Analysis of the methylation change related to aKG
Junyan Lu
Last updated: 2024-10-31
Checks: 5 1
Knit directory: RA_Tcell_omics/analysis/
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Load libraries
Global variables
Load and preprocess datasets
Load omics data
Subset
Remove probes on Y chromosomes
Subset for RA samples (not keto)
Remove pat RA121, which does not have RA
Filter for interested CpGs
To increase statistic power by reducing number of test and increase intepretability
CpGs that associated with interested genes
CpGs within known enhancers
Find GC probs in the enhancer region
Subset CpGs that in either of the two list
Final data dimension
[1] 82147 12Prepare annotation table for CpGs
Experimental conditions
aKG ns st
RA_120 1 1 1
RA_122 1 1 1
RA_123 1 1 1
RA_125 1 1 1PCA analysis
All samples
Calculate PCA
PCA plots
PC1 versus PC2
Maybe two patients, RA_122 and RA_121 has miss annotated sex?
PC3 versus PC4
All samples and and adjust for patient specific effect
Use combat to remove patient specific effect
Calculate PCA
PCA plots
PC1 versus PC2
PC3 versus PC4
PC3 and PC4 can separate aKG group from others
Identify differentially methylated CpGs related to aKG treatment
aKG versus ns
Further subset for samples in st and aKG group.
Perform differential test
Difference is not strong.
Table with P.Value < 0.01
Visualize top 9 CpGs methylation changes
aKG versus st
Further subset for samples in st and aKG group.
Perform differential test
Difference is not strong.
Table with P.Value < 0.01
Visualize top 9 CpGs methylation changes
In the above plots, the first line in the title is the
mehtylation CpG id, the second line is the gene and gene region
annotations from Epic methylation array, the third line is the
annotations from GeneEnhancer. The number in the parenthesis is the
enhancer score, the higher the more confident.
Whether aKG reverse the methylation change related to RA
Load diff CpG from RA versus control test
Using result from RA versus ns
Using result from RA versus st
Compare aKG effect with keto diet effect
Load diff CpG from RA versus control test
Non-stimulated Keto diet samples
Stimulated Keto diet samples
R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.1
[3] dplyr_1.1.4.9000 purrr_0.3.4
[5] readr_2.1.2 tidyr_1.2.0
[7] tibble_3.2.1 ggplot2_3.4.1
[9] tidyverse_1.3.2 pheatmap_1.0.12
[11] SummarizedExperiment_1.26.1 Biobase_2.56.0
[13] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
[15] IRanges_2.30.0 S4Vectors_0.34.0
[17] BiocGenerics_0.42.0 MatrixGenerics_1.8.1
[19] matrixStats_0.62.0 limma_3.52.2
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 colorspace_2.0-3 ellipsis_0.3.2
[4] rprojroot_2.0.3 XVector_0.36.0 fs_1.5.2
[7] rstudioapi_0.13 farver_2.1.1 DT_0.23
[10] ggrepel_0.9.1 bit64_4.0.5 AnnotationDbi_1.58.0
[13] fansi_1.0.6 lubridate_1.8.0 xml2_1.3.3
[16] codetools_0.2-18 splines_4.2.0 cachem_1.0.6
[19] knitr_1.39 jsonlite_1.8.3 workflowr_1.7.0
[22] broom_1.0.0 annotate_1.74.0 dbplyr_2.2.1
[25] png_0.1-7 compiler_4.2.0 httr_1.4.3
[28] backports_1.4.1 assertthat_0.2.1 Matrix_1.5-4
[31] fastmap_1.1.0 gargle_1.2.0 cli_3.6.2
[34] later_1.3.0 htmltools_0.5.4 tools_4.2.0
[37] gtable_0.3.0 glue_1.7.0 GenomeInfoDbData_1.2.8
[40] Rcpp_1.0.11 cellranger_1.1.0 jquerylib_0.1.4
[43] vctrs_0.6.5 Biostrings_2.64.0 nlme_3.1-158
[46] crosstalk_1.2.0 xfun_0.31 rvest_1.0.2
[49] lifecycle_1.0.4 XML_3.99-0.10 googlesheets4_1.0.0
[52] edgeR_3.38.1 zlibbioc_1.42.0 scales_1.2.0
[55] hms_1.1.1 promises_1.2.0.1 parallel_4.2.0
[58] RColorBrewer_1.1-3 yaml_2.3.5 memoise_2.0.1
[61] sass_0.4.2 stringi_1.7.8 RSQLite_2.2.15
[64] highr_0.9 genefilter_1.78.0 BiocParallel_1.30.4
[67] rlang_1.1.3 pkgconfig_2.0.3 bitops_1.0-7
[70] evaluate_0.15 lattice_0.20-45 htmlwidgets_1.5.4
[73] labeling_0.4.2 cowplot_1.1.1 bit_4.0.4
[76] tidyselect_1.2.1 magrittr_2.0.3 R6_2.5.1
[79] generics_0.1.3 DelayedArray_0.22.0 DBI_1.1.3
[82] mgcv_1.8-40 pillar_1.9.0 haven_2.5.0
[85] withr_3.0.0 survival_3.4-0 KEGGREST_1.36.3
[88] RCurl_1.98-1.7 modelr_0.1.8 crayon_1.5.2
[91] utf8_1.2.4 tzdb_0.3.0 rmarkdown_2.14
[94] locfit_1.5-9.6 grid_4.2.0 readxl_1.4.0
[97] sva_3.44.0 blob_1.2.3 git2r_0.30.1
[100] reprex_2.0.1 digest_0.6.30 xtable_1.8-4
[103] httpuv_1.6.6 munsell_0.5.0 bslib_0.4.1