Last updated: 2020-03-08

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library(corrplot)
library(ggplot2)

Load data

We present here three examples of genes that have distinct characteristic patterns of tissue-wide correlations.

robocov_gtex = get(load("/Users/kushaldey/Documents/Robocov-pages/data/Robocov_Box_all_genes.rda"))
probocov_gtex = get(load("/Users/kushaldey/Documents/Robocov-pages/data/Robocov_Precision_all_genes.rda"))
robospan = apply(robocov_gtex, 3, sum)/(53*53)
robospan[order(robospan, decreasing = T)[1:10]]
ENSG00000134184 ENSG00000226278 ENSG00000184674 ENSG00000163682 
      0.7032426       0.6950521       0.6912778       0.6872412 
ENSG00000233927 ENSG00000260246 ENSG00000204792 ENSG00000183298 
      0.6794906       0.6776832       0.6722359       0.6624388 
ENSG00000196436 ENSG00000271581 
      0.6610709       0.6344899

Correlation Matrix

RPL9

corrplot(robocov_gtex[,,"ENSG00000163682"],   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

HBB

corrplot(robocov_gtex[,,"ENSG00000244734"],   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

robospan[order(robospan, decreasing = F)[1:10]]
ENSG00000235795 ENSG00000257307 ENSG00000242100 ENSG00000228205 
     0.01889093      0.01890535      0.01891395      0.01892251 
ENSG00000197830 ENSG00000227344 ENSG00000268543 ENSG00000258782 
     0.01892328      0.01892549      0.01895040      0.01895996 
ENSG00000188505 ENSG00000180389 
     0.01896545      0.01896681

RP11-778D9.4

corrplot(robocov_gtex[,,"ENSG00000228205"],   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

NCCRP1

corrplot(robocov_gtex[,,"ENSG00000188505"],   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

Partial correlation matrices

RPL9

corrplot(probocov_gtex[,,"ENSG00000163682"],   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

HBB

corrplot(probocov_gtex[,,"ENSG00000244734"],   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

Median Robocov patterns across genes

mean_robocov = apply(robocov_gtex, c(1,2), median)
mean_probocov = apply(probocov_gtex, c(1,2), median)
corrplot(mean_robocov,   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

Mean Robocov pattern across genes

corrplot(mean_probocov,   diag = TRUE,
         col = colorRampPalette(c("lightblue4", "lightblue2", "white", "indianred1", "indianred3"))(200),
         tl.pos = "ld", tl.cex = 0.6, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "lower", tl.srt=45)

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.1.1 corrplot_0.84

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1        knitr_1.20        whisker_0.3-2    
 [4] magrittr_1.5      workflowr_1.1.1   tidyselect_0.2.5 
 [7] munsell_0.5.0     colorspace_1.4-1  R6_2.4.0         
[10] rlang_0.4.2       dplyr_0.8.0.1     plyr_1.8.4       
[13] stringr_1.4.0     tools_3.5.1       grid_3.5.1       
[16] gtable_0.3.0      R.oo_1.22.0       withr_2.1.2      
[19] git2r_0.23.0      htmltools_0.3.6   assertthat_0.2.1 
[22] yaml_2.2.0        lazyeval_0.2.2    rprojroot_1.3-2  
[25] digest_0.6.19     tibble_2.1.1      crayon_1.3.4     
[28] purrr_0.3.2       R.utils_2.7.0     glue_1.3.1       
[31] evaluate_0.12     rmarkdown_1.10    stringi_1.4.3    
[34] pillar_1.3.1      compiler_3.5.1    scales_1.0.0     
[37] backports_1.1.4   R.methodsS3_1.7.1 pkgconfig_2.0.2

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