Nelson, Heather H., and Kelsey, Karl T.
Abstract:: BACKGROUND: Cell lineage-specific DNA methylation patterns distinguish normal human leukocyte subsets and can be used to detect and quantify these subsets in peripheral blood. We have developed an approach that uses DNA methylation to simultaneously quantify multiple leukocyte subsets, enabling investigation of immune modulations in virtually any blood sample including...

HousemanEugenePHHSQuantitativeReconstructionLeukocyte.pdf

Creator:: Accomando, William P., Wiencke, John K., Houseman, E. Andres, Nelson, Heather H., and Kelsey, Karl T.
Abstract:: BACKGROUND: Cell lineage-specific DNA methylation patterns distinguish normal human leukocyte subsets and can be used to detect and quantify these subsets in peripheral blood. We have developed an approach that uses DNA methylation to simultaneously quantify multiple leukocyte subsets, enabling investigation of immune modulations in virtually any blood sample including...

Reference-free deconvolution of DNA methylation data and mediation by cell composition effects

Creator:: Houseman, E. Andres, Kile, Molly L., Christiani, David C., Ince, Tan A., Kelsey, Karl T., and Marsit, Carmen J.
Abstract:: 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. Results: We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying...
Resource Type:: Article

Understanding the Role of the Immune System in the Development of Cancer: New Opportunities for Population-Based Research

Creator:: Michaud, Dominique S., Houseman, E. Andres, Marsit, Carmen J., Nelson, Heather H., Wiencke, John K., and Kelsey, Karl T.
Abstract:: Understanding the precise role of the immune system in cancer has been hindered by the complexity of the immune response and challenges in measuring immune cell types in health and disease in the context of large epidemiologic studies. In this review, we present the rationale to study immunity in cancer...
Resource Type:: Article

Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective

Creator:: Houseman, E. Andres, Kelsey, Karl T., Wiencke, John K., and Marsit, Carmen J.
Abstract:: BACKGROUND: The impact of cell-composition effects in analysis of DNA methylation data is now widely appreciated. With the availability of a reference data set consisting of DNA methylation measurements on isolated cell types, it is possible to impute cell proportions and adjust for them, but there is increasing interest in...
Resource Type:: Article

Quantitative reconstruction of leukocyte subsets using DNA methylation

Creator:: Accomando, William P., Wiencke, John K., Houseman, E. Andres, Nelson, Heather H., and Kelsey, Karl T.
Abstract:: BACKGROUND: Cell lineage-specific DNA methylation patterns distinguish normal human leukocyte subsets and can be used to detect and quantify these subsets in peripheral blood. We have developed an approach that uses DNA methylation to simultaneously quantify multiple leukocyte subsets, enabling investigation of immune modulations in virtually any blood sample including...
Resource Type:: Article

Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis

Creator:: Koestler, Devin C., Christensen, Brock C., Karagas, Margaret R., Marsit, Carmen J., Langevin, Scott M., Kelsey, Karl T., Wiencke, John K., and Houseman, E. Andres
Abstract:: The potential influence of underlying differences in relative leukocyte distributions in studies involving blood-based profiling of DNA methylation is well recognized and has prompted development of a set of statistical methods for inferring changes in the distribution of white blood cells using DNA methylation signatures. However, the extent to which...
Resource Type:: Article

Review of processing and analysis methods for DNA methylation array data

Creator:: Wilhelm-Benartzi, Charlotte S., Koestler, Devin C., Karagas, Margaret R., Flanagan, James M., Christensen, Brock C., Kelsey, Karl T., Marsit, Carmen J., Houseman, E. Andres, and Brown, Robert
Abstract:: The promise of epigenome-wide association studies (EWAS) and cancer specific somatic changes in improving our understanding of cancer coupled with the decreasing cost and increasing coverage of DNA methylation microarrays, has brought about a surge in the use of these technologies. Here, we aim to provide both a review of...
Resource Type:: Article

Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures

Creator:: Koestler, Devin C., Christensen, Brock C., Marsit, Carmen J., Kelsey, Karl T., and Houseman, E. Andres
Abstract:: DNA methylation is a well-recognized epigenetic mechanism that has been the subject of a growing body of literature typically focused on the identification and study of profiles of DNA methylation and their association with human diseases and exposures. In recent years, a number of unsupervised clustering algorithms, both parametric and...
Resource Type:: Article

ScholarsArchive@OSU

HousemanReferenceFreeDeconvolutionAdditionalFile3.zip

HousemanReferenceFreeDeconvolutionAdditionalFile2.zip

HousemanReferenceFreeDeconvolutionAdditionalFile1.pdf

HousemanReferenceFreeDeconvolution.pdf

HousemanEugenePHHSCell-CompositionEffects_SupplementaryMaterials.pdf

HousemanEugenePHHSCell-CompositionEffects.pdf

HousemanEugenePHHSQuantitativeReconstructionLeukocyte_AdditionalFiles.zip

HousemanEugenePHHSQuantitativeReconstructionLeukocyte.pdf

Reference-free deconvolution of DNA methylation data and mediation by cell composition effects

Understanding the Role of the Immune System in the Development of Cancer: New Opportunities for Population-Based Research

Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective

Quantitative reconstruction of leukocyte subsets using DNA methylation

Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis

Review of processing and analysis methods for DNA methylation array data

Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures

Limit your search

Academic Affiliation

Creator

Date

Decade

Language

Peer Reviewed

Resource Type

Rights Statement

Search Constraints

Search Results

Limit your search