gbdmr is used to identify the differential methylated CpG regions for the microarray data. The method can be smoothly conducted in three steps. First, order the CpG sites based on the chromosome number and chromosome coordinates; calculate the adjacent CpG sites’ correlation in the same chromosome one by one; get the clustered CpG sites that share similar information based on the correlation and treat these clusters as candidate DMRs. Then generalized beta regression is used to model the CpG sites in the same cluster; parameter estimation is performed via the maximum likelihood function (ML). Finally, conduct a log likelihood ratio test to the interesting phenotype after adjusting for the confounders, then summarize the result and report the DMRs. The method can be used to find the risk CpG sites related to any disease or CpG sites that can be potential mediators in the causal inference.
For running the algorithm one has to have three objects:
a matrix with methylation values;
a phenotype dataset (the IDs of the two data sets need to correspond one-to-one);
specific correlation value from 0 to 1.
All the work is done by one command gbdmr and for more information use ?gbdmr in R.
library(devtools)
install_github("chengzhouwu/gbdmr")##
checking for file 'C:\Users\cwu8\AppData\Local\Temp\Rtmpo7FnaR\remotes364c42d544b\chengzhouwu-gbdmr-2c7cd35/DESCRIPTION' ...
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✔ checking for file 'C:\Users\cwu8\AppData\Local\Temp\Rtmpo7FnaR\remotes364c42d544b\chengzhouwu-gbdmr-2c7cd35/DESCRIPTION' (460ms)
##
─ preparing 'gbdmr':
## checking DESCRIPTION meta-information ...
✔ checking DESCRIPTION meta-information
## Warning: C:/Users/cwu8/AppData/Local/Temp/RtmpwvuTip/Rbuild6af442967852/gbdmr/man/gbdmr_introduction.Rd:6: unexpected UNKNOWN '\Description'
##
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##
NB: this package now depends on R (>=
NB: this package now depends on R (>= 3.5.0)
##
WARNING: Added dependency on R >= 3.5.0 because serialized objects in
## serialize/load version 3 cannot be read in older versions of R.
## File(s) containing such objects:
## 'gbdmr/data/beta.Rdata' 'gbdmr/data/phenotype.Rdata'
'gbdmr/data/beta.Rdata' 'gbdmr/data/phenotype.Rdata'
## ─ building 'gbdmr_0.1.0.tar.gz'
##
## library(gbdmr)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
library(minfi)
library(geograbi)
library(limma)
library(corrplot)
library(maxLik)An example dataset beta and phenotype. It contains 1,000 CpG sites for 506 samples. Finding regions in this data can be accomplished using commands:
load("beta.Rdata")
load("phenotype.Rdata")
DMR_summary_list = gbdmr(beta,phenotype,rho = 0.5)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following object is masked from 'package:minfi':
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## intersect, setdiff, setequal, unionstr(DMR_summary_list)## List of 5
## $ gbdmrall :'data.frame': 1000 obs. of 3 variables:
## ..$ cpg : chr [1:1000] "cg13869341" "cg24669183" "cg15560884" "cg01014490" ...
## ..$ clusterIDs : num [1:1000] 1 2 3 4 5 6 7 8 8 9 ...
## ..$ raw_p_value: num [1:1000] 0.0106 0.6414 0.4339 0.3697 0.3157 ...
## $ all_sig_cpG :'data.frame': 33 obs. of 3 variables:
## ..$ cpg : chr [1:33] "cg05151709" "cg08546707" "cg25066040" "cg16318112" ...
## ..$ clusterIDs : num [1:33] 28 28 164 222 229 247 283 283 320 326 ...
## ..$ raw_p_value: num [1:33] 5.42e-05 5.42e-05 6.43e-07 0.00 2.64e-05 ...
## $ DMR :'data.frame': 21 obs. of 3 variables:
## ..$ cpg : chr [1:21] "cg05151709" "cg08546707" "cg21139076" "cg07549208" ...
## ..$ clusterIDs : num [1:21] 28 28 283 283 414 414 498 498 498 498 ...
## ..$ raw_p_value: num [1:21] 5.42e-05 5.42e-05 6.38e-07 6.38e-07 8.27e-06 ...
## $ DMP :'data.frame': 12 obs. of 3 variables:
## ..$ cpg : chr [1:12] "cg25066040" "cg16318112" "cg00525597" "cg12469361" ...
## ..$ clusterIDs : num [1:12] 164 222 229 247 320 326 354 399 554 652 ...
## ..$ raw_p_value: num [1:12] 6.43e-07 0.00 2.64e-05 3.42e-05 3.67e-05 ...
## $ positive_cpg: chr [1:33] "cg05151709" "cg08546707" "cg25066040" "cg16318112" ...DMR_summary_list[[1]]## cpg clusterIDs raw_p_value
## 1 cg13869341 1 1.064633e-02
## 2 cg24669183 2 6.413510e-01
## 3 cg15560884 3 4.339386e-01
## 4 cg01014490 4 3.697491e-01
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## 800 cg07624812 665 2.574165e-01
## 801 cg04910752 666 5.548927e-02
## 802 cg13246026 667 2.727748e-01
## 803 cg21396988 668 8.180519e-01
## 804 cg06802641 669 7.627142e-01
## 805 cg03402404 670 2.531884e-01
## 806 cg10065209 671 1.878976e-01
## 807 cg08621441 672 2.299848e-02
## 808 cg18386618 673 7.214226e-01
## 809 cg06395167 674 8.335688e-01
## 810 cg16174225 674 8.335688e-01
## 811 cg00002593 675 5.574915e-01
## 812 cg11808871 676 4.446758e-01
## 813 cg14911892 677 2.516835e-01
## 814 cg26814650 678 9.831483e-02
## 815 cg08160862 679 6.911186e-01
## 816 cg23643813 680 1.166516e-01
## 817 cg26351554 681 1.843188e-01
## 818 cg24399111 682 6.161815e-01
## 819 cg18024631 683 2.549345e-01
## 820 cg07313814 684 1.103209e-01
## 821 cg22475339 685 6.260046e-01
## 822 cg11339519 686 2.734775e-02
## 823 cg00542041 687 6.768503e-01
## 824 cg16575694 688 7.031491e-01
## 825 cg06646682 689 2.091199e-03
## 826 cg12072393 690 2.380452e-01
## 827 cg13494355 691 1.973510e-01
## 828 cg25203539 692 8.132868e-01
## 829 cg19898526 693 4.288062e-01
## 830 cg06649390 694 8.268218e-01
## 831 cg17748182 695 7.457506e-01
## 832 cg03465880 696 5.633817e-02
## 833 cg15462203 697 1.520850e-02
## 834 cg10556264 698 3.654789e-01
## 835 cg22709966 699 5.832098e-02
## 836 cg15976416 700 2.080430e-01
## 837 cg17838754 701 1.359557e-01
## 838 cg23596199 702 1.341888e-03
## 839 cg13932473 703 5.794148e-01
## 840 cg04119843 704 6.824767e-02
## 841 cg09236098 705 3.273168e-01
## 842 cg12625874 706 7.539595e-01
## 843 cg10164137 707 2.067628e-03
## 844 cg17873420 708 2.852480e-01
## 845 cg21118819 709 3.961976e-01
## 846 cg11414742 710 6.728198e-02
## 847 cg14270725 711 1.545670e-01
## 848 cg15386434 712 2.458871e-02
## 849 cg14793596 712 2.458871e-02
## 850 cg07284273 713 5.612699e-01
## 851 cg25063151 714 7.055496e-01
## 852 cg19436320 715 8.497794e-01
## 853 cg00040588 715 8.497794e-01
## 854 cg27399052 716 9.488028e-01
## 855 cg12478066 716 9.488028e-01
## 856 cg23036947 716 9.488028e-01
## 857 cg17132079 716 9.488028e-01
## 858 cg01198994 717 5.916806e-01
## 859 cg23973885 718 4.605405e-01
## 860 cg13393580 719 9.247314e-01
## 861 cg13354934 720 9.608237e-01
## 862 cg14375163 721 6.737502e-01
## 863 cg20426768 722 3.734699e-01
## 864 cg24153574 723 6.939215e-01
## 865 cg21165029 724 1.434165e-01
## 866 cg03400623 724 1.434165e-01
## 867 cg22109675 724 1.434165e-01
## 868 cg15492552 724 1.434165e-01
## 869 cg05387269 725 8.845270e-01
## 870 cg14992101 726 7.875016e-01
## 871 cg09864992 727 2.498120e-01
## 872 cg25685675 728 5.667855e-01
## 873 cg14094639 729 2.557654e-01
## 874 cg01508988 730 1.448307e-01
## 875 cg07395004 731 1.623515e-01
## 876 cg19871494 732 4.062026e-01
## 877 cg15604233 733 2.559994e-01
## 878 cg11518257 734 1.869523e-01
## 879 cg19823490 735 2.540888e-02
## 880 cg15617543 736 6.090872e-02
## 881 cg02144516 737 3.403506e-01
## 882 cg26865478 738 1.256054e-01
## 883 cg16361266 739 1.159364e-01
## 884 cg12033178 740 1.481788e-01
## 885 cg04880874 741 7.749657e-01
## 886 cg15588177 742 5.602611e-01
## 887 cg22232737 743 2.086807e-02
## 888 cg01006034 744 5.378935e-01
## 889 cg23621766 745 4.652348e-01
## 890 cg06903451 746 7.621012e-01
## 891 cg23209302 747 2.612951e-04
## 892 cg01399319 748 1.310950e-01
## 893 cg17014728 749 5.928722e-02
## 894 cg22389936 750 3.611720e-02
## 895 cg04481810 751 8.404662e-01
## 896 cg19384072 752 1.122040e-01
## 897 cg24837149 753 2.368594e-02
## 898 cg16278124 754 3.135294e-01
## 899 cg06817090 755 9.822828e-01
## 900 cg16573386 756 6.649700e-01
## 901 cg17397377 757 7.419745e-01
## 902 cg07191003 758 6.446980e-02
## 903 cg08470185 759 7.738021e-01
## 904 cg15130241 760 6.753444e-01
## 905 cg25842896 761 2.070579e-01
## 906 cg00173405 762 1.329641e-01
## 907 cg03943717 763 8.533711e-01
## 908 cg23437836 764 4.073063e-01
## 909 cg00968800 765 6.617829e-01
## 910 cg09064061 766 7.983287e-01
## 911 cg24399028 767 8.364817e-01
## 912 cg14795268 768 7.300667e-01
## 913 cg21439539 769 5.838414e-01
## 914 cg20275133 770 5.181686e-03
## 915 cg16728516 771 6.023783e-01
## 916 cg05048680 771 6.023783e-01
## 917 cg25769755 771 6.023783e-01
## 918 cg11993086 772 6.827753e-01
## 919 cg05595602 773 2.457936e-01
## 920 cg25397779 774 9.547643e-01
## 921 cg12525514 775 1.142524e-02
## 922 cg11222665 776 1.302980e-01
## 923 cg25939543 777 2.016425e-03
## 924 cg16661858 778 2.435240e-01
## 925 cg00009834 779 1.011871e-06
## 926 cg27295412 780 3.553127e-01
## 927 cg00060374 781 8.775605e-01
## 928 cg17507949 782 6.602174e-02
## 929 cg23894219 783 1.000000e+00
## 930 cg15633699 783 1.000000e+00
## 931 cg10380643 783 1.000000e+00
## 932 cg15081886 783 1.000000e+00
## 933 cg15823872 784 6.289149e-01
## 934 cg24555071 785 3.778047e-01
## 935 cg06711078 786 7.149081e-01
## 936 cg12286462 787 4.683533e-02
## 937 cg01721822 788 7.204166e-01
## 938 cg21285198 789 9.387008e-02
## 939 cg00305774 789 9.387008e-02
## 940 cg22517356 789 9.387008e-02
## 941 cg09368716 790 4.105097e-07
## 942 cg12208770 790 4.105097e-07
## 943 cg26863172 790 4.105097e-07
## 944 cg17142722 791 9.958164e-01
## 945 cg04897188 792 9.823111e-03
## 946 cg04865726 793 1.912277e-03
## 947 cg12407057 793 1.912277e-03
## 948 cg19270739 793 1.912277e-03
## 949 cg21837069 793 1.912277e-03
## 950 cg17491622 794 2.144834e-01
## 951 cg06444575 794 2.144834e-01
## 952 cg00702126 794 2.144834e-01
## 953 cg14667273 794 2.144834e-01
## 954 cg27391934 794 2.144834e-01
## 955 cg08589721 795 7.669086e-01
## 956 cg14271665 796 3.609043e-02
## 957 cg02896318 797 6.740275e-02
## 958 cg25234611 798 8.973379e-01
## 959 cg09668627 799 7.751540e-01
## 960 cg08161163 800 2.608068e-01
## 961 cg26798702 800 2.608068e-01
## 962 cg09843049 801 7.718506e-01
## 963 cg13897675 802 5.882994e-01
## 964 cg09860601 803 3.128164e-02
## 965 cg19639479 804 4.938106e-03
## 966 cg03116557 805 6.318947e-03
## 967 cg13570957 805 6.318947e-03
## 968 cg03538326 806 7.344489e-05
## 969 cg26795248 807 5.285028e-01
## 970 cg25592215 808 5.879852e-01
## 971 cg08426733 809 5.642950e-02
## 972 cg08270205 810 6.098874e-01
## 973 cg15099879 811 1.186886e-01
## 974 cg06786804 812 2.981744e-01
## 975 cg24492273 813 9.052431e-01
## 976 cg21001441 814 8.294284e-01
## 977 cg00528453 815 4.217983e-01
## 978 cg27383362 816 1.446930e-01
## 979 cg26651722 817 5.567023e-01
## 980 cg19682639 818 7.109150e-02
## 981 cg13388025 819 1.669867e-01
## 982 cg26226110 820 8.573231e-02
## 983 cg07280242 821 2.898762e-01
## 984 cg13798621 821 2.898762e-01
## 985 cg04107003 822 1.097445e-01
## 986 cg17282836 823 2.464766e-01
## 987 cg10522845 824 4.576880e-01
## 988 cg04981940 825 5.382501e-01
## 989 cg17821649 826 4.969917e-01
## 990 cg00391668 827 5.039001e-02
## 991 cg03742705 828 2.534423e-01
## 992 cg15875062 829 5.319267e-01
## 993 cg10228629 830 8.194878e-04
## 994 cg06377929 831 1.704660e-04
## 995 cg10240778 832 1.257148e-02
## 996 cg04286729 833 1.830558e-01
## 997 cg12909536 834 5.870642e-01
## 998 cg12439834 835 3.939019e-06
## 999 cg05818185 836 7.836874e-02
## 1000 cg01768480 837 2.737069e-02# gbdmr overall result: including CpG, clusterID and p-value
# extract the significant CpG names
DMR_summary_list[[5]]## [1] "cg05151709" "cg08546707" "cg25066040" "cg16318112" "cg00525597"
## [6] "cg12469361" "cg21139076" "cg07549208" "cg15207999" "cg16060740"
## [11] "cg10126324" "cg04311266" "cg21778193" "cg15489799" "cg22335801"
## [16] "cg14661234" "cg23394673" "cg23166857" "cg09586191" "cg23867494"
## [21] "cg21815220" "cg17084044" "cg16252905" "cg09315878" "cg01075305"
## [26] "cg03058163" "cg10245314" "cg04232698" "cg00009834" "cg09368716"
## [31] "cg12208770" "cg26863172" "cg12439834"You can annotation the CpGs and extract the gemonic information. Then do the enrichment analysis: GO, KEGG, etc.
# link the positive CpGs to the gene and explore the genomic region and island region
data(list="IlluminaHumanMethylation450kanno.ilmn12.hg19")
data(Locations)
data(Other)
annotation <- cbind(as.data.frame(Locations), as.data.frame(Other))
common <- intersect(DMR_summary_list[[5]], rownames(annotation))
annotation <- annotation[match(common, rownames(annotation)),]
# now you will have the genomic region information, gene information, etc.
annotation$UCSC_RefGene_Group## [1] "" ""
## [3] "TSS1500;TSS1500" "Body"
## [5] "Body" "Body"
## [7] "" ""
## [9] "Body" "Body"
## [11] "TSS1500" ""
## [13] "" ""
## [15] "Body" "Body"
## [17] "Body" "1stExon"
## [19] "TSS200" "TSS1500"
## [21] "TSS1500" "TSS1500"
## [23] "TSS1500" "3'UTR;3'UTR"
## [25] "Body;3'UTR" "Body;Body"
## [27] "Body;Body;Body;Body;Body" "1stExon;5'UTR;TSS200"
## [29] "" "1stExon"
## [31] "1stExon" "1stExon"
## [33] "Body"annotation$UCSC_RefGene_Name## [1] ""
## [2] ""
## [3] "HES4;HES4"
## [4] "AGRN"
## [5] "AGRN"
## [6] "AGRN"
## [7] ""
## [8] ""
## [9] "C1orf159"
## [10] "C1orf159"
## [11] "C1orf159"
## [12] ""
## [13] ""
## [14] ""
## [15] "TNFRSF4"
## [16] "TNFRSF4"
## [17] "TNFRSF4"
## [18] "TNFRSF4"
## [19] "TNFRSF4"
## [20] "TNFRSF4"
## [21] "TNFRSF4"
## [22] "TNFRSF4"
## [23] "TNFRSF4"
## [24] "SDF4;SDF4"
## [25] "SDF4;SDF4"
## [26] "SDF4;SDF4"
## [27] "UBE2J2;UBE2J2;LOC100128842;UBE2J2;UBE2J2"
## [28] "GLTPD1;GLTPD1;CPSF3L"
## [29] ""
## [30] "TMEM88B"
## [31] "TMEM88B"
## [32] "TMEM88B"
## [33] "ATAD3B"For example, if you have data recording dependent and indenpendent variable information
Y <- ASTHMA_18
X <- BMI_CLUSTER.x
M <- new_beta[rownames(new_beta) == “cg09705784”,]
M <- as.numeric(M[1,])
model.0 <- glm(Y~X)
summary(model.0)
model.M <- glm(M ~ X)
summary(model.M)
model.Y <- glm(Y ~ X + M)
summary(model.Y)
library(mediation)
results <- mediate(model.M, model.Y, treat=“X”, mediator = “M”, boot = T, sims=500)
summary(results)
Y <- ASTHMA_18
X1 <- POLLEN_18
X2 <- new_beta[rownames(new_beta) == “cg22024479”,] X2 <- as.numeric(X2[1,])
X3 <- new_variable$SEX_0.x
model1 <- glm(Y ~ X1 + X2 + X3 + X1X2 + X1X3 + X2*X3)
summary(model1)
model2 <- glm(Y ~ X1 + X2)
summary(model2)
model3 <- glm(Y ~ X1 + X2 + X3 )
summary(model3)
coef <- model2$coefficients[2]
se <- summary(model2)$coefficients[2,2]
OR <- exp(round(coef,2))
Epigenetic factors can be used as a candidate biomarker, and gbdmr can be potentially used to make the feature selection in the disease prediction.