Project description:DNA methylation microarrays have been extensively used for understanding cell type composition in complex tissue samples. Here we expand on existing libraries for reference-based deconvolution of blood DNA methylation data assayed using the Illumina HumanMethylationEPIC array to include 12 different leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells naïve and memory, CD4+ and CD8+ naïve and memory cells, natural killers, and T regulatory cells). Application of the IDOL algorithm for identifying optimal libraries for the deconvolution of blood-derived DNA methylation data led to to a library consisting of 1200 CpGs. The accuracy of deconvolution estimates obtained using our IDOL-optimized library was evaluated using artificial mixtures with known cellular composition and in samples were both whole-blood DNA methylation data and FACS information were available. We further showcase potential applications of our expanded library using publicly available cancer, aging, and autoimmune disease data sets.
Project description:DNA methylation microarrays have been extensively used for understanding cell type composition in complex tissue samples. Here we expand on existing libraries for reference-based deconvolution of blood DNA methylation data assayed using the Illumina HumanMethylationEPIC array to include 12 different leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells naïve and memory, CD4+ and CD8+ naïve and memory cells, natural killers, and T regulatory cells). Application of the IDOL algorithm for identifying optimal libraries for the deconvolution of blood-derived DNA methylation data led to to a library consisting of 1200 CpGs. The accuracy of deconvolution estimates obtained using our IDOL-optimized library was evaluated using artificial mixtures with known cellular composition and in samples were both whole-blood DNA methylation data and FACS information were available. We further showcase potential applications of our expanded library using publicly available cancer, aging, and autoimmune disease data sets.
Project description:DNA methylation microarrays have been extensively used for understanding cell type composition in complex tissue samples. Here we expand on existing libraries for reference-based deconvolution of blood DNA methylation data assayed using the Illumina HumanMethylationEPIC array to include 12 different leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells naïve and memory, CD4+ and CD8+ naïve and memory cells, natural killers, and T regulatory cells). Application of the IDOL algorithm for identifying optimal libraries for the deconvolution of blood-derived DNA methylation data led to to a library consisting of 1200 CpGs. The accuracy of deconvolution estimates obtained using our IDOL-optimized library was evaluated using artificial mixtures with known cellular composition and in samples were both whole-blood DNA methylation data and FACS information were available. We further showcase potential applications of our expanded library using publicly available cancer, aging, and autoimmune disease data sets.
Project description:DNA methylation microarrays have been extensively used for understanding cell type composition in complex tissue samples. Here we expand on existing libraries for reference-based deconvolution of blood DNA methylation data assayed using the Illumina HumanMethylationEPIC array to include 12 different leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells naïve and memory, CD4+ and CD8+ naïve and memory cells, natural killers, and T regulatory cells). Application of the IDOL algorithm for identifying optimal libraries for the deconvolution of blood-derived DNA methylation data led to a library consisting of 1200 CpGs. The accuracy of deconvolution estimates obtained using our IDOL-optimized library was evaluated using artificial mixtures with known cellular composition and in samples were both whole-blood DNA methylation data and FACS information were available. We further showcase potential applications of our expanded library using publicly available cancer, aging, and autoimmune disease data sets.
Project description:7 male OUD patients and 7 male health controls with demographic and clinical data matched were enrolled in this study. rRNA removed library and small RNA library were constructed using peripheral blood RNA. RNA-seq was used to investigate the peripheral transcriptomic changes between the two groups.