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 4.4.0 RC (2024-04-16 r86468)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 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.3.10.0
#>
#> 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_1.41.0
#> [3] FlowSorted.Blood.EPIC_2.9.0
#> [4] ExperimentHub_2.13.0
#> [5] AnnotationHub_3.13.0
#> [6] BiocFileCache_2.13.0
#> [7] dbplyr_2.5.0
#> [8] minfi_1.51.0
#> [9] bumphunter_1.47.0
#> [10] locfit_1.5-9.9
#> [11] iterators_1.0.14
#> [12] foreach_1.5.2
#> [13] Biostrings_2.73.0
#> [14] XVector_0.45.0
#> [15] SummarizedExperiment_1.35.0
#> [16] Biobase_2.65.0
#> [17] MatrixGenerics_1.17.0
#> [18] matrixStats_1.3.0
#> [19] GenomicRanges_1.57.0
#> [20] GenomeInfoDb_1.41.0
#> [21] IRanges_2.39.0
#> [22] S4Vectors_0.43.0
#> [23] BiocGenerics_0.51.0
#> [24] HiBED_1.3.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_1.8.8
#> [3] magrittr_2.0.3 GenomicFeatures_1.57.0
#> [5] rmarkdown_2.26 BiocIO_1.15.0
#> [7] zlibbioc_1.51.0 vctrs_0.6.5
#> [9] multtest_2.61.0 memoise_2.0.1
#> [11] Rsamtools_2.21.0 DelayedMatrixStats_1.27.0
#> [13] RCurl_1.98-1.14 askpass_1.2.0
#> [15] htmltools_0.5.8.1 S4Arrays_1.5.0
#> [17] curl_5.2.1 Rhdf5lib_1.27.0
#> [19] SparseArray_1.5.0 rhdf5_2.49.0
#> [21] sass_0.4.9 nor1mix_1.3-3
#> [23] bslib_0.7.0 plyr_1.8.9
#> [25] cachem_1.0.8 GenomicAlignments_1.41.0
#> [27] lifecycle_1.0.4 pkgconfig_2.0.3
#> [29] Matrix_1.7-0 R6_2.5.1
#> [31] fastmap_1.1.1 GenomeInfoDbData_1.2.12
#> [33] digest_0.6.35 siggenes_1.79.0
#> [35] reshape_0.8.9 AnnotationDbi_1.67.0
#> [37] RSQLite_2.3.6 base64_2.0.1
#> [39] filelock_1.0.3 fansi_1.0.6
#> [41] httr_1.4.7 abind_1.4-5
#> [43] compiler_4.4.0 beanplot_1.3.1
#> [45] rngtools_1.5.2 bit64_4.0.5
#> [47] BiocParallel_1.39.0 DBI_1.2.2
#> [49] HDF5Array_1.33.0 MASS_7.3-60.2
#> [51] openssl_2.1.2 rappdirs_0.3.3
#> [53] DelayedArray_0.31.0 rjson_0.2.21
#> [55] tools_4.4.0 glue_1.7.0
#> [57] quadprog_1.5-8 restfulr_0.0.15
#> [59] nlme_3.1-164 rhdf5filters_1.17.0
#> [61] grid_4.4.0 generics_0.1.3
#> [63] tzdb_0.4.0 preprocessCore_1.67.0
#> [65] tidyr_1.3.1 data.table_1.15.4
#> [67] hms_1.1.3 xml2_1.3.6
#> [69] utf8_1.2.4 BiocVersion_3.20.0
#> [71] pillar_1.9.0 limma_3.61.0
#> [73] genefilter_1.87.0 splines_4.4.0
#> [75] dplyr_1.1.4 lattice_0.22-6
#> [77] survival_3.6-4 rtracklayer_1.65.0
#> [79] bit_4.0.5 GEOquery_2.73.0
#> [81] annotate_1.83.0 tidyselect_1.2.1
#> [83] knitr_1.46 xfun_0.43
#> [85] scrime_1.3.5 statmod_1.5.0
#> [87] UCSC.utils_1.1.0 yaml_2.3.8
#> [89] evaluate_0.23 codetools_0.2-20
#> [91] tibble_3.2.1 BiocManager_1.30.22
#> [93] cli_3.6.2 xtable_1.8-4
#> [95] jquerylib_0.1.4 Rcpp_1.0.12
#> [97] png_0.1-8 XML_3.99-0.16.1
#> [99] readr_2.1.5 blob_1.2.4
#> [101] mclust_6.1.1 doRNG_1.8.6
#> [103] sparseMatrixStats_1.17.0 bitops_1.0-7
#> [105] illuminaio_0.47.0 purrr_1.0.2
#> [107] crayon_1.5.2 rlang_1.1.3
#> [109] KEGGREST_1.45.0
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.