Last updated: 2021-11-10

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Knit directory: kinase_tf_mini_tuto/

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This is a short tutorial to show how to estimate transcription factor and kinase activities from transcriptomic and phosphoproteomic data, respectively. First, we load the packages and functions that we will use during the analysis



Kinase activity estimation

Then, we load the results of the phosphoproteomic differential analysis (carried out previously) and format it properly. In addition, here we represent the top 10 up and down regulated phosphosites.

phospho_differential_analysis <- read_csv(here("data/phospho_differential_analysis.csv")) %>%
Rows: 14243 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): psite_ID
dbl (1): t_value_tumor_vs_healthy

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
plot_top_features(phospho_differential_analysis, n_top = 10) +
  ggtitle('Phosphosite space')

Version Author Date
8e3f301 Martin Garrido Rodriguez-Cordoba 2021-06-09

Next, we can load the prior knowledge interactions, composed by kinase-target relationships

omnipath_ptm <- OmnipathR::get_signed_ptms() %>%
  dplyr::filter(modification %in% c("dephosphorylation","phosphorylation")) %>%
  dplyr::mutate(p_site = paste0(substrate_genesymbol, "_", residue_type, residue_offset),
                mor = ifelse(modification == "phosphorylation", 1, -1)) %>%
  dplyr::transmute(p_site, enzyme_genesymbol, mor) %>%

omnipath_ptm$likelihood <- 1

#we remove ambiguous modes of regulations
omnipath_ptm$id <- paste(omnipath_ptm$p_site,omnipath_ptm$enzyme_genesymbol,sep ="")
omnipath_ptm <- omnipath_ptm[!duplicated(omnipath_ptm$id),]
omnipath_ptm <- omnipath_ptm[,-5]

On a final step, we run viper to get the Kinase activities from the phosphoproteomic data. You can also run that on wour normalised intesity matrix of phosphosites directly, as long as it is formatted as a dataframe of similar format as here. User is strongly encouraged to check for more info on the algorithm here employed.

#rename KSN to fit decoupler format
names(omnipath_ptm)[c(1,2)] <- c("target","tf")

kin_activity <- run_wmean(
  mat = as.matrix(phospho_differential_analysis), 
  network = omnipath_ptm, 
  .source = "tf",
  times = 1000
kin_activity <- kin_activity[kin_activity$statistic == "norm_wmean",c(2,4)] %>%
  tibble::column_to_rownames(var = "source")
plot_top_features(kin_activity, n_top = 10) +
  ggtitle('Kinase space')

Version Author Date
87802fa Martin Garrido Rodriguez-Cordoba 2021-06-11
8e3f301 Martin Garrido Rodriguez-Cordoba 2021-06-09

Transcription factor activity

First we import the dorothea regulons (using only confidence A, B, and C), see dorothea publication for information on confidence levels.

dorothea_df <- dorothea_hs %>%
  dplyr::filter(confidence %in% c("A", "B", "C")) %>%
  dplyr::select(target, tf, mor) %>%

dorothea_df$likelihood <- 1

Now we import the RNAseq data. It has entrez gene identifiers, but we need it to have gene symbols to match dorothea database, so we have to do some id conversion as well. Here we can also take a look to the top altered features.

RNA_differential_analysis <- read_csv(here("data/RNA_differential_analysis.csv")) %>%
  tibble::column_to_rownames("ID") %>%
  dplyr::select(t) %>%
  as.matrix() %>%
  translateMatrixWithDb(mat = ., db =, 
                                sourceKey = "ENTREZID", targetKey = "SYMBOL")
Rows: 15919 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (7): ID, logFC, AveExpr, t, P.Value, adj.P.Val, B

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
'select()' returned 1:1 mapping between keys and columns
No input summarise function detected, using first match on multi-mapping situations.
153 of 15919 input ids on the translator data frame could not be mapped.
0 of 15919 input ids on the translator data frame were mapped to 2 or more target ids.
0 of 15766 target ids on the translator data frame were mapped to 2 or more input ids.
Input keys were finally mapped to 15766 target ids.
plot_top_features(RNA_differential_analysis, n_top = 10) +
  ggtitle('Transcriptomic space')

Version Author Date
87802fa Martin Garrido Rodriguez-Cordoba 2021-06-11
8e3f301 Martin Garrido Rodriguez-Cordoba 2021-06-09

Now we estimate the TF activities using run_mean from decoupleR and visualize the top 10 altered TFs

TF_activities <- = as.matrix(RNA_differential_analysis), 
                                         network = dorothea_df, .source = "tf",
                                         times = 1000))
TF_activities <- TF_activities %>%
  dplyr::filter(statistic == "norm_wmean") %>%
  dplyr::select(source, score) %>%
  tibble::column_to_rownames(var = "source")
plot_top_features(TF_activities, n_top = 10) +
  ggtitle('TF space')

Version Author Date
87802fa Martin Garrido Rodriguez-Cordoba 2021-06-11
8e3f301 Martin Garrido Rodriguez-Cordoba 2021-06-09

Next steps

Now you have succefully estimated kinase and TF activities from phosphoproteomic and transcriptomic. You can now combine them together and use them as input for COSMOS. You may also leave them separated and use them a separated input and measurments in cosmos, if you lack metabolomic data

See for more info on how to use cosmos

R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

[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] parallel  stats4    stats     graphics  grDevices datasets  utils    
[8] methods   base     

other attached packages:
 [1]  AnnotationDbi_1.56.1 IRanges_2.26.0      
 [4] S4Vectors_0.30.2     Biobase_2.52.0       BiocGenerics_0.38.0 
 [7] rmarkdown_2.11       decoupleR_2.1.0      dorothea_1.6.0      
[10] OmnipathR_3.2.0      here_1.0.1           forcats_0.5.1       
[13] stringr_1.4.0        dplyr_1.0.7          purrr_0.3.4         
[16] readr_2.0.2          tidyr_1.1.4          tibble_3.1.6        
[19] ggplot2_3.3.5        tidyverse_1.3.1      workflowr_1.6.2     

loaded via a namespace (and not attached):
  [1] colorspace_2.0-2            ellipsis_0.3.2             
  [3] rprojroot_2.0.2             XVector_0.32.0             
  [5] GenomicRanges_1.44.0        fs_1.5.0                   
  [7] rstudioapi_0.13             farver_2.1.0               
  [9] bit64_4.0.5                 fansi_0.5.0                
 [11] lubridate_1.8.0             ranger_0.13.1              
 [13] xml2_1.3.2                  RobustRankAggreg_1.1       
 [15] cachem_1.0.6                knitr_1.36                 
 [17] jsonlite_1.7.2              speedglm_0.3-3             
 [19] bcellViper_1.30.0           broom_0.7.10               
 [21] dbplyr_2.1.1                png_0.1-7                  
 [23] compiler_4.1.1              httr_1.4.2                 
 [25] backports_1.3.0             assertthat_0.2.1           
 [27] Matrix_1.3-4                fastmap_1.1.0              
 [29] cli_3.1.0                   later_1.3.0                
 [31] htmltools_0.5.2             prettyunits_1.1.1          
 [33] tools_4.1.1                 igraph_1.2.8               
 [35] gtable_0.3.0                glue_1.5.0                 
 [37] GenomeInfoDbData_1.2.6      rappdirs_0.3.3             
 [39] Rcpp_1.0.7                  cellranger_1.1.0           
 [41] jquerylib_0.1.4             Biostrings_2.60.2          
 [43] vctrs_0.3.8                 xfun_0.28                  
 [45] rvest_1.0.2                 lifecycle_1.0.1            
 [47] renv_0.13.2                 zlibbioc_1.38.0            
 [49] MASS_7.3-54                 scales_1.1.1               
 [51] vroom_1.5.5                 hms_1.1.1                  
 [53] promises_1.2.0.1            MatrixGenerics_1.4.3       
 [55] SummarizedExperiment_1.22.0 yaml_2.2.1                 
 [57] curl_4.3.2                  memoise_2.0.0              
 [59] sass_0.4.0                  rpart_4.1-15               
 [61] stringi_1.7.5               RSQLite_2.2.8              
 [63] highr_0.9                   checkmate_2.0.0            
 [65] GenomeInfoDb_1.28.4         rlang_0.4.12               
 [67] pkgconfig_2.0.3             matrixStats_0.61.0         
 [69] bitops_1.0-7                evaluate_0.14              
 [71] lattice_0.20-45             labeling_0.4.2             
 [73] bit_4.0.4                   tidyselect_1.1.1           
 [75] logger_0.2.2                magrittr_2.0.1             
 [77] R6_2.5.1                    generics_0.1.1             
 [79] DelayedArray_0.18.0         DBI_1.1.1                  
 [81] pillar_1.6.4                haven_2.4.3                
 [83] whisker_0.4                 withr_2.4.2                
 [85] KEGGREST_1.32.0             RCurl_1.98-1.5             
 [87] modelr_0.1.8                crayon_1.4.2               
 [89] utf8_1.2.2                  tzdb_0.2.0                 
 [91] progress_1.2.2              grid_4.1.1                 
 [93] readxl_1.3.1                blob_1.2.2                 
 [95] git2r_0.28.0                reprex_2.0.1               
 [97] digest_0.6.28               httpuv_1.6.3               
 [99] munsell_0.5.0               bslib_0.3.1