Sample Metadata Inference
4 May 2023
SeSAMe implements inference of sex, age, ethnicity. These are valuable information for checking the integrity of the experiment and detecting sample swaps.
Sex, XCI
Sex is inferred based on our curated X-linked probes and Y chromosome probes excluding pseudo-autosomal regions and XCI escapes.
Human:
sdf = sesameDataGet('EPIC.1.SigDF')
inferSex(sdf)## [1] "MALE"inferSexKaryotypes(sdf)## [1] "XaY"Mouse:
sdf = sesameDataGet("MM285.1.SigDF")
inferSex(sdf)## [1] Male
## Levels: Female MaleEthnicity
Ethnicity is inferred using a random forest model trained based on
both the built-in SNPs (rs probes) and channel-switching
Type-I probes.
sdf = sesameDataGet('EPIC.1.SigDF')
inferEthnicity(sdf)## [1] "WHITE"Age & Epigenetic Clock
SeSAMe provides age regression through multiple previously established models, e.g., the well-known Horvath 353 model (Horvath 2013) which returns the chronological age in the number of years. Here is an example:
betas <- sesameDataGet('HM450.1.TCGA.PAAD')$betas
model <- sesameAnno_get("Anno/HM450/Clock_Horvath353.rds")
predictAge(betas, model)And MM285 mouse array data using a set of 347 CpGs (see Zhou et al. 2022) The function returns the age in the number of months. We recommend using SeSAMe preprocessed data as input to the function. Here’s an example:
library(SummarizedExperiment)
betas <- assay(sesameDataGet("MM285.10.SE.tissue"))[,1]
model <- sesameAnno_get("Anno/MM285/Clock_Zhou347.rds")
predictAge(betas, model)This indicates that this mouse is approximately 1.41 months old. The function looks for overlapping probes and estimates age using the corresponding clock models. Other available epigenetic clocks are
| RDS Key | Platform | N | Reference (PMID) |
|---|---|---|---|
| Anno/HM450/Clock_Horvath353.rds | 353 | HM450/EPIC | Horvath 2013 (24138928) |
| Anno/HM450/Clock_Hannum.rds | 71 | HM450 | Hannum 2013 (23177740) |
| Anno/HM450/Clock_SkinBlood.rds | 391 | HM450/EPIC | Horath 2018 (30048243) |
| Anno/EPIC/Clock_PhenoAge.rds | 514 | HM450/EPIC | Levine 2018 (29676998) |
| Anno/MM285/Clock_Zhou347.rds | 347 | MM285 | Zhou 2022 |
Copy Number
Cell Count Deconvolution
SeSAMe estimates leukocyte fraction using a two-component model.This function works for samples whose targeted cell-of-origin is not related to white blood cells.
betas.tissue <- sesameDataGet('HM450.1.TCGA.PAAD')$betas
estimateLeukocyte(betas.tissue)## [1] 0.2007592Genomic Privacy
The goal of data sanitization is to modifiy IDAT files in place, so they can be released to public domain without privacy leak. This will be achieved by deIdentification.
Let’s take DNA methylation data from the HM450 platform for example.
tmp = tempdir()
res_grn = sesameAnno_download("Test/3999492009_R01C01_Grn.idat", dest_dir=tmp)
res_red = sesameAnno_download("Test/3999492009_R01C01_Red.idat", dest_dir=tmp)De-identify by Masking
This first method of deIdentification masks SNP probe intensity mean by zero. As a consequence, the allele frequency will be 0.5.
deIdentify(res_grn$dest_file, sprintf("%s/deidentified_Grn.idat", tmp))
deIdentify(res_red$dest_file, sprintf("%s/deidentified_Red.idat", tmp))
betas1 = getBetas(readIDATpair(sprintf("%s/Test/3999492009_R01C01", tmp)))
betas2 = getBetas(readIDATpair(sprintf("%s/deidentified", tmp)))
head(betas1[grep('rs',names(betas1))])
head(betas2[grep('rs',names(betas2))])Note that before deIdentify, the rs values will all be different. After deIdentify, the rs values will all be masked at an intensity of 0.5.
De-identify by Scrambling
This second method of deIdentification will scramble the intensities using a secret key to help formalize a random number. Therefore, randomize needs to be set to TRUE.
my_secret <- 13412084
set.seed(my_secret)
deIdentify(res_grn$dest_file,
sprintf("%s/deidentified_Grn.idat", tmp), randomize=TRUE)
my_secret <- 13412084
set.seed(my_secret)
deIdentify(res_red$dest_file,
sprintf("%s/deidentified_Red.idat", tmp), randomize=TRUE)
betas1 = getBetas(readIDATpair(sprintf("%s/Test/3999492009_R01C01", tmp)))
betas2 = getBetas(readIDATpair(sprintf("%s/deidentified", tmp)))
head(betas1[grep('rs',names(betas1))])
head(betas2[grep('rs',names(betas2))]) Note that the rs values are scrambled after deIdentify.
Re-identify
To restore order of the deIdentified intensities, one can re-identify IDATs. The reIdentify function can thus restore the scrambled SNP intensities.
my_secret <- 13412084
set.seed(my_secret)
reIdentify(sprintf("%s/deidentified_Grn.idat", tmp),
sprintf("%s/reidentified_Grn.idat", tmp))
my_secret <- 13412084
set.seed(my_secret)
reIdentify(sprintf("%s/deidentified_Red.idat", tmp),
sprintf("%s/reidentified_Red.idat", tmp))
betas1 = getBetas(readIDATpair(sprintf("%s/Test/3999492009_R01C01", tmp)))
betas2 = getBetas(readIDATpair(sprintf("%s/reidentified", tmp)))
head(betas1[grep('rs',names(betas1))])
head(betas2[grep('rs',names(betas2))]) Note that reIdentify restored the values. Subsequently, they are the same as betas1.
Session Info
sessionInfo()## R version 4.3.0 (2023-04-21)
## Platform: aarch64-apple-darwin22.3.0 (64-bit)
## Running under: macOS Ventura 13.2.1
##
## Matrix products: default
## BLAS: /Users/zhouw3/.Renv/versions/4.3.0/lib/R/lib/libRblas.dylib
## LAPACK: /Users/zhouw3/.Renv/versions/4.3.0/lib/R/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] knitr_1.42 sesame_1.18.4 sesameData_1.18.0
## [4] ExperimentHub_2.8.0 AnnotationHub_3.8.0 BiocFileCache_2.8.0
## [7] dbplyr_2.3.2 BiocGenerics_0.46.0 rmarkdown_2.21
## [10] R6_2.5.1
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## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 bitops_1.0-7
## [3] rlang_1.1.1 magrittr_2.0.3
## [5] matrixStats_0.63.0 e1071_1.7-13
## [7] compiler_4.3.0 RSQLite_2.3.1
## [9] png_0.1-8 vctrs_0.6.2
## [11] reshape2_1.4.4 stringr_1.5.0
## [13] pkgconfig_2.0.3 crayon_1.5.2
## [15] fastmap_1.1.1 XVector_0.40.0
## [17] ellipsis_0.3.2 utf8_1.2.3
## [19] promises_1.2.0.1 tzdb_0.3.0
## [21] preprocessCore_1.62.1 purrr_1.0.1
## [23] bit_4.0.5 xfun_0.39
## [25] randomForest_4.7-1.1 zlibbioc_1.46.0
## [27] cachem_1.0.8 GenomeInfoDb_1.36.0
## [29] jsonlite_1.8.4 blob_1.2.4
## [31] later_1.3.1 DelayedArray_0.26.1
## [33] BiocParallel_1.34.0 interactiveDisplayBase_1.38.0
## [35] parallel_4.3.0 bslib_0.4.2
## [37] stringi_1.7.12 RColorBrewer_1.1-3
## [39] GenomicRanges_1.52.0 jquerylib_0.1.4
## [41] Rcpp_1.0.10 SummarizedExperiment_1.30.1
## [43] wheatmap_0.2.0 readr_2.1.4
## [45] IRanges_2.34.0 httpuv_1.6.9
## [47] Matrix_1.5-4 tidyselect_1.2.0
## [49] yaml_2.3.7 codetools_0.2-19
## [51] curl_5.0.0 lattice_0.21-8
## [53] tibble_3.2.1 plyr_1.8.8
## [55] Biobase_2.60.0 shiny_1.7.4
## [57] withr_2.5.0 KEGGREST_1.40.0
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## [65] MatrixGenerics_1.12.0 stats4_4.3.0
## [67] generics_0.1.3 RCurl_1.98-1.12
## [69] BiocVersion_3.17.1 S4Vectors_0.38.1
## [71] hms_1.1.3 ggplot2_3.4.2
## [73] munsell_0.5.0 scales_1.2.1
## [75] BiocStyle_2.27.2 xtable_1.8-4
## [77] class_7.3-21 glue_1.6.2
## [79] tools_4.3.0 grid_4.3.0
## [81] AnnotationDbi_1.62.1 colorspace_2.1-0
## [83] GenomeInfoDbData_1.2.10 cli_3.6.1
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## [93] htmltools_0.5.5 lifecycle_1.0.3
## [95] httr_1.4.5 mime_0.12
## [97] MASS_7.3-58.4 bit64_4.0.5