HiBED

Ze Zhang

2023-10-26

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 version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
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#>  [1] IlluminaHumanMethylation450kmanifest_0.4.0
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#>  [3] FlowSorted.Blood.EPIC_2.6.0               
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#> [22] S4Vectors_0.40.0                          
#> [23] BiocGenerics_0.48.0                       
#> [24] HiBED_1.0.0                               
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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.