HiBED

Ze Zhang

2024-11-05

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")
  1. 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 Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-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_1.43.0              
#>  [3] FlowSorted.Blood.EPIC_2.11.0              
#>  [4] ExperimentHub_2.15.0                      
#>  [5] AnnotationHub_3.15.0                      
#>  [6] BiocFileCache_2.15.0                      
#>  [7] dbplyr_2.5.0                              
#>  [8] minfi_1.53.0                              
#>  [9] bumphunter_1.49.0                         
#> [10] locfit_1.5-9.10                           
#> [11] iterators_1.0.14                          
#> [12] foreach_1.5.2                             
#> [13] Biostrings_2.75.0                         
#> [14] XVector_0.47.0                            
#> [15] SummarizedExperiment_1.37.0               
#> [16] Biobase_2.67.0                            
#> [17] MatrixGenerics_1.19.0                     
#> [18] matrixStats_1.4.1                         
#> [19] GenomicRanges_1.59.0                      
#> [20] GenomeInfoDb_1.43.0                       
#> [21] IRanges_2.41.0                            
#> [22] S4Vectors_0.45.0                          
#> [23] BiocGenerics_0.53.1                       
#> [24] generics_0.1.3                            
#> [25] HiBED_1.5.0                               
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3        jsonlite_1.8.9           
#>   [3] magrittr_2.0.3            GenomicFeatures_1.59.0   
#>   [5] rmarkdown_2.29            BiocIO_1.17.0            
#>   [7] zlibbioc_1.53.0           vctrs_0.6.5              
#>   [9] multtest_2.63.0           memoise_2.0.1            
#>  [11] Rsamtools_2.23.0          DelayedMatrixStats_1.29.0
#>  [13] RCurl_1.98-1.16           askpass_1.2.1            
#>  [15] htmltools_0.5.8.1         S4Arrays_1.7.1           
#>  [17] curl_5.2.3                Rhdf5lib_1.29.0          
#>  [19] SparseArray_1.7.0         rhdf5_2.51.0             
#>  [21] sass_0.4.9                nor1mix_1.3-3            
#>  [23] bslib_0.8.0               plyr_1.8.9               
#>  [25] cachem_1.1.0              GenomicAlignments_1.43.0 
#>  [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_1.81.0          
#>  [35] reshape_0.8.9             AnnotationDbi_1.69.0     
#>  [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_4.5.0            beanplot_1.3.1           
#>  [45] rngtools_1.5.2            bit64_4.5.2              
#>  [47] BiocParallel_1.41.0       DBI_1.2.3                
#>  [49] HDF5Array_1.35.1          MASS_7.3-61              
#>  [51] openssl_2.2.2             rappdirs_0.3.3           
#>  [53] DelayedArray_0.33.1       rjson_0.2.23             
#>  [55] tools_4.5.0               rentrez_1.2.3            
#>  [57] glue_1.8.0                quadprog_1.5-8           
#>  [59] restfulr_0.0.15           nlme_3.1-166             
#>  [61] rhdf5filters_1.19.0       grid_4.5.0               
#>  [63] tzdb_0.4.0                preprocessCore_1.69.0    
#>  [65] tidyr_1.3.1               data.table_1.16.2        
#>  [67] hms_1.1.3                 xml2_1.3.6               
#>  [69] utf8_1.2.4                BiocVersion_3.21.1       
#>  [71] pillar_1.9.0              limma_3.63.1             
#>  [73] genefilter_1.89.0         splines_4.5.0            
#>  [75] dplyr_1.1.4               lattice_0.22-6           
#>  [77] survival_3.7-0            rtracklayer_1.67.0       
#>  [79] bit_4.5.0                 GEOquery_2.75.0          
#>  [81] annotate_1.85.0           tidyselect_1.2.1         
#>  [83] knitr_1.48                xfun_0.49                
#>  [85] scrime_1.3.5              statmod_1.5.0            
#>  [87] UCSC.utils_1.3.0          yaml_2.3.10              
#>  [89] evaluate_1.0.1            codetools_0.2-20         
#>  [91] tibble_3.2.1              BiocManager_1.30.25      
#>  [93] cli_3.6.3                 xtable_1.8-4             
#>  [95] jquerylib_0.1.4           Rcpp_1.0.13-1            
#>  [97] png_0.1-8                 XML_3.99-0.17            
#>  [99] readr_2.1.5               blob_1.2.4               
#> [101] mclust_6.1.1              doRNG_1.8.6              
#> [103] sparseMatrixStats_1.19.0  bitops_1.0-9             
#> [105] illuminaio_0.49.0         purrr_1.0.2              
#> [107] crayon_1.5.3              rlang_1.1.4              
#> [109] KEGGREST_1.47.0

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.