Author: Ze Zhang
Date: 2026-05-05

# HiBED

#### Ze Zhang

#### 2026-05-05

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:

    library(HiBED)

Objects included:
1. HiBED_Libraries contains 4 libraries for deconvolution

    data("HiBED_Libraries")

2. HiBED_deconvolution function for brain cell 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 4.6.0 RC (2026-04-17 r89917)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
    #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 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
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    #>
    #> 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_1.48.0
    #>  [3] FlowSorted.Blood.EPIC_2.16.0
    #>  [4] ExperimentHub_3.2.0
    #>  [5] AnnotationHub_4.2.0
    #>  [6] BiocFileCache_3.2.0
    #>  [7] dbplyr_2.5.2
    #>  [8] minfi_1.58.0
    #>  [9] bumphunter_1.54.0
    #> [10] locfit_1.5-9.12
    #> [11] iterators_1.0.14
    #> [12] foreach_1.5.2
    #> [13] Biostrings_2.80.0
    #> [14] XVector_0.52.0
    #> [15] SummarizedExperiment_1.42.0
    #> [16] Biobase_2.72.0
    #> [17] MatrixGenerics_1.24.0
    #> [18] matrixStats_1.5.0
    #> [19] GenomicRanges_1.64.0
    #> [20] Seqinfo_1.2.0
    #> [21] IRanges_2.46.0
    #> [22] S4Vectors_0.50.0
    #> [23] BiocGenerics_0.58.0
    #> [24] generics_0.1.4
    #> [25] HiBED_1.10.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3        jsonlite_2.0.0
    #>   [3] magrittr_2.0.5            GenomicFeatures_1.64.0
    #>   [5] rmarkdown_2.31            BiocIO_1.22.0
    #>   [7] vctrs_0.7.3               multtest_2.68.0
    #>   [9] memoise_2.0.1             Rsamtools_2.28.0
    #>  [11] DelayedMatrixStats_1.34.0 RCurl_1.98-1.18
    #>  [13] askpass_1.2.1             htmltools_0.5.9
    #>  [15] S4Arrays_1.12.0           curl_7.1.0
    #>  [17] Rhdf5lib_2.0.0            SparseArray_1.12.2
    #>  [19] rhdf5_2.56.0              sass_0.4.10
    #>  [21] nor1mix_1.3-3             bslib_0.10.0
    #>  [23] plyr_1.8.9                httr2_1.2.2
    #>  [25] cachem_1.1.0              GenomicAlignments_1.48.0
    #>  [27] lifecycle_1.0.5           pkgconfig_2.0.3
    #>  [29] Matrix_1.7-5              R6_2.6.1
    #>  [31] fastmap_1.2.0             digest_0.6.39
    #>  [33] siggenes_1.86.0           reshape_0.8.10
    #>  [35] AnnotationDbi_1.74.0      RSQLite_2.4.6
    #>  [37] base64_2.0.2              filelock_1.0.3
    #>  [39] httr_1.4.8                abind_1.4-8
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    #>  [43] rngtools_1.5.2            bit64_4.8.0
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    #>  [49] openssl_2.4.0             rappdirs_0.3.4
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    #>  [53] tools_4.6.0               otel_0.2.0
    #>  [55] rentrez_1.2.4             glue_1.8.1
    #>  [57] quadprog_1.5-8            h5mread_1.4.0
    #>  [59] restfulr_0.0.16           nlme_3.1-169
    #>  [61] rhdf5filters_1.24.0       grid_4.6.0
    #>  [63] tzdb_0.5.0                preprocessCore_1.74.0
    #>  [65] tidyr_1.3.2               data.table_1.18.2.1
    #>  [67] hms_1.1.4                 xml2_1.5.2
    #>  [69] BiocVersion_3.23.1        pillar_1.11.1
    #>  [71] limma_3.68.1              genefilter_1.94.0
    #>  [73] splines_4.6.0             dplyr_1.2.1
    #>  [75] lattice_0.22-9            survival_3.8-6
    #>  [77] rtracklayer_1.72.0        bit_4.6.0
    #>  [79] GEOquery_2.80.0           annotate_1.90.0
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    #>  [83] xfun_0.57                 scrime_1.3.7
    #>  [85] statmod_1.5.1             yaml_2.3.12
    #>  [87] evaluate_1.0.5            codetools_0.2-20
    #>  [89] cigarillo_1.2.0           tibble_3.3.1
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    #> [103] bitops_1.0-9              illuminaio_0.54.0
    #> [105] purrr_1.2.2               crayon_1.5.3
    #> [107] rlang_1.2.0               KEGGREST_1.52.0

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.