FlowSorted.Blood.EPIC: FlowSorted.Blood.EPIC

In immunomethylomics/FlowSorted.Blood.EPIC: Illumina EPIC data on immunomagnetic sorted peripheral adult blood cells

FlowSorted.Blood.EPIC

Description

Illumina Human Methylation data from EPIC on immunomagnetic sorted adult blood cell populations. The FlowSorted.Blood.EPIC package contains Illumina HumanMethylationEPIC (“EPIC”)) DNA methylation microarray data from the immunomethylomics group (Salas et al. 2018), consisting of 37 magnetic sorted blood cell references and 12 samples, formatted as an RGChannelSet object for integration and normalization using most of the existing Bioconductor packages.

This package contains data similar to the FlowSorted.Blood.450k package consisting of data from peripheral blood samples generated from adult men and women. However, when using the newer EPIC microarray minfi estimates of cell type composition using the FlowSorted.Blood.450k package are less precise compared to actual cell counts. Hence, this package consists of appropriate data for deconvolution of adult blood samples used in for example EWAS relying in the newer EPIC technology.

Researchers may find this package useful as these samples represent different cellular populations ( T lymphocytes (CD4+ and CD8+), B cells (CD19+), monocytes (CD14+), NK cells (CD56+) and Neutrophils of cell sorted blood generated with high purity estimates. As a test of accuracy 12 experimental mixtures were reconstructed using fixed amounts of DNA from purified cells. We offer the function estimateCellCounts2 a modification of the popular estimatesCellCounts function in minfi. This function allows estimating cellular composition in users' whole blood Illumina EPIC samples using a modified version of the algorithm constrained projection/quadratic programming described in Houseman et al. 2012. For a slightly more accurate estimations we also offered an IDOL optimized CpG selection for cell deconvolution as the object IDOLOptimizedCpGs, and the IDOLOptimizedCpGs450klegacy object for legacy 450K datasets. See the objects help for details.

Usage

FlowSorted.Blood.EPIC
#See ?estimateCellCounts2 for cell deconvolution guidelines

Format

A class: RGChannelSet, dimensions: 1051815 49

Value

RGChannelSet 49 samples

Source

The FlowSorted.Blood.EPIC object is based in samples assayed by Brock Christensen and colleagues; Salas et al. 2018. GSE110554

See Also

References

1. LA Salas et al. (2018). An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biology 19, 64. doi: 10.1186/s13059-018-1448-7.

2. DC Koestler et al. (2016). Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC bioinformatics. 17, 120. doi:10.1186/s12859-016-0943-7.

3. EA Houseman et al. (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86. doi:10.1186/1471-2105-13-86.

4. minfi package, tools for analyzing DNA methylation microarrays

Examples

# FlowSorted.Blood.EPIC<-
# libraryDataGet('FlowSorted.Blood.EPIC')
# FlowSorted.Blood.EPIC
# table(FlowSorted.Blood.EPIC$CellType)

immunomethylomics/FlowSorted.Blood.EPIC documentation built on May 24, 2023, 2:22 a.m.