Last updated: 2020-03-08

workflowr checks: (Click a bullet for more information)

  • ✖ R Markdown file: uncommitted changes The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

  • ✔ 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(20190721)

    The command set.seed(20190721) 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.

  • ✔ Repository version: 47185fd

    Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

    Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 
    
Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    docs/.DS_Store
    Ignored:    draft/
    Ignored:    output/output_sparse/

Untracked files:
    Untracked:  .gitignore~
    Untracked:  analysis/band_prec_robocov.Rmd
    Untracked:  analysis/blood_corspan_robospan_probospan.Rmd
    Untracked:  analysis/corspan_robospan_probospan.Rmd
    Untracked:  analysis/diff_robospan_examples.Rmd
    Untracked:  analysis/eigenvalues_hub.Rmd
    Untracked:  analysis/figure2.Rmd
    Untracked:  analysis/gtex_analysis_robocov_examples.Rmd
    Untracked:  analysis/gtex_pRobocov_pRobospan.Rmd
    Untracked:  analysis/gtex_predictive_robocov.Rmd
    Untracked:  analysis/gtex_rank_imputation.Rmd
    Untracked:  analysis/gtex_robocov.Rmd
    Untracked:  analysis/gtex_robocov_ex_robospan.Rmd
    Untracked:  analysis/housekeeping_PPI_MR_enrichment.Rmd
    Untracked:  analysis/hub_robocov.Rmd
    Untracked:  analysis/mean_robocov.Rmd
    Untracked:  analysis/pLI_shet_robospan_probospan.Rmd
    Untracked:  analysis/predictive_analytics_gtex.Rmd
    Untracked:  analysis/robocov_demo.Rmd
    Untracked:  analysis/robospan_blood_compare.Rmd
    Untracked:  analysis/robospan_corr.Rmd
    Untracked:  analysis/summary_correlation_structure.Rmd
    Untracked:  analysis/supp_figure_simulation.Rmd
    Untracked:  analysis/toeplitz_robocov.Rmd
    Untracked:  code/  robocov_sim.R
    Untracked:  code/FDR_Robospan.R
    Untracked:  code/Joints_Nov24.R
    Untracked:  code/Untitled.R
    Untracked:  code/annot_size.R
    Untracked:  code/band_prec_ocor_sim.R
    Untracked:  code/band_prec_pcor_sim.R
    Untracked:  code/band_prec_sim.R
    Untracked:  code/band_prec_sim2.R
    Untracked:  code/bandprec_cor_sim.R
    Untracked:  code/baseline_strategies.R
    Untracked:  code/corr_span.R
    Untracked:  code/correlation_gene_scores.R
    Untracked:  code/gene_score_all_strategies.R
    Untracked:  code/gtex_robocov.R
    Untracked:  code/gtex_robocov_precision.R
    Untracked:  code/hub_prec_cor_sim.R
    Untracked:  code/hub_sim.R
    Untracked:  code/hub_sim2.R
    Untracked:  code/hub_sim_sparse.R
    Untracked:  code/joint_robocov_model.R
    Untracked:  code/joints_robocov.R
    Untracked:  code/many_tau_star.R
    Untracked:  code/maxHiCgene.R
    Untracked:  code/meta_enrich.R
    Untracked:  code/meta_single_enrich.R
    Untracked:  code/postprocess_hub_toeplitz.R
    Untracked:  code/predictive_analytics_hub.R
    Untracked:  code/predictive_analytics_toeplitz.R
    Untracked:  code/robocov_gtex.R
    Untracked:  code/robocov_gtex_blood.R
    Untracked:  code/robocov_gtex_brain.R
    Untracked:  code/sim_results.R
    Untracked:  code/single_tau_star.R
    Untracked:  code/toeplitz_prec_cor_sim.R
    Untracked:  code/toeplitz_sim.R
    Untracked:  code/toeplitz_sim2.R
    Untracked:  code/toeplitz_sim_sparse.R
    Untracked:  data/Cor_pairwise_all_genes.rda
    Untracked:  data/Gene_Scores/
    Untracked:  data/Robocov_Box_all_genes.rda
    Untracked:  data/Robocov_Precision_all_genes.rda
    Untracked:  data/gene_names_GTEX_V6.txt
    Untracked:  data/housekeeping_genes.txt
    Untracked:  data/person_tissue_genes_voom.rda
    Untracked:  docs/figure/
    Untracked:  sim_code/

Unstaged changes:
    Modified:   .gitignore
    Modified:   analysis/index.Rmd
    Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes. 
library(Robocov)
library(corrplot)
corrplot 0.84 loaded
library(ggplot2)
Warning: package 'ggplot2' was built under R version 3.5.2

Here we illustrare the Robocov estimates for a few genes of interest and then introduce the concept of Robospan.

probocov_gtex = get(load("/Users/kushaldey/Documents/Robocov-pages/data/Robocov_Precision_all_genes.rda"))
robocov_gtex = get(load("/Users/kushaldey/Documents/Robocov-pages/data/Robocov_Box_all_genes.rda"))
dim(probocov_gtex)
[1]    53    53 16069

HBB (ENSG00000244734)

corrplot(probocov_gtex[,,"ENSG00000244734"],  diag = TRUE,
         col = colorRampPalette(c("blue", "white", "red"))(200),
         tl.pos = "td", tl.cex = 0.4, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "upper")

probospan = apply(probocov_gtex, 3, sum)/(53*53)
plot(density(probospan), xlab = "Robospan score", ylab = "density")

probospan[order(probospan, decreasing = T)[1:10]]
ENSG00000237039 ENSG00000233927 ENSG00000254353 ENSG00000224114 
     0.03582869      0.03582276      0.03578276      0.03576680 
ENSG00000260246 ENSG00000134184 ENSG00000196436 ENSG00000152726 
     0.03575554      0.03570917      0.03567876      0.03567044 
ENSG00000271581 ENSG00000204792 
     0.03565612      0.03564449 
corrplot(probocov_gtex[,,"ENSG00000237039"],  diag = TRUE,
         col = colorRampPalette(c("blue", "white", "red"))(200),
         tl.pos = "td", tl.cex = 0.4, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "upper")

corrplot(robocov_gtex[,,"ENSG00000237039"],  diag = TRUE,
         col = colorRampPalette(c("blue", "white", "red"))(200),
         tl.pos = "td", tl.cex = 0.4, tl.col = "black",
         rect.col = "white",na.label.col = "white",
         method = "color", type = "upper")

Ensembl and Gene symbol names

ensembl_gene_symbol = read.table("/Users/kushaldey/Documents/Robocov-pages/data/Gene_Scores/ensembl_and_hgnc_symbols.txt")
head(ensembl_gene_symbol)
               V1       V2
1 ENSG00000000419     DPM1
2 ENSG00000000457    SCYL3
3 ENSG00000000460 C1orf112
4 ENSG00000000938      FGR
5 ENSG00000000971      CFH
6 ENSG00000001036    FUCA2
gene_symbols = ensembl_gene_symbol[match(names(probospan), ensembl_gene_symbol[,1]), 2]
probospan2 = probospan[which(!is.na(gene_symbols))] 
names(probospan2) = gene_symbols[which(!is.na(gene_symbols))]

pRobospan mean

genes = names(probospan2)[order(probospan2, decreasing = T)[1:1600]]
df = cbind.data.frame(genes, 1)
write.table(df, file = "/Users/kushaldey/Documents/Robocov-pages/data/Gene_Scores/pRobospan_mean.txt",
            row.names = F, col.names = F, sep = "\t", quote=F)

pRobospan mean Blood

probospan = apply(probocov_gtex[,53,], 2, mean)
gene_symbols = ensembl_gene_symbol[match(names(probospan), ensembl_gene_symbol[,1]), 2]
probospan2 = probospan[which(!is.na(gene_symbols))] 
names(probospan2) = gene_symbols[which(!is.na(gene_symbols))]
genes = names(probospan2)[order(probospan2, decreasing = T)[1:1600]]
df = cbind.data.frame(genes, 1)
write.table(df, file = "/Users/kushaldey/Documents/Robocov-pages/data/Gene_Scores/pRobospan_Mean_Blood.txt",
            row.names = F, col.names = F, sep = "\t", quote=F)

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 Robocov_0.1-6

loaded via a namespace (and not attached):
 [1] gmp_0.5-13.2      Rcpp_1.0.1        pillar_1.3.1     
 [4] compiler_3.5.1    git2r_0.23.0      CVXR_0.99-2      
 [7] plyr_1.8.4        workflowr_1.1.1   R.methodsS3_1.7.1
[10] R.utils_2.7.0     tools_3.5.1       digest_0.6.19    
[13] bit_1.1-14        tibble_2.1.1      evaluate_0.12    
[16] gtable_0.3.0      lattice_0.20-35   pkgconfig_2.0.2  
[19] rlang_0.4.2       Matrix_1.2-14     yaml_2.2.0       
[22] withr_2.1.2       dplyr_0.8.0.1     Rmpfr_0.7-1      
[25] ECOSolveR_0.4     stringr_1.4.0     knitr_1.20       
[28] tidyselect_0.2.5  rprojroot_1.3-2   bit64_0.9-7      
[31] grid_3.5.1        glue_1.3.1        R6_2.4.0         
[34] rmarkdown_1.10    purrr_0.3.2       magrittr_1.5     
[37] whisker_0.3-2     backports_1.1.4   scales_1.0.0     
[40] htmltools_0.3.6   scs_1.1-1         assertthat_0.2.1 
[43] colorspace_1.4-1  stringi_1.4.3     lazyeval_0.2.2   
[46] munsell_0.5.0     crayon_1.3.4      R.oo_1.22.0

This reproducible R Markdown analysis was created with workflowr 1.1.1