In SalasLab/HiTIMED: HiTIMED

knitr::opts_chunk$set(cache = FALSE, warning = FALSE, message = FALSE, 
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Loading package:

library(HiTIMED)

The HiTIMED package contains reference libraries derived from Illumina HumanMethylation450K DNA methylation microarrays (Zhang Z, Salas LA et al. 2022).

The reference libraries were used to estimate proportions of 17 cell types (tumor, epithelial, endothelial, stromal, basophil, eosinophil, neutrophil, dendritic cell, monocyte, B naïve, B memory, CD4T naïve, CD4T memory, CD8T naïve, CD8T memory, T regulatory, and natural killer cells) in 3 major tumor microenvironment components (tumor, immune, angiogenic) for 20 types of carcinomas using a modified version of the algorithm constrained projection/quadratic programming described in Houseman et al. 2012.

Objects included:

HiTIMED_deconvolution function for tumor microenvironment deconvolution:

We offer the function HiTIMED_deconvolution to estimate proportions for 17 cell types (tumor, epithelial, endothelial, stromal, basophil, eosinophil, neutrophil, dendritic cell, monocyte, B naïve, B memory, CD4T naïve, CD4T memory, CD8T naïve, CD8T memory, T regulatory, and natural killer cells) in 3 major tumor microenvironment components (tumor, immune, angiogenic) for 20 types of carcinomas using a modified version of the algorithm constrained projection/quadratic programming described in Houseman et al. 2012. see ?HiTIMED_deconvolution for details

# Step 1: Load example data
library(ExperimentHub)
Example_Beta<-query(ExperimentHub(), "HiTIMED")[["EH8092"]]


# Step 2: Run HiTIMED and show results
HiTIMED_result<-HiTIMED_deconvolution(Example_Beta,"COAD",6,"tumor")
head(HiTIMED_result)

sessionInfo()

References

Z Zhang, LA Salas et al. (2023) HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data. Journal of Tranlsational Medicine, 8;20(1):516. doi: [10.1186/s12967-022-03736-6] (https://dx.doi.org/10.1186/s12967-022-03736-6).

Zheng X et al. (2017). Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies. Genome Biol. 2017;18(1):17. doi: [10.1186/s13059-016-1143-5] (https://doi.org/10.1186/s13059-016-1143-5).

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] (https://dx.doi.org/10.1186/s13059-018-1448-7).

DC Koestler et al. (2016). Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)}. BMC bioinformatics. doi: [10.1186/s12859-016-0943-7] (https://dx.doi.org/10.1186/s12859-016-0943-7)

LA Salas et al. (2022). Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nature Communications 13(1):761. doi:10.1038/s41467-021-27864-7.

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.

SalasLab/HiTIMED documentation built on Oct. 21, 2023, 10:12 a.m.