Last updated: 2024-05-21

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Knit directory: RA_Tcell_omics/analysis/

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Load libraries

Global variables

Load and preprocess datasets

Load omics data

Only keep probes on X chromose

Get unique symbol

Remove CpGs not associated with any function

Correlation between average methylation level and phenotype


    Two Sample t-test

data:  meanMethylation by group
t = -0.21264, df = 21, p-value = 0.8337
alternative hypothesis: true difference in means between group Control and group RA is not equal to 0
95 percent confidence interval:
 -0.03569470  0.02907226
sample estimates:
mean in group Control      mean in group RA 
            0.5530622             0.5563734

Without Male 


    Two Sample t-test

data:  meanMethylation by group
t = 0.79875, df = 16, p-value = 0.4361
alternative hypothesis: true difference in means between group Control and group RA is not equal to 0
95 percent confidence interval:
 -0.007774737  0.017175766
sample estimates:
mean in group Control      mean in group RA 
            0.5750633             0.5703628

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       9.08  0.0146
 2 PC8      -5.29  0.0369
 3 PC7       4.18  0.127 
 4 PC2      10.0   0.133 
 5 PC17     -2.45  0.195 
 6 PC16     -2.30  0.233 
 7 PC14      1.76  0.391 
 8 PC20      1.24  0.468 
 9 PC13     -1.52  0.468 
10 PC1      25.6   0.531 
# ℹ 13 more rows

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

PCA analysis without males

Calculate PCA

PCA plots

PC1 versus PC2

PC2 versus PC3

Associate PCs with disease

# A tibble: 18 × 3
# Groups:   pc [18]
   pc     estimate p.value
   <chr>     <dbl>   <dbl>
 1 PC3   -1.56e+ 1 0.00999
 2 PC12  -5.43e+ 0 0.0669 
 3 PC5    7.13e+ 0 0.107  
 4 PC9    3.54e+ 0 0.302  
 5 PC18   1.85e-14 0.349  
 6 PC7   -3.54e+ 0 0.351  
 7 PC1    7.61e+ 0 0.463  
 8 PC10   2.45e+ 0 0.469  
 9 PC16  -1.82e+ 0 0.501  
10 PC14   1.66e+ 0 0.569  
11 PC11   1.81e+ 0 0.580  
12 PC6   -2.12e+ 0 0.600  
13 PC13   1.37e+ 0 0.654  
14 PC15   7.70e- 1 0.781  
15 PC17   7.11e- 1 0.781  
16 PC4   -1.27e+ 0 0.831  
17 PC8   -6.53e- 1 0.854  
18 PC2    4.50e- 1 0.958

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

Detect differentially methylated CpGs only in Male samples

Prepare data

Process methylation dataset

[1] 13294    18

Differential methylation using limma

Add mean difference of beta values

Save the full table as excel file

diffMeth_table_onlyChrX.xlsx

Number of hypermethylation and hypomethylation

Visualize results (including males samples in the visualization)

Heatmap of associated CpGs

Table of significantly associated probes (P-Value < 0.01)

Visualize top 20 associations

Number of associated CpGs per gene

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 detected

[1] "TKTL1"  "IDH3G"  "PDK3"   "COX7B"  "FOXP3"  "NOX1"   "PDHA1"  "PFKFB1"

Add all DGKs and NDUFs

Visualize results

Visualize associations 


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] 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             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_1.0.9             cellranger_1.1.0       jquerylib_0.1.4       
 [43] vctrs_0.6.5            Biostrings_2.64.0      writexl_1.4.0         
 [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] zlibbioc_1.42.0        scales_1.2.0           hms_1.1.1             
 [55] promises_1.2.0.1       RColorBrewer_1.1-3     yaml_2.3.5            
 [58] memoise_2.0.1          sass_0.4.2             stringi_1.7.8         
 [61] RSQLite_2.2.15         highr_0.9              genefilter_1.78.0     
 [64] rlang_1.1.3            pkgconfig_2.0.3        bitops_1.0-7          
 [67] evaluate_0.15          lattice_0.20-45        htmlwidgets_1.5.4     
 [70] labeling_0.4.2         cowplot_1.1.1          bit_4.0.4             
 [73] tidyselect_1.2.1       magrittr_2.0.3         R6_2.5.1              
 [76] generics_0.1.3         DelayedArray_0.22.0    DBI_1.1.3             
 [79] pillar_1.9.0           haven_2.5.0            withr_3.0.0           
 [82] survival_3.4-0         KEGGREST_1.36.3        RCurl_1.98-1.7        
 [85] modelr_0.1.8           crayon_1.5.2           utf8_1.2.4            
 [88] tzdb_0.3.0             rmarkdown_2.14         grid_4.2.0            
 [91] readxl_1.4.0           blob_1.2.3             git2r_0.30.1          
 [94] reprex_2.0.1           digest_0.6.30          xtable_1.8-4          
 [97] httpuv_1.6.6           munsell_0.5.0          beeswarm_0.4.0        
[100] vipor_0.4.5            bslib_0.4.1