In this workshop we will focus on two published phosphoproteomics datasets to illustrate some key components in PhosR package, including:
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.
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:
R 3.6
from https://cloud.r-project.org/RStudio
from https://www.rstudio.com/products/rstudio/download/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")
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.
Methodologies
Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data, Pengyi Yang et al. PLoS Computational Biology, 2015.
KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis Pengyi Yang et al. Proteomics, 2016
Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data, Pengyi Yang et al. Bioinformatics, 2016
Data
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