Aim of this workshop

In this workshop we will focus on two published phosphoproteomics datasets to illustrate some key components in PhosR package, including:

  1. Phosphoproteomic data preprocessing (normalisation, imputation, and quality control),
  2. Knowledge-based kinase perturbation analysis (using direction analysis),
  3. Kinase substrate predictions (using positive unlabelled learning).

At the end of this workshop you should have the basic understanding of some key steps in phosphoproteomics data analysis and some key computational and statistical methods that could be applied in each step.

Prerequisites

Ideally, you should be somewhat familiar with R. If you haven’t used R before, you can still pick up key elements in phosphoproteomics data analysis by running the codes I provided.

Please try to download and install:

Installation

You can find all the data and materials here.

You should be able to run the codes below in R to install all the packages needed in this workshop. If you encounter any problems. Please let one of the instructors know.

devtools::install_github("PengyiYang/PhosR")

Clipboard

In some circumstances, we might need to share codes that are not currently a part of the existing materials. Please click here to access these codes.

References

Methodologies

  1. Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data, Pengyi Yang et al. PLoS Computational Biology, 2015.

  2. KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis Pengyi Yang et al. Proteomics, 2016

  3. Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data, Pengyi Yang et al. Bioinformatics, 2016

Data

  1. Multi-omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency, Pengyi Yang et al. Cell Systems, 2019.
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.15
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] Biobase_2.44.0       GenomicRanges_1.36.0 GenomeInfoDb_1.20.0 
##  [4] IRanges_2.18.0       S4Vectors_0.22.1     BiocGenerics_0.30.0 
##  [7] scMerge_1.0.0        PhosR_0.1.0          directPA_1.4        
## [10] calibrate_1.7.2      MASS_7.3-51.4        rgl_0.100.19        
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_1.4-1            RcppEigen_0.3.3.5.0        
##   [3] class_7.3-15                htmlTable_1.13.1           
##   [5] XVector_0.24.0              base64enc_0.1-3            
##   [7] rstudioapi_0.10             proxy_0.4-23               
##   [9] codetools_0.2-16            splines_3.6.0              
##  [11] knitr_1.23                  Formula_1.2-3              
##  [13] jsonlite_1.6                cluster_2.0.9              
##  [15] shiny_1.3.2                 BiocManager_1.30.4         
##  [17] compiler_3.6.0              backports_1.1.4            
##  [19] assertthat_0.2.1            Matrix_1.2-17              
##  [21] lazyeval_0.2.2              limma_3.40.6               
##  [23] later_0.8.0                 acepack_1.4.1              
##  [25] htmltools_0.3.6             tools_3.6.0                
##  [27] rsvd_1.0.1                  gtable_0.3.0               
##  [29] glue_1.3.1                  GenomeInfoDbData_1.2.1     
##  [31] dplyr_0.8.1                 Rcpp_1.0.1                 
##  [33] bbmle_1.0.20                gdata_2.18.0               
##  [35] nlme_3.1-140                iterators_1.0.10           
##  [37] crosstalk_1.0.0             xfun_0.7                   
##  [39] stringr_1.4.0               mime_0.6                   
##  [41] miniUI_0.1.1.1              irlba_2.3.3                
##  [43] gtools_3.8.1                statmod_1.4.32             
##  [45] dendextend_1.12.0           zlibbioc_1.30.0            
##  [47] scales_1.0.0                pcaMethods_1.76.0          
##  [49] promises_1.0.1              SummarizedExperiment_1.14.0
##  [51] RColorBrewer_1.1-2          SingleCellExperiment_1.6.0 
##  [53] yaml_2.2.0                  gridExtra_2.3              
##  [55] ggplot2_3.1.1               rpart_4.1-15               
##  [57] latticeExtra_0.6-28         stringi_1.4.3              
##  [59] foreach_1.4.4               e1071_1.7-1                
##  [61] checkmate_1.9.3             caTools_1.17.1.2           
##  [63] BiocParallel_1.18.0         manipulateWidget_0.10.0    
##  [65] rlang_0.4.0                 pkgconfig_2.0.2            
##  [67] matrixStats_0.55.0          bitops_1.0-6               
##  [69] M3Drop_1.10.0               evaluate_0.13              
##  [71] lattice_0.20-38             purrr_0.3.2                
##  [73] ruv_0.9.7                   htmlwidgets_1.3            
##  [75] tidyselect_0.2.5            plyr_1.8.4                 
##  [77] magrittr_1.5                R6_2.4.0                   
##  [79] gplots_3.0.1.1              Hmisc_4.2-0                
##  [81] DelayedArray_0.10.0         pillar_1.4.1               
##  [83] foreign_0.8-71              mgcv_1.8-28                
##  [85] survival_2.44-1.1           RCurl_1.95-4.12            
##  [87] nnet_7.3-12                 tibble_2.1.2               
##  [89] crayon_1.3.4                KernSmooth_2.23-15         
##  [91] rmarkdown_1.13              viridis_0.5.1              
##  [93] grid_3.6.0                  data.table_1.12.2          
##  [95] reldist_1.6-6               digest_0.6.19              
##  [97] webshot_0.5.1               xtable_1.8-4               
##  [99] httpuv_1.5.1                numDeriv_2016.8-1          
## [101] munsell_0.5.0               viridisLite_0.3.0