The HiBED package contains reference libraries derived from Illumina HumanMethylation450K and Illumina HumanMethylationEPIC DNA methylation microarrays (Zhang Z, Salas LA et al. 2023), consisting of 6 astrocyte, 12 endothelial, 5 GABAergic neuron, 5 glutamatergic neuron, 18 microglial, 20 oligodendrocyte, and 5 stromal samples from public resources.
The reference libraries were used to estimate proportions of 7 major brain cell types in 450K and EPIC bulk brain samples using a modified version of the algorithm constrained projection/quadratic programming described in Houseman et al. 2012.
Loading package:
Objects included:
1. HiBED_Libraries contains 4 libraries for deconvolution
We offer the function HiBED_deconvolution to estimate proportions for 7 major brain cell types, including GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells. The estimates are calculated using modified CP/QP method described in Houseman et al. 2012.
see ?HiBED_deconvolution for details
# Step 1 load and process example
library(FlowSorted.Blood.EPIC)
library(FlowSorted.DLPFC.450k)
library(minfi)
Mset<-preprocessRaw(FlowSorted.DLPFC.450k)
Examples_Betas<-getBeta(Mset)
# Step 2: use the HiBED_deconvolution function in combinatation with the
# reference libraries for brain cell deconvolution.
HiBED_result<-HiBED_deconvolution(Examples_Betas, h=2)
head(HiBED_result)
#> Endothelial Stromal Astrocyte Microglial Oligodendrocyte GABA
#> 813_N NaN NaN 0.8548534 0.7915309 5.643616 14.867764
#> 1740_N NaN NaN 0.8524800 1.1596800 3.747840 17.805161
#> 1740_G 4.2758290 2.0241710 6.3462006 19.9935161 60.030283 3.336364
#> 1228_G 2.6479470 2.1120530 4.2803944 7.2064838 78.253122 2.508475
#> 813_G 2.5763484 1.9536516 5.4130230 14.4480688 69.668908 2.738889
#> 1228_N 0.5389908 0.7110092 1.5104187 1.6272037 7.832378 14.880146
#> GLU
#> 813_N 70.812236
#> 1740_N 70.134839
#> 1740_G 4.003636
#> 1228_G 2.991525
#> 813_G 3.211111
#> 1228_N 69.869854
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] IlluminaHumanMethylation450kmanifest_0.4.0
#> [2] FlowSorted.DLPFC.450k_1.42.0
#> [3] FlowSorted.Blood.EPIC_2.10.0
#> [4] ExperimentHub_2.14.0
#> [5] AnnotationHub_3.14.0
#> [6] BiocFileCache_2.14.0
#> [7] dbplyr_2.5.0
#> [8] minfi_1.52.0
#> [9] bumphunter_1.48.0
#> [10] locfit_1.5-9.10
#> [11] iterators_1.0.14
#> [12] foreach_1.5.2
#> [13] Biostrings_2.74.0
#> [14] XVector_0.46.0
#> [15] SummarizedExperiment_1.36.0
#> [16] Biobase_2.66.0
#> [17] MatrixGenerics_1.18.0
#> [18] matrixStats_1.4.1
#> [19] GenomicRanges_1.58.0
#> [20] GenomeInfoDb_1.42.0
#> [21] IRanges_2.40.0
#> [22] S4Vectors_0.44.0
#> [23] BiocGenerics_0.52.0
#> [24] HiBED_1.4.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_1.8.9
#> [3] magrittr_2.0.3 GenomicFeatures_1.58.0
#> [5] rmarkdown_2.28 BiocIO_1.16.0
#> [7] zlibbioc_1.52.0 vctrs_0.6.5
#> [9] multtest_2.62.0 memoise_2.0.1
#> [11] Rsamtools_2.22.0 DelayedMatrixStats_1.28.0
#> [13] RCurl_1.98-1.16 askpass_1.2.1
#> [15] htmltools_0.5.8.1 S4Arrays_1.6.0
#> [17] curl_5.2.3 Rhdf5lib_1.28.0
#> [19] SparseArray_1.6.0 rhdf5_2.50.0
#> [21] sass_0.4.9 nor1mix_1.3-3
#> [23] bslib_0.8.0 plyr_1.8.9
#> [25] cachem_1.1.0 GenomicAlignments_1.42.0
#> [27] lifecycle_1.0.4 pkgconfig_2.0.3
#> [29] Matrix_1.7-1 R6_2.5.1
#> [31] fastmap_1.2.0 GenomeInfoDbData_1.2.13
#> [33] digest_0.6.37 siggenes_1.80.0
#> [35] reshape_0.8.9 AnnotationDbi_1.68.0
#> [37] RSQLite_2.3.7 base64_2.0.2
#> [39] filelock_1.0.3 fansi_1.0.6
#> [41] httr_1.4.7 abind_1.4-8
#> [43] compiler_4.4.1 beanplot_1.3.1
#> [45] rngtools_1.5.2 bit64_4.5.2
#> [47] BiocParallel_1.40.0 DBI_1.2.3
#> [49] HDF5Array_1.34.0 MASS_7.3-61
#> [51] openssl_2.2.2 rappdirs_0.3.3
#> [53] DelayedArray_0.32.0 rjson_0.2.23
#> [55] tools_4.4.1 rentrez_1.2.3
#> [57] glue_1.8.0 quadprog_1.5-8
#> [59] restfulr_0.0.15 nlme_3.1-166
#> [61] rhdf5filters_1.18.0 grid_4.4.1
#> [63] generics_0.1.3 tzdb_0.4.0
#> [65] preprocessCore_1.68.0 tidyr_1.3.1
#> [67] data.table_1.16.2 hms_1.1.3
#> [69] xml2_1.3.6 utf8_1.2.4
#> [71] BiocVersion_3.20.0 pillar_1.9.0
#> [73] limma_3.62.0 genefilter_1.88.0
#> [75] splines_4.4.1 dplyr_1.1.4
#> [77] lattice_0.22-6 survival_3.7-0
#> [79] rtracklayer_1.66.0 bit_4.5.0
#> [81] GEOquery_2.74.0 annotate_1.84.0
#> [83] tidyselect_1.2.1 knitr_1.48
#> [85] xfun_0.48 scrime_1.3.5
#> [87] statmod_1.5.0 UCSC.utils_1.2.0
#> [89] yaml_2.3.10 evaluate_1.0.1
#> [91] codetools_0.2-20 tibble_3.2.1
#> [93] BiocManager_1.30.25 cli_3.6.3
#> [95] xtable_1.8-4 jquerylib_0.1.4
#> [97] Rcpp_1.0.13 png_0.1-8
#> [99] XML_3.99-0.17 readr_2.1.5
#> [101] blob_1.2.4 mclust_6.1.1
#> [103] doRNG_1.8.6 sparseMatrixStats_1.18.0
#> [105] bitops_1.0-9 illuminaio_0.48.0
#> [107] purrr_1.0.2 crayon_1.5.3
#> [109] rlang_1.1.4 KEGGREST_1.46.0
References
Z Zhang, LA Salas et al. (2023) SHierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation. Under Review
J. Guintivano, et al. (2013). A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics, 8(3):290–302, 2013. doi: [10.4161/epi.23924] (https://dx.doi.org/10.4161/epi.23924).
Weightman Potter PG, et al. (2021) Attenuated Induction of the Unfolded Protein Response in Adult Human Primary Astrocytes in Response to Recurrent Low Glucose. Front Endocrinol (Lausanne) 2021;12:671724. doi: [10.3389/fendo.2021.671724] (https://dx.doi.org/10.3389/fendo.2021.671724).
Kozlenkov, et al. (2018) A unique role for DNA (hydroxy)methylation in epigenetic regulation of human inhibitory neurons. Sci. Adv. 2018;4:eaau6190. doi: [10.1126/sciadv.aau6190] (https://dx.doi.org/10.1126/sciadv.aau6190).
de Whitte, et al. (2022) Contribution of Age, Brain Region, Mood Disorder Pathology, and Interindividual Factors on the Methylome of Human Microglia. Biological Psychiatry March 15, 2022; 91:572–581. doi: [10.1016/j.biopsych.2021.10.020] (https://doi.org/10.1016/j.biopsych.2021.10.020).
X Lin, et al. (2018) Cell type-specific DNA methylation in neonatal cord tissue and cord blood: A 850K-reference panel and comparison of cell-types. Epigenetics. 13:941–58. doi: [10.1080/15592294.2018.1522929] (https://dx.doi.org/10.1080/15592294.2018.1522929).
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](https://dx.doi.org/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.
minfi Tools to analyze & visualize Illumina Infinium methylation arrays.