FlowSorted.BloodExtended.EPIC: FlowSorted.Blood.EPIC

In immunomethylomics/FlowSorted.BloodExtended.EPIC: Illumina EPIC data on extended immunomagnetic sorted blood populations

FlowSorted.Blood.EPIC

Description

The FlowSorted.BloodExtended.EPIC package contains data derived from Illumina HumanMethylationEPIC (“EPIC”)) DNA methylation microarray data from the immunomethylomics group (Salas et al. 2021), consisting of 56 blood cell references and 12 mixed blood samples, formatted as an RGChannelSet object for integration and normalization using most of the existing Bioconductor packages.

The FlowSorted.BloodExtended.EPIC dataset includes information from neutrophils (Neu, n=6), eosinophils (Eos, n=4), basophils (Bas, n=6), monocytes (Mono, n=5), B naive cells (Bnv, n=4), B memory cells (Bmem, n=6), T-helper CD4+ naive cells, (CD4nv, n=5), T-helper CD4+ memory cells (CD4mem, n=4), T regulatory cells (Treg, n=3), T-cytotoxic CD8+ naive cells (CD8nv, n=5),T-cytotoxic memory CD8+ cells (CD8mem, n=4), and natural killer cells (NK, n=4), plus 12 DNA artificial mixtures, labeled as MIX in this dataset.

Researchers may find this package useful as these samples represent different cellular populations, including eosinophils, basophils, monocytes, B naive cells, B memory cells, T-helper CD4+ naive cells, T-helper CD4+ memory cells, T regulatory cells, T-cytotoxic CD8+ naive cells, T-cytotoxic memory CD8+ cells and natural killer cells, 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.

This package contains data similar to the FlowSorted.Blood.EPIC package consisting of data from peripheral blood samples generated from adult men and women. However, more granular information is useful for some specific applications. You can use any algorithm with the data. We recommend the function estimateCellCounts2 in (FlowSorted.Blood.EPIC) for cell estimation using the option IDOL. 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. If you use IDOL for more accurate estimations we include an IDOL optimized CpG selection for cell deconvolution as the object IDOLOptimizedCpGsBloodExtended, and the IDOLOptimizedCpGsBloodExtended450k object for legacy 450K datasets. See the objects help for details.

Usage

FlowSorted.BloodExtended.EPIC

Format

A class: RGChannelSet, dimensions: 1008711 68

Source

The FlowSorted.Blood.EPIC object is based in samples assayed by Brock Christensen and colleagues; Salas et al. 2021. GSE167998

See Also

References

1. LA Salas et al. (2021) Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. (Under review)

2. 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.

3. 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.

4. 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.

5. minfi package, tools for analyzing DNA methylation microarrays

Examples

# Explore the reference library
library(ExperimentHub)
hub <- ExperimentHub()
query(hub, "FlowSorted.BloodExtended.EPIC")
FlowSorted.BloodExtended.EPIC <- hub[["*****"]]
FlowSorted.BloodExtended.EPIC

immunomethylomics/FlowSorted.BloodExtended.EPIC documentation built on Jan. 29, 2024, 8:43 p.m.