Last updated: 2024-04-08

Checks: 5 1

Knit directory: RA_Tcell_omics/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20221110)

The command set.seed(20221110) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: no version control

Tracking code development and connecting the code version to the results is critical for reproducibility. To start using Git, open the Terminal and type git init in your project directory.

This project is not being versioned with Git. To obtain the full reproducibility benefits of using workflowr, please see ?wflow_start.

Load libraries

Global variables

Load and preprocess datasets

Load omics data

Get unique symbol

Remove probes on sex chromosomes

Remove CpGs not associated with known genes

Save table for interested methylation probes

Correlation between average methylation level and phenotype


    Two Sample t-test

data:  meanMethylation by group
t = 1.3468, df = 21, p-value = 0.1924
alternative hypothesis: true difference in means between group Control and group RA is not equal to 0
95 percent confidence interval:
 -0.003516213  0.016440056
sample estimates:
mean in group Control      mean in group RA 
            0.5630603             0.5565984

On the overall methylation level, there’s no strong trend that RA samples have higher methylation.

PCA analysis

Calculate PCA

PCA plots

PC1 versus PC2

PC2 versus PC3

Associate PCs with disease

# A tibble: 23 × 3
# Groups:   pc [23]
   pc     estimate p.value
   <chr>     <dbl>   <dbl>
 1 PC4    1.59e+ 1  0.0133
 2 PC9    1.09e+ 1  0.0468
 3 PC2    3.01e+ 1  0.0802
 4 PC12  -8.90e+ 0  0.0852
 5 PC10   7.02e+ 0  0.202 
 6 PC23  -6.00e-14  0.319 
 7 PC5    5.95e+ 0  0.342 
 8 PC16  -4.39e+ 0  0.371 
 9 PC6   -5.09e+ 0  0.406 
10 PC13  -3.57e+ 0  0.495 
# ℹ 13 more rows

The first three principal components can separate RA with control samples, to some degree.

Differentially methylated CpGs

Prepare data

Process methylation dataset

[1] 572165     23

Differential methylation using limma

Add mean difference of beta values

Save the full table as excel file

diffMeth_table_all_withChrX.xlsx

Number of hypermethylation and hypomethylation

Visualize results

Heatmap of associated CpGs

quartz_off_screen 
                3

meth_heatmap.png

Top 1000 associated probes

Visualize top 20 associations

The top hit seems interesting: https://pubmed.ncbi.nlm.nih.gov/18759932/

Number of associated CpGs per gene

Visualize selected probes in genes

Look at specified gene list

Prepare data

Probes on sex chromosomes are not removed, as some genes in the list are from chrX and chrY

Fix some names

Check if the names are present

Genes not detected

[1] "PHD"     "ATP5F1D" "ATP5ME"  "NDUF?"

Add all DGKs and NDUFs

Visualize results

Visualize top 20 associations

Number of associated CpGs per gene

Correlating methylation signature with protein and phosphoprotein expression

Correlate methylation with protein abundance

Because of small sample size, only focus on cis-regulations

No strong associations

Significant associations with p < 0.01

Plot

Methylations associations with DNMT1_S143 phosphorylation

DMST1_S143 phosphorylation showed association with phenotype and may reflect the activity of DMNT1 protein

[1] 572171      5

How many are known to be associated with the phenotype?


FALSE  TRUE 
14581   130

Table of CpGs that associated with DNMT1_S143 phosphorylation and also RA phenotype

Among above CpGs, any of them also associate with protein expression?

# A tibble: 0 × 10
# ℹ 10 variables: methID <chr>, logFC <dbl>, AveExpr <dbl>, t <dbl>,
#   P.Value <dbl>, adj.P.Val <dbl>, B <dbl>, symbol <chr>, site <chr>,
#   ifGroup <lgl>

Unfortunately no.


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] BiocParallel_1.30.3         MultiAssayExperiment_1.22.0
 [3] forcats_0.5.1               stringr_1.4.1              
 [5] dplyr_1.1.4.9000            purrr_0.3.4                
 [7] readr_2.1.2                 tidyr_1.2.0                
 [9] tibble_3.2.1                ggplot2_3.4.1              
[11] tidyverse_1.3.2             pheatmap_1.0.12            
[13] SummarizedExperiment_1.26.1 Biobase_2.56.0             
[15] GenomicRanges_1.48.0        GenomeInfoDb_1.32.2        
[17] IRanges_2.30.0              S4Vectors_0.34.0           
[19] BiocGenerics_0.42.0         MatrixGenerics_1.8.1       
[21] matrixStats_0.62.0          limma_3.52.2               

loaded via a namespace (and not attached):
  [1] ggbeeswarm_0.6.0       googledrive_2.0.0      colorspace_2.0-3      
  [4] ellipsis_0.3.2         rprojroot_2.0.3        XVector_0.36.0        
  [7] fs_1.5.2               rstudioapi_0.13        farver_2.1.1          
 [10] DT_0.23                ggrepel_0.9.1          bit64_4.0.5           
 [13] AnnotationDbi_1.58.0   fansi_1.0.6            lubridate_1.8.0       
 [16] xml2_1.3.3             codetools_0.2-18       splines_4.2.0         
 [19] cachem_1.0.6           knitr_1.39             jsonlite_1.8.3        
 [22] workflowr_1.7.0        broom_1.0.0            annotate_1.74.0       
 [25] dbplyr_2.2.1           png_0.1-7              compiler_4.2.0        
 [28] httr_1.4.3             backports_1.4.1        assertthat_0.2.1      
 [31] Matrix_1.5-4           fastmap_1.1.0          gargle_1.2.0          
 [34] cli_3.6.2              later_1.3.0            htmltools_0.5.4       
 [37] tools_4.2.0            gtable_0.3.0           glue_1.7.0            
 [40] GenomeInfoDbData_1.2.8 Rcpp_1.0.9             cellranger_1.1.0      
 [43] jquerylib_0.1.4        vctrs_0.6.5            Biostrings_2.64.0     
 [46] writexl_1.4.0          nlme_3.1-158           crosstalk_1.2.0       
 [49] xfun_0.31              rvest_1.0.2            lifecycle_1.0.4       
 [52] XML_3.99-0.10          googlesheets4_1.0.0    zlibbioc_1.42.0       
 [55] scales_1.2.0           ragg_1.2.2             hms_1.1.1             
 [58] promises_1.2.0.1       parallel_4.2.0         RColorBrewer_1.1-3    
 [61] yaml_2.3.5             memoise_2.0.1          sass_0.4.2            
 [64] stringi_1.7.8          RSQLite_2.2.15         highr_0.9             
 [67] genefilter_1.78.0      systemfonts_1.0.4      rlang_1.1.3           
 [70] pkgconfig_2.0.3        bitops_1.0-7           evaluate_0.15         
 [73] lattice_0.20-45        htmlwidgets_1.5.4      labeling_0.4.2        
 [76] cowplot_1.1.1          bit_4.0.4              tidyselect_1.2.1      
 [79] magrittr_2.0.3         R6_2.5.1               generics_0.1.3        
 [82] DelayedArray_0.22.0    DBI_1.1.3              mgcv_1.8-40           
 [85] pillar_1.9.0           haven_2.5.0            withr_3.0.0           
 [88] survival_3.4-0         KEGGREST_1.36.3        RCurl_1.98-1.7        
 [91] modelr_0.1.8           crayon_1.5.2           utf8_1.2.4            
 [94] tzdb_0.3.0             rmarkdown_2.14         grid_4.2.0            
 [97] readxl_1.4.0           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           textshaping_0.3.6      munsell_0.5.0         
[106] beeswarm_0.4.0         vipor_0.4.5            bslib_0.4.1