Introduction to Rtpca
26 April, 2022
Package
Rtpca 1.6.0
Contents
1 Installation
Installation from Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Rtpca")- Load the package into your R session.
library(Rtpca)2 Introduction
Thermal proteome profiling (TPP) (Mateus et al., 2020; Savitski et al., 2014) is a mass
spectrometry-based, proteome-wide implemention of the cellular thermal shift
assay (Martinez Molina et al., 2013). It was originally developed to study drug-(off-)target
engagement. However, it was realized that profiles of interacting protein
pairs appeared more similar than by chance which was coined as
‘thermal proximity co-aggregation’ (TPCA) (Tan et al., 2018). The R package Rtpca
enables analysis of TPP datasets using the TPCA concept for studying
protein-protein interactions and protein complexes and also allows to test for
differential protein-protein interactions across different conditions.
This vignette only represents a minimal example. To have a look at a more realistic example feel free to check out this more realisticexample.
Note: if you use Rtpca in published research, please cite:
Kurzawa, N., Mateus, A. & Savitski, M.M. (2020) Rtpca: an R package for differential thermal proximity coaggregation analysis. Bioinformatics, 10.1093/bioinformatics/btaa682
3 The Rtpca package workflow
We also load the TPP package to illustrate how to import TPP data with the
Bioconductor package and then input it into the Rtpca functions.
library(TPP)3.1 Import Thermal proteome profiling data using the TPP package
We load the data hdacTR_smallExample which is part of the TPP package
data("hdacTR_smallExample")Filter hdacTR_data to speed up computations
set.seed(123)
random_proteins <- sample(hdacTR_data[[1]]$gene_name, 300)hdacTR_data_fil <- lapply(hdacTR_data, function(temp_df){
filter(temp_df, gene_name %in% random_proteins)
})We can now import our small example dataset using the import function from
the TPP package:
trData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data_fil)## Importing data...## Comparisons will be performed between the following experiments:## Panobinostat_1_vs_Vehicle_1## Panobinostat_2_vs_Vehicle_2## ## The following valid label columns were detected:
## 126, 127L, 127H, 128L, 128H, 129L, 129H, 130L, 130H, 131L.##
## Importing TR dataset: Vehicle_1## Removing duplicate identifiers using quality column 'qupm'...## 300 out of 300 rows kept for further analysis.## -> Vehicle_1 contains 300 proteins.## -> 299 out of 300 proteins (99.67%) suitable for curve fit (criterion: > 2 valid fold changes per protein).##
## Importing TR dataset: Vehicle_2## Removing duplicate identifiers using quality column 'qupm'...## 299 out of 299 rows kept for further analysis.## -> Vehicle_2 contains 299 proteins.## -> 296 out of 299 proteins (99%) suitable for curve fit (criterion: > 2 valid fold changes per protein).##
## Importing TR dataset: Panobinostat_1## Removing duplicate identifiers using quality column 'qupm'...## 300 out of 300 rows kept for further analysis.## -> Panobinostat_1 contains 300 proteins.## -> 298 out of 300 proteins (99.33%) suitable for curve fit (criterion: > 2 valid fold changes per protein).##
## Importing TR dataset: Panobinostat_2## Removing duplicate identifiers using quality column 'qupm'...## 300 out of 300 rows kept for further analysis.## -> Panobinostat_2 contains 300 proteins.## -> 294 out of 300 proteins (98%) suitable for curve fit (criterion: > 2 valid fold changes per protein).## 3.2 Performing thermal co-aggregation analysis with Rtpca
Then, we load string_ppi_df which is a data frame that annotates
protein-protein interactions as obtained from StringDB (Szklarczyk et al., 2019) that
comes with the Rtpca package
data("string_ppi_df")
string_ppi_df## # A tibble: 252,558 × 4
## x y combined_score pair
## <chr> <chr> <dbl> <chr>
## 1 ARF5 SPTBN2 909 ARF5:SPTBN2
## 2 ARF5 KIF13B 910 ARF5:KIF13B
## 3 ARF5 KIF21A 910 ARF5:KIF21A
## 4 ARF5 TMED7 906 ARF5:TMED7
## 5 ARF5 ARFGAP1 971 ARF5:ARFGAP1
## 6 ARF5 ANK2 915 ANK2:ARF5
## 7 ARF5 KLC1 905 ARF5:KLC1
## 8 ARF5 COPZ2 927 ARF5:COPZ2
## 9 ARF5 KIF15 914 ARF5:KIF15
## 10 ARF5 DCTN5 902 ARF5:DCTN5
## # … with 252,548 more rowsThis table has been created from the human protein.links table downloaded from the StringDB website. It can serve as a template for users to create equivalent tables for other organisms.
3.2.1 Run TPCA on data from a single condition
We can run TPCA for protein-protein interactions like this by using the
function runTPCA
string_ppi_cs_950_df <- string_ppi_df %>%
filter(combined_score >= 950 )
vehTPCA <- runTPCA(
objList = trData,
ppiAnno = string_ppi_cs_950_df
)## Checking input arguments.##
## Creating distance matrices.##
## Testing for complex co-aggregation.##
## Performing PPi ROC analysis.Note: it is not necessary that your data has the format of the TPP package (ExpressionSet), you can also supply the function with a list of matrices of data frames (in the case of data frames you need to additionally indicate with column contains the protein or gene names).
We can also run TPCA to test for coaggregation of protein complexes. For this purpose with can load a data frame that annotates proteins to protein complexes curated by Ori et al. (2016)
data("ori_et_al_complexes_df")
ori_et_al_complexes_df## # A tibble: 2,597 × 3
## ensembl_id protein id
## <chr> <chr> <chr>
## 1 ENSG00000222028 PSMB11 26S Proteasome
## 2 ENSG00000108671 PSMD11 26S Proteasome
## 3 ENSG00000197170 PSMD12 26S Proteasome
## 4 ENSG00000110801 PSMD9 26S Proteasome
## 5 ENSG00000115233 PSMD14 26S Proteasome
## 6 ENSG00000101182 PSMA7 26S Proteasome
## 7 ENSG00000108344 PSMD3 26S Proteasome
## 8 ENSG00000101843 PSMD10 26S Proteasome
## 9 ENSG00000165916 PSMC3 26S Proteasome
## 10 ENSG00000008018 PSMB1 26S Proteasome
## # … with 2,587 more rowsThen, we can invoke
vehComplexTPCA <- runTPCA(
objList = trData,
complexAnno = ori_et_al_complexes_df,
minCount = 2
)## Checking input arguments.##
## Creating distance matrices.##
## Testing for complex co-aggregation.##
## Performing Complex ROC analysis.We can plot a ROC curve for how well our data captures protein-protein interactions:
plotPPiRoc(vehTPCA, computeAUC = TRUE)
And we can also plot a ROC curve for how well our data captures protein complexes:
plotComplexRoc(vehComplexTPCA, computeAUC = TRUE)
3.2.2 Run differential TPCA on two conditions
In order to test for protein-protein interactions that change significantly
between both conditions, we can run the runDiffTPCA as illustrated below:
diffTPCA <-
runDiffTPCA(
objList = trData[1:2],
contrastList = trData[3:4],
ctrlCondName = "DMSO",
contrastCondName = "Panobinostat",
ppiAnno = string_ppi_cs_950_df)## Checking input arguments.## Creating distance matrices.## Comparing annotated protein-pairs across conditions.## Comparing random protein-pairs across conditions.## Generating result table.We can then plot a volcano plot to visualize the results:
plotDiffTpcaVolcano(
diffTPCA,
setXLim = TRUE,
xlimit = c(-0.5, 0.5))
The underlying result table can be inspected like this;
head(diffTpcaResultTable(diffTPCA) %>%
arrange(p_value) %>%
dplyr::select(pair, rssC1_rssC2, f_stat, p_value, p_adj))## # A tibble: 6 × 5
## pair rssC1_rssC2 f_stat p_value p_adj
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 PPP2R1A:PPP2R2D 0.109 3.86 0.0233 0.672
## 2 KPNA6:KPNB1 -0.0707 3.62 0.0267 0.672
## 3 NDUFS4:NDUFV2 -292. 2.95 0.0371 0.672
## 4 GLB1:HEXA -0.143 2.84 0.0402 0.672
## 5 SEC22B:SEC24D -1.05 2.77 0.0423 0.672
## 6 MAP2K4:MAP3K2 0.0683 2.58 0.0482 0.672We can see that none of these interactions is significant consiering the multiple comparisons we have done. Yet, we can look at the melting curves of pairs like the “KPNA6:KPNB1” by evoking:
plotPPiProfiles(diffTPCA, pair = c("KPNA6", "KPNB1"))
We can see that both protein do seem to coaggregate, but that the mild
difference in the treatment condition compared to the control condition is
likely due to technical rather than biological reasons.
This way of inspecting hits obtained by the differential analysis is
recommended in the case that significant pairs can be found to validate that
they do coaggregate in one condition and that the less strong coaggregations
in the other condition is based on reliable signal.
4 Additional remarks
As mentioned above, this vignette includes only a very minimal example, have a look at a more extensive example here.
sessionInfo()## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
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## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
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##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] TPP_3.24.0 magrittr_2.0.3 Biobase_2.56.0
## [4] BiocGenerics_0.42.0 Rtpca_1.6.0 tidyr_1.2.0
## [7] dplyr_1.0.8 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.1 VGAM_1.1-6 jsonlite_1.8.0
## [4] splines_4.2.0 foreach_1.5.2 bslib_0.3.1
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## [10] stats4_4.2.0 nls2_0.2 yaml_2.3.5
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## [16] limma_3.52.0 pROC_1.18.0 digest_0.6.29
## [19] RColorBrewer_1.1-3 colorspace_2.0-3 Matrix_1.4-1
## [22] htmltools_0.5.2 plyr_1.8.7 pkgconfig_2.0.3
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## [28] scales_1.2.0 VennDiagram_1.7.3 openxlsx_4.2.5
## [31] fdrtool_1.2.17 tibble_3.1.6 mgcv_1.8-40
## [34] generics_0.1.2 farver_2.1.0 ggplot2_3.3.5
## [37] ellipsis_0.3.2 cli_3.3.0 crayon_1.5.1
## [40] evaluate_0.15 fansi_1.0.3 nlme_3.1-157
## [43] doParallel_1.0.17 MASS_7.3-57 tools_4.2.0
## [46] data.table_1.14.2 formatR_1.12 lifecycle_1.0.1
## [49] stringr_1.4.0 munsell_0.5.0 zip_2.2.0
## [52] lambda.r_1.2.4 compiler_4.2.0 jquerylib_0.1.4
## [55] rlang_1.0.2 futile.logger_1.4.3 grid_4.2.0
## [58] RCurl_1.98-1.6 iterators_1.0.14 bitops_1.0-7
## [61] labeling_0.4.2 rmarkdown_2.14 gtable_0.3.0
## [64] codetools_0.2-18 DBI_1.1.2 reshape2_1.4.4
## [67] R6_2.5.1 gridExtra_2.3 knitr_1.38
## [70] fastmap_1.1.0 utf8_1.2.2 futile.options_1.0.1
## [73] stringi_1.7.6 parallel_4.2.0 Rcpp_1.0.8.3
## [76] vctrs_0.4.1 tidyselect_1.1.2 xfun_0.305 Acknowledgements
A big thanks to Thomas Naake and Mike Smith for helping with speeding up the code.
References
Martinez Molina, D., Jafari, R., Ignatushchenko, M., Seki, T., Larsson, E.A., Dan, C., Sreekumar, L., Cao, Y., and Nordlund, P. (2013). Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341, 84–87.
Mateus, A., Kurzawa, N., Becher, I., Sridharan, S., Helm, D., Stein, F., Typas, A., and Savitski, M.M. (2020). Thermal proteome profiling for interrogating protein interactions. Molecular Systems Biology 16, e9232.
Ori, A., Iskar, M., Buczak, K., Kastritis, P., Parca, L., Andrés-pons, A., Singer, S., Bork, P., and Beck, M. (2016). Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biology 1–15.
Savitski, M.M., Reinhard, F.B.M., Franken, H., Werner, T., Savitski, M.F., Eberhard, D., Martinez Molina, D., Jafari, R., Dovega, R.B., Klaeger, S., et al. (2014). Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 6205, 1255784.
Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., et al. (2019). STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research 47, D607–D613.
Tan, C.S.H., Go, K.D., Bisteau, X., Dai, L., Yong, C.H., Prabhu, N., Ozturk, M.B., Lim, Y.T., Sreekumar, L., Lengqvist, J., et al. (2018). Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells. Science 359, 6380, 1170–1177.