--- title: "Introduction to TPP2D for 2D-TPP analysis" author: "Nils Kurzawa" date: "15/4/2019" package: TPP2D output: BiocStyle::html_document vignette: > %\VignetteIndexEntry{Introduction to TPP2D for 2D-TPP analysis} %\VignetteEngine{knitr::rmarkdown} %VignetteEncoding{UTF-8} bibliography: bibliography.bib csl: cell.csl --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` # Abstract Thermal proteome profiling (TPP) [@Savitski2014; @Franken2015] is an unbiased mass spectrometry-based method to assess protein-ligand interactions. It works by employing the cellular thermal shift assay (CETSA) [@Molina2013] on a proteome-wide scale which in brief monitors the profiles of proteins in cells over a temperature gradient and tries to detect shifts induced by ligand-protein interactions in a treatment versus a control sample. 2D-TPP represents a refined version of the assay [@Becher2016] which uses a concentration gradient of the ligand of interest over a temperature gradient. This package aims to analyze data retrieved from 2D-TPP experiments by a functional analysis approach. # General information This package aims at providing an analysis tool for datasets obtained with the 2D-TPP assay. Please note that methods for analyzing convential TPP datasets (e.g. single dose, melting curve approach) can be found at: https://bioconductor.org/packages/release/bioc/html/TPP.html and https://git.embl.de/childs/TPP-data-analysis/blob/master/NPARC_paper/reports/NPARC_workflow.Rmd . This vignette is not aimed to represent an in-depth introduction to thermal proteome profiling, please refer to other sources for this purpose: - Original TPP paper: http://science.sciencemag.org/content/346/6205/1255784 - 2D-TPP paper: https://www.nature.com/articles/nchembio.2185 - review article: https://proteomesci.biomedcentral.com/articles/10.1186/s12953-017-0122-4 # Installation 1. Download the package from Bioconductor. ```{r getPackage, eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("TPP2D") ``` Or install the development version of the package from Github. ```{r, eval = FALSE} BiocManager::install(“nkurzaw/TPP2D”) ``` 2. Load the package into R session. ```{r Load, message=FALSE} library(TPP2D) ``` # Introduction The 2D-TPP assay is usually set up in a way that for each temperature 5 different ligand concentrations (including a vehicle condition) are used and two adjacent temperatures each are multiplexed in a single mass spectrometry (MS) run. Typically up to 10 or 12 temperatures are used in total that add up to 5 or 6 MS runs respectively (Figure 1). ```{r tpp_schematic, echo=FALSE, fig.cap="Experimental 2D-TPP workflow."} knitr::include_graphics(file.path(system.file(package = "TPP2D"), "tpp_2d_schematic.jpg")) ``` This package aims at providing a tool for finding 'hits' (proteins affected in their thermal stability by the treatment used in the experiment) at a given false disscovery rate (FDR). Please note that a change in thermal satbility of a protein is not a guarantee for it interacting with the molecule used as treatment. However, we try to give the user additional information by specifying whether an observed effect is likely due to stabilization or a change in expression or solubility of a given protein to make the interpretation of detected hits as easy as possible. # Step-by-step workflow ```{r, message=FALSE, warning=FALSE} library(dplyr) library(TPP2D) ``` After having loaded `dplyr` and the `TPP2D` package itself we start by loading an example dataset which is supplied with the package. Therefore, we use the `import2dDataset` function. For this puporse we need to supply a config table that essentially describes which experimental conditions the different TMT labels used correspond to and supplies paths to the raw data files (note: since this example dataset is included in the package it does not contain a "Path" column, this is however mandatory if the data should be read in from external raw files). ```{r} data("config_tab") data("raw_dat_list") config_tab ``` We then call the import function (note: we here supply a list of data frames for the "data" argument, replacing the raw data files that would be normally specified in the above mentioned column of the config table. If this is supplied the argument "data" can simply be ignored): ```{r, warning=FALSE} import_df <- import2dDataset( configTable = config_tab, data = raw_dat_list, idVar = "protein_id", intensityStr = "signal_sum_", fcStr = "rel_fc_", nonZeroCols = "qusm", geneNameVar = "gene_name", addCol = NULL, qualColName = "qupm", naStrs = c("NA", "n/d", "NaN"), concFactor = 1e6, medianNormalizeFC = TRUE, filterContaminants = TRUE) recomp_sig_df <- recomputeSignalFromRatios(import_df) ``` Please refer to the help page of the function to retrieve in-depth description of the different arguments. Essentially the function needs to know the names or prefixes of the columns in the raw data files, that contain different informations like protein id or the raw or relative signal intensities measured for the different TMT labels. The imported synthetic dataset consists of 17 simulated protein 2D thermal profiles (protein1-17) and 3 spiked-in true positives (tp1-3). It represents a data frame with the columns: ```{r, echo=FALSE} knitr::kable(tibble( column = colnames(recomp_sig_df), description = c("protein identifier", "number of unique quantified peptides", "number of unique spectra", "gene name", "temperature incubated at", "experiment identifier", "TMT label", "RefCol", "treatment concentration", "raw reporter ion intensity sum", paste("raw relative fold change compared to", "vehicle condition at the same temperature"), "log10 treatment concentration", "median normalized fold change", "recomputed reporter ion intensity", "recomputed log2 reporter ion intensity"), required = c("Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "Yes")) ) ``` Here the column "required" indicates which of these columns is neccessary for usage of the downstream functions. We then begin our actual data analysis by fitting two competing models to each protein profil: A H0 model that is expected when a protein profile remains unaffected by a given treatment and a H1 that fits a contrained sigmoidal dose-response model across all temperatures. The goodness of fit of both models for each protein is then compared and a $F$ statistic is computed. ```{r} competed_models <- competeModels( df = recomp_sig_df) ``` Then we create a null model using our dataset to be able to estimate the FDR for a given $F$ statistic in the next step. ```{r} set.seed(12, kind = "L'Ecuyer-CMRG") null_model <- bootstrapNull( df = recomp_sig_df, ncores = 1, B = 1/5) ``` Please note that setting $B = 1/5$ (corresponsing to $B \times 10$ permutations) is not enough to guarantee faithful FDR estimation, this has simply been set for fast demonstration purposes. We recommend to use at least $B = 2$ for applications in praxis. To estimate the FDR for all given $F$ statistics and retrieve all significant hits at a set FDR $\alpha$ we use the following functions: ```{r, warning=FALSE} fdr_tab <- computeFdr( df_out = competed_models, df_null = null_model) hits <- findHits( fdr_df = fdr_tab, alpha = 0.1) hits %>% dplyr::select(clustername, nObs, F_statistic, fdr) ``` Finally we can fit and plot proteins that have come up as significant in our analysis by using: ```{r} plot2dTppFit(recomp_sig_df, "tp1", model_type = "H0") ``` or respectively for the H1 model: ```{r} plot2dTppFit(recomp_sig_df, "tp1", model_type = "H1") ``` ```{r} sessionInfo() ``` # References