This script uses the Bioconductor package MEAT to calculate bAge from muscle DNAm data
Load packages
library(tidyverse)
library(survival)
library(SummarizedExperiment)
library(MEAT)load('../GOTO_Data/Processing/GOTO_methData-unfiltered.Rdata')
methData <- methData_unfiltered[ , methData_unfiltered$tissue == 'muscle']
methData## class: SummarizedExperiment
## dim: 865859 160
## metadata(0):
## assays(1): beta
## rownames(865859): cg14817997 cg26928153 ... cg07587934 cg16855331
## rowData names(4): cpg chr start end
## colnames(160): 203527980082_R01C01 203527980082_R02C01 ...
## 203550300091_R01C01 203550300091_R02C01
## colData names(19): DNA_labnr IOP2_ID ... Basename smokemethData_clean <- clean_beta(SE = methData,
version = "MEAT2.0")## -------------------Step 1-------------------------------## Reducing your beta-matrix to the 18747 CpGs used to calibrate methylation profiles in MEAT2.0## Your beta-matrix contains 18747 of the 18747 CpGs needed to calibrate methylation profiles.## -------------------Step 2-------------------------------## Checking for missing values in the beta-matrix.## -------------------Step 3-------------------------------## Checking for the presence of 0 and 1.## Your beta-matrix contains 0 0 values and 0 1 values.Calibration
methData_calib <- BMIQcalibration(SE = methData_clean,
version = "MEAT2.0")## [1] Inf
## [1] 0.001738881
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## [1] 0.002669617
## [1] 0.002586373
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## ii= 160Estimate age
methData_age <- epiage_estimation(
SE = methData_calib,
age_col_name = "age",
version = "MEAT2.0"
)## function (x) .Primitive("dim")save(methData_age,
file="../GOTO_Data/Clocks/Clock_Out/MEAT.Rdata")sessionInfo()## R version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Rocky Linux 8.10 (Green Obsidian)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.15.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] MEAT_1.10.0 survival_3.5-5
## [3] RPMM_1.25 cluster_2.1.4
## [5] impute_1.72.3 SummarizedExperiment_1.24.0
## [7] Biobase_2.58.0 GenomicRanges_1.46.1
## [9] GenomeInfoDb_1.34.9 IRanges_2.32.0
## [11] S4Vectors_0.36.2 BiocGenerics_0.44.0
## [13] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [15] dnaMethyAge_0.1.0 forcats_0.5.2
## [17] stringr_1.5.0 dplyr_1.1.3
## [19] purrr_0.3.4 readr_2.1.2
## [21] tidyr_1.2.1 tibble_3.2.1
## [23] ggplot2_3.4.3 tidyverse_1.3.2
## [25] rmarkdown_2.16
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.3 tidyselect_1.2.0
## [3] RSQLite_2.2.17 AnnotationDbi_1.56.2
## [5] grid_4.2.2 BiocParallel_1.32.6
## [7] munsell_0.5.0 codetools_0.2-19
## [9] preprocessCore_1.60.2 nleqslv_3.3.4
## [11] withr_2.5.0 colorspace_2.1-0
## [13] filelock_1.0.2 highr_0.10
## [15] knitr_1.43 rstudioapi_0.14
## [17] GenomeInfoDbData_1.2.9 bit64_4.0.5
## [19] rhdf5_2.42.1 vctrs_0.6.3
## [21] generics_0.1.3 xfun_0.39
## [23] timechange_0.2.0 BiocFileCache_2.2.1
## [25] R6_2.5.1 illuminaio_0.40.0
## [27] locfit_1.5-9.8 bitops_1.0-7
## [29] rhdf5filters_1.10.1 cachem_1.0.8
## [31] reshape_0.8.9 DelayedArray_0.24.0
## [33] assertthat_0.2.1 BiocIO_1.8.0
## [35] scales_1.2.1 vroom_1.5.7
## [37] googlesheets4_1.0.1 gtable_0.3.3
## [39] affy_1.76.0 methylumi_2.40.1
## [41] rlang_1.1.1 genefilter_1.76.0
## [43] splines_4.2.2 rtracklayer_1.54.0
## [45] gargle_1.5.0 GEOquery_2.62.2
## [47] broom_1.0.1 BiocManager_1.30.21
## [49] yaml_2.3.7 modelr_0.1.9
## [51] GenomicFeatures_1.46.5 backports_1.4.1
## [53] tools_4.2.2 nor1mix_1.3-0
## [55] affyio_1.68.0 ellipsis_0.3.2
## [57] lumi_2.46.0 jquerylib_0.1.4
## [59] RColorBrewer_1.1-3 siggenes_1.68.0
## [61] dynamicTreeCut_1.63-1 Rcpp_1.0.10
## [63] plyr_1.8.8 sparseMatrixStats_1.10.0
## [65] ROC_1.70.0 progress_1.2.2
## [67] zlibbioc_1.44.0 RCurl_1.98-1.12
## [69] prettyunits_1.1.1 openssl_2.0.6
## [71] bumphunter_1.36.0 haven_2.5.1
## [73] fs_1.6.2 magrittr_2.0.3
## [75] data.table_1.14.8 reprex_2.0.2
## [77] googledrive_2.0.0 wateRmelon_2.0.0
## [79] hms_1.1.2 evaluate_0.21
## [81] xtable_1.8-4 XML_3.99-0.14
## [83] mclust_6.0.0 readxl_1.4.1
## [85] shape_1.4.6 compiler_4.2.2
## [87] biomaRt_2.50.3 minfi_1.40.0
## [89] KernSmooth_2.23-21 crayon_1.5.2
## [91] htmltools_0.5.5 mgcv_1.8-42
## [93] tzdb_0.4.0 lubridate_1.9.2
## [95] DBI_1.1.3 dbplyr_2.2.1
## [97] MASS_7.3-60 rappdirs_0.3.3
## [99] Matrix_1.5-4.1 cli_3.6.1
## [101] quadprog_1.5-8 parallel_4.2.2
## [103] pkgconfig_2.0.3 GenomicAlignments_1.30.0
## [105] xml2_1.3.4 foreach_1.5.2
## [107] annotate_1.72.0 bslib_0.5.0
## [109] rngtools_1.5.2 multtest_2.50.0
## [111] beanplot_1.3.1 XVector_0.34.0
## [113] rvest_1.0.3 doRNG_1.8.6
## [115] scrime_1.3.5 digest_0.6.31
## [117] Biostrings_2.62.0 base64_2.0.1
## [119] cellranger_1.1.0 DelayedMatrixStats_1.16.0
## [121] restfulr_0.0.15 curl_5.0.1
## [123] Rsamtools_2.10.0 rjson_0.2.21
## [125] lifecycle_1.0.3 nlme_3.1-162
## [127] jsonlite_1.8.5 Rhdf5lib_1.20.0
## [129] askpass_1.1 limma_3.54.2
## [131] fansi_1.0.4 pillar_1.9.0
## [133] lattice_0.21-8 KEGGREST_1.34.0
## [135] fastmap_1.1.1 httr_1.4.6
## [137] glue_1.6.2 png_0.1-8
## [139] iterators_1.0.14 glmnet_4.1-7
## [141] bit_4.0.5 stringi_1.7.12
## [143] sass_0.4.6 HDF5Array_1.22.1
## [145] blob_1.2.4 memoise_2.0.1Clear
rm(list=ls())