--- title: "EventPointer: An effective identification of alternative splicing events using junction arrays and RNA-Seq data" author: "Juan P. Romero, Juan A. Ferrer-Bonsoms, Cesar Lobato & Angel Rubio" date: "`r BiocStyle::doc_date()`" abstract: > *EventPointer* is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in ($\Delta \Psi$) for all the events. vignette: > %\VignetteEncoding{UTF-8} %\VignetteIndexEntry{EventPointer} %\VignettePackage{EventPointer} %\VignetteEngine{knitr::rmarkdown} output: BiocStyle::html_document: toc: true fig_caption: true editor_options: markdown: wrap: 72 --- ```{r LoadFunctions, echo=FALSE, message=FALSE, warning=FALSE, results='hide'} library(knitr) opts_chunk$set(error = FALSE) library(EventPointer) library(dplyr) library(kableExtra) ``` ```{r style, echo = FALSE, results = 'asis'} ##BiocStyle::markdown() ``` # Installation EventPointer can be installed from Bioconductor using the BiocManager package: ```{r, eval=FALSE} library(BiocManager) if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("EventPointer") ``` # Introduction *EventPointer* R package provides users a simplified way to identify, classify and visualize alternative splicing events. EventPointer can work with arrays and bulk-RNA-Seq data. For arrays there is a unique pipeline. For RNA-Seq there are two main pipelines: one able to detect novel events and the other one relying on annotated events. The steps required by the algorithm are almost the same for all approaches: - *Splicing Graph Creation* The Splicing Graph (SG) is a directed graph used to represent the structure of a gene. EventPointer relies on SGSeq package to obtain the corresponding SGs for every gene. For arrays, the SG is built according to the reference transcriptome selected by the user and for RNA-Seq, it is created by predicting the features in the .bam files provided by the user or by the reference transcriptome. - *Event Detection* Once the graphs are created, EventPointer analyzes each SG in order to find the alternative splicing events. The definition of splicing events by EventPointer consists in a triplet of subgraphs (P1,P2 and PR) i.e. for a cassette exon, PR correspond to the flanking exons, P1 the junctions and exon, and P2 the junction that skips the exon. This is depicted in Figure 1. ![**Figure 1.** Definition of alternative splicing events for EventPointer](Figure2.png) - *Statistical Analysis* With all the detected events, EventPointer performs a statistical analysis to obtain the statistical significance of every splicing event: - for RNA-Seq EventPointer use bootstrap approeach to perform the statistical test. - for arrays, EventPointer considers there is a differential splicing event if the isoforms in the associated paths change their expression in opposite directions. Different and specific statistical tests can be applied. - *Visualization* To ease the interpretation of the splicing events, EventPointer generates .gtf files that can be loaded into Integrative Genomcis Viewer (IGV). The visualization allows researchers to design primers to validated the detected events using standard PCR. Figure 2 shows each of the main steps in a graphical layout. EventPointer was originally developed for array-based analysis and has since been adapted for bulk RNA-Seq. This vignette is structured into two main sections: RNA-Seq analysis and array analysis. The RNA-Seq section is divided into two parts: - **EventPointerBAM**: Alignment-based analysis, which detects novel events specific to the samples (novel version). - **EventPonterST**: Analysis of events annotated in a reference transcriptome. To cite *EventPointer*: For EventPonterST: - Ferrer-Bonsoms JA, Gimeno M, Olaverri D, Sacristan P, Lobato C, Castilla C, Carazo F, Rubio A (2022). "EventPointer 3.0: flexible and accurate splicing analysis that includes studying the differential usage of protein-domains." *NAR Genomics & Bioinformatics*, **4**(3), lqac067. [doi:10.1093/nargab/lqac067](https://doi.org/10.1093/nargab/lqac067). For EventPointer for arryas: - Romero, Juan P., et al. "EventPointer: an effective identification of alternative splicing events using junction arrays." BMC genomics 17.1 (2016): 467. [doi:10.1186/s12864-016-2816-x](http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2816-x) - Romero, Juan P., et al. "Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm." BMC genomics 19.1 (2018): 703. [doi:10.1186/s12864-018-5082-2](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-5082-2) ![**Figure 2.** Overview of *EventPointer*](Romero_Fig1.png) # RNA-Seq analysis EventPointer has two pipelines for RNA-Seq analysis: - **EventPointerBAM (EP_BAM)**: The detection, classification and quantification of splicing events is carried out using the **BAM** **files generated from RNA-seq sequence alignment**. It should be noted that the **alignment must be performed using an aligner that** **takes splicing** **into account (recommended tool: STAR)**. The main advantage of this methodology is its ability to **detect de** **novo** **events** based on reads alignment. - **EventPointerST (EP_ST)**: The detection and classification of events are determined by the **corresponding transcriptome annotation** in GTF format. In this case, the quantification files of transcripts come from pseudoaligners (examples: salmon or kallisto). As mentioned above, EventPointer consists of several core steps: (i) building the splicing graph, (ii) detecting and classifying events, (iii) quantification, and (iv) performing statistical analysis. Finally, users can visualize the results with IGV by files generated by EventPointer. The Figure 3 shows the pipeline of EventPointerBAM and EventPointerST: ![Figure 3 (A) Worflow and advantages and disadvantages of EventPointerBAM. (B) Worflow and advantages and disadvantages of EventPointerST.](EPST_EPBAM.png) ## EventPointerBAM The aim of `EventPointerBAM` is to perform detection, classification, quantification and differential analysis between conditions, using as input data sequence alignments of RNA-seq data performed using aligners sensitive to junction reads. There are two requirements for the BAM files in order to get `EventPointerBAM` working correctly: 1. The BAM files should include the **XS-flag**, the flag can be included in the files when running the alignment. Most of the available software has parameters to incude the flag. For example, in the case of [STAR](https://github.com/alexdobin/STAR) the flag *--outSAMattributes XS* must be included when mapping. 2. All files to be analyzed should have the corresponding index files **(.bai)** in the same directory as the BAM files. Create the index before running EventPointer. To demonstrate the use of EventPointer, we utilise synthetically generated FASTQ files containing genes with predefined alternative splicing events, including alternative 3' sites, alternative 5' sites, and cassette exons. These FASTQ files are first processed with the STAR aligner to generate the BAM files required as input for EventPointer. In order to reduce computation time in this example, the data have been limited to genes from chromosome 3 with positive strand (strand +). ### Events Detection The function **EventsDetection_BAM()** is responsible for the detection of events from the input alignment files, their classification and the calculation of the associated PSI. The inputs for the function are described below: *Bams and GTF file parameters* - **PathSamplesAbundance**: Path to bam and bai files or path to folder with bam and bai files. - **PathTranscriptomeGTF**: Path to file containing the regions to be analysed from the bam files in GTF format. *Detection parameters* - **region**: Numerical vector indicating the index of positions (at chromosomal level) to be analysed from the GTF. Default value is NULL so that all regions are analysed. Note that the index position depends on the bam file. In order to see which is the specific index of the region of interest we can use the `Rsamtools` library: ```{r} path_bam_files = system.file("extdata/bams", package = "EventPointer") path_file_1 = dir(path_bam_files,pattern = "*.bam$",full.names = TRUE)[1] file_tmp <- BamFile(path_file_1) si <- seqinfo(file_tmp) sl <- rep(seqlevels(si),2) st <- c(rep(c("+"), length(si)),rep(c("-"), length(si))) ranges_info <- GRanges(sl, IRanges(1, seqlengths(si)[sl]), st) ``` ```{r,echo=FALSE} ranges_info = as.data.frame(ranges_info) ranges_info$index = 1:nrow(ranges_info) ranges_info = ranges_info[,c(6,1:5)] levels(ranges_info$strand) = c("p","n","o") # ranges_info kable(ranges_info) %>% kable_paper() %>% scroll_box(width = "500px", height = "200px") ``` In this case, as we are interested in the chr3 with + starnd, we want the region 16. Therefore, we will set the parameter `region` to 16. - **min_junction_count**: Minimum number of junctions detected in the alignment to be considered in the splicing graph. Default value is 2. - **max_complexity**: Maximum allowed complexity. If a locus exceeds this threshold, it is skipped, resulting in a warning. Complexity is defined as the maximum number of unique predicted splice junctions overlapping a given position. High complexity regions are often due to spurious read alignments and can slow down processing. Default value is 30. - **min_n_sample**: Minimum number of samples that a junction must have to be considered. Default value is 3. - **min_anchor**: Minimum number of aligned bases at one end of an exon to consider a junction. Default value is 6. *PSI calculation parameters* - **nboot**: Number of resamples of the quantification of the samples to perform bootstrap. Default value is 20. - **lambda**: *Performance parameters* - **cores**: Nº of cores used for the function. Default value is 1. - **verbose**: Logical indicating whether to show warnings and messages. If FALSE, warnings from internal functions (e.g., makeTxDbFromGRanges) will be suppressed. Default is FALSE. *Results parameters* - **PathSGResult**: Path where to save the following 4 files: - **SgFC.RData**: Contains the splicing graph in RData format. - **TotalEventsFound.csv**: General data for total events detected in csv format. - **EventsDetection_EPBAM.RData**: Raw data per event, paths of splicing graph and counts in RData format. - **PSI_boots.RData**: $\Psi$ per event and sample in RData format. The following code chunk describe how to use this function: ```{r, eval=FALSE, warning=FALSE, collapse=TRUE} PathSamplesAbundance <- system.file("extdata/bams", package = "EventPointer") PathTranscriptomeGTF <- list.files(PathSamplesAbundance,"*.gtf",full.names = T) PathSGResult <- tempdir() EventsDetection_BAM(PathSamplesAbundance, PathTranscriptomeGTF, region = 16, min_junction_count = 2, max_complexity = 30, min_n_sample = NULL, min_anchor = 6, nboot = 20, lambda = NULL, cores = 1, PathSGResult = PathSGResult) ``` The 4 files created by this function are: - **SgFC.RData**: Contains the needed information to create the splicing graph. - **TotalEventsFound.csv**: General data for total events detected in csv format. - **EventsDetection_EPBAM.RData**: Raw data per event, paths of splicing graph and counts in RData format. - **PSI_boots.RData**: $\Psi$ per event and sample in RData format. A further explanation of these files are depicted in the following subsecction "step-by-step" #### step-by-step As mentioned above, the function `EventsDetection_BAM` unifies three main steps. As each task has a long duration and it may be of interest to a user to perform any of these tasks separately, the following section explains how to execute this function step by step. **get Information for the Splicing graph** The first step of `EventsDetection_BAM` is to get information to build the splicing graph. For this, the function `PrepareBam_EP` is called. The parameters of this functions are depicted above. ```{r,eval=FALSE} PathSamplesAbundance <- system.file("extdata/bams", package = "EventPointer") PathTranscriptomeGTF <- list.files(PathSamplesAbundance,"*.gtf",full.names = T) PathSGResult <- tempdir() SgFC <- PrepareBam_EP(PathSamplesAbundance = PathSamplesAbundance, PathTranscriptomeGTF = PathTranscriptomeGTF, region = 16, min_junction_count = 2, max_complexity = 30, min_n_sample = NULL, min_anchor = 6, cores = 1, PathSGResult = PathSGResult, verbose = FALSE) ``` This functions is the most demading regarding time computing. The SgFC object can be laoded from the EventPointer package:

```{r,eval=TRUE} load(system.file("extdata/SgFC.RData", package = "EventPointer")) SgFC ```
The SgFC object is a SGFeatureCounts object. for more information check [SGSeq Vignette](https://bioconductor.org/packages/release/bioc/vignettes/SGSeq/inst/doc/SGSeq.html). Briefly the SGFeaturesCounts contains a GRanges object with all the required elements to create the different splicing graphs found in the given samples. It also contains the number of counts associated with each element of the splicing graph. **Event Detection** The second step of `EventsDetection_BAM` takes the SgFC object to create the splicing graph and then detect and classify splicing events. This is done by calling the function `EventDetection`. This function only requieres two parameters: the SgFC object, and the number of cores: ```{r,eval=FALSE} EventsDetection_EPBAM<-EventDetection(SgFC, cores=1) ``` ```{r,echo=FALSE} load(system.file("extdata/EventsDetection_EPBAM.RData", package = "EventPointer")) ``` The function retunes a list called EventsDetection_EPBAM that corresponds to the `EventsDetection_EPBAM.RData` generated by `EventsDetection_BAM` function.
The output is a list vector with one element for each gene. Each gene (element of the list) is itself a list with a length equal to the number of events in that gene. Finally, each event is also a list containing the event information. In order to see the values of this object, the follownig code chunk show an example of how to see the information of the first event of the first gene. ```{r,eval=FALSE} index_gene <- 1 index_event <- 1 length(EventsDetection_EPBAM) #number of genes length(EventsDetection_EPBAM[[index_gene]]) #number of events in the corresponding gene names(EventsDetection_EPBAM[[index_gene]][[index_event]]) #the different information stored in the object. ```

In order to replicate the `.csv` file stored by the `EventsDetection_BAM` function, you can use the following code chunk: ```{r,eval=FALSE} event_list <- list() index <- 1 for (geneEvent in EventsDetection_EPBAM) { for (event in geneEvent) { event_list[[index]] <- event$Info index <- index + 1 } } totalEventTable <- do.call(rbind, event_list) write.csv(totalEventTable, file = paste0(PathSGResult, "/TotalEventsFound.csv"), row.names = FALSE) ```

**Get PSI** Finally, the `EventsDetection_BAM` function compute the PSI values by calling the function `getPSI_RNASeq_boot` which needs the following parameters: lambda, and nboot (depicted above). ```{r,eval=FALSE} cores <- 1 lambda <- NULL nboot <- 20 PSI_boots <- getPSI_RNASeq_boot(EventsDetection_EPBAM, lambda = lambda, cores = cores, nboot = nboot) ``` PSI_boots is an array of number_of_events x (number_of_boots+1) x number_of_samples. In the second dimension, we have the PSI values corresponding to the maximum likelihood estimation of the expression and the PSI corresponding to the nboot bootstraps.
### Statistical Analysis The main function to perform the statistical analysis is `EventPointerStats_BAM`. ```{r} data(PSI_boots) Design <- cbind(rep(1, 9), rep(c(1, 0, 0), 3), rep(c(0, 1, 0), 3)) Contrast <- cbind(c(0, 1, 0), c(0, 0, 1)) PathSGResult <- tempdir() result = EventPointerStats_BAM(PSI_boots, Design, Contrast, Threshold = 0, nbootstraps = 1000, cores = 1, ram = 0.1, pathResult = PathSGResult) ``` `EventPointerStats_BAM` returns a list with the reulsts of the statistical analysis. The list contains the summary of the Bootstrap analysis: deltaPSI, Pvalues, iqr_events, median_events, and LocalFDR. The first for are data.frame with the corresponding information for each event (row) and contrast. LocalFDR is a list of length equal to the number of contrast. Each elemnt has information of the local flase dicovery rate. Brielfy: - **deltaPSI**: the increment of PSI of each event in each contrast. - **Pvalues**: p.values obteined from the bootstrap test. - **iqr_events**: - **median_events**: - **LocalFDR**: multiple hypothesis correction information. `EventPointerStats_BAM` also return files with the results of the statistical analysis for each contrast in a .csv file. ### IGV visualization Alternative splicing events detected by EventPointerBAM can be visualized using the `EventPointerBAM_IGV()` function as shown in Figure 4. This function displays the paths that make up the splicing graph of each event in a gtf format that can be viewed using the IGV program. Each event is composed of 3 paths: path1, path2, and Reference, which will be indicated along with the event id for identification as "path1\_", "path2\_", "Ref_1", and "Ref_2". To create the GTF files, the algorithm uses the `EventPointerBAM_IGV()` function that has the following parameters: *Events and reference transcriptome parameters* - **SG_RNASeq**: Data.frame generated by `EventsDetection_BAM` with the events to be included in the GTF file. - **EventsTxt**: Reference transcriptome. Must be one of: "Ensembl", "UCSC", "AffyGTF" or "CustomGTF". *Results parameters* - **PathGTF**: Folder where to save the generated GTF file. ```{r, eval=FALSE, collapse=TRUE} EventsTxt<-paste(system.file("extdata",package="EventPointer"),"/TotalEventsFound.csv",sep="") load(paste(system.file("extdata",package="EventPointer"),"/SgFC.RData",sep="")) SG_RNASeq <- SgFC PathGTF <- tempdir() EventPointerBAM_IGV(SG_RNASeq = SgFC, EventsCSV = EventsTxt, PathGTF = "./") ``` The GTF file can be loaded in IGV and will display as following: ![**Figure 4.** EventPointer visualization using IGV.](4_IGV.png) ### summary This approach of EventPointer can be run with tow main functions: `EventsDetection_BAM()` and `EventPointerStats_BAM()`. The former functin can be run step by step by using the function: `PrepareBam_EP`, `EventDetection`, and `getPSI_RNASeq_boot`.
When running both `EventsDetection_BAM()` and `EventPointerStats_BAM()`, the resulting files at the defined output folder will be those shown in Figure 5. ![Figure 5. Output EventPointerBAM functions.](3_ficheros_EPBAM.drawio.png) Finally, results can be visualized using the function `EventPointerBAM_IGV`. ## EventPointerST In this pipeline alternative splicng events are detected from a **reference transcriptome without finding novel events**. The events quantification relies on isoform expression estimate from pseudo-alignment process such as *Kallisto* or *Salmon*. Besides, we provide a function to leverage the bootstrap data from *kallisto* or *salmon*. Preveous statistical analysis have also been adapted to this data. Further, Primers design for PCR validation and protein domain enrichment analysis can be performed. Figure 6 shows an overview of this branch of EventPointer. ![**Figure 6.** Analysis of RNA-Seq data with kallisto output](Figure7_2.png) ### Event Detection We use `EventDetection_transcriptome` to identify and classify alternative splicing events of a given reference transcriptome. The required parameters are: - **inputFile** = inputFile should point to the GTF file to be used. - **Pathtxt** = Directory to save the .txt file of the events found. - **cores** = Number of cores using in the parallel processing (by default = 1). Following code shows how to apply `EventDetection_transcriptome` function using the Gencode 24 transcriptome (GRCH 38) as reference annotation. In this example we do not use the full reference but only two genes (ENSG00000185252.17 and ENSG00000254709.7). ```{r GTFfGTF, eval=TRUE, warning=FALSE, collapse=TRUE} # Set input variables PathFiles<-system.file("extdata",package="EventPointer") inputFile <- paste(PathFiles,"/gencode.v24.ann_2genes.gtf",sep="") Pathtxt <- tempdir() ``` ```{r GTFfGTF_2, eval=FALSE, warning=FALSE, collapse=TRUE} # Run the function EventXtrans <- EventDetection_transcriptome(inputFile = inputFile, Pathtxt=Pathtxt, cores=1) ``` This function takes a couple of hours to complete (depending on the computer), and creates a file called **EventsFound.txt**. This file contains the information of the events. This .txt file will be used as an input in further functions. The object returned by the function is a list containing five elements: three sparce matrices that relate which isoforms build up the paths (path1,path2 and pathRef) of each event. The fourth element contains the name of the reference annotation: only appear the name of the transcript. The final element is SG_List: a list with the information of the graph of each gene, this variable is necesary for Primers design step. ```{r GTFfGTFnames, eval=FALSE, warning=FALSE, collapse=TRUE} names(EventXtrans) ``` **NOTE**: as the events are detected based on the reference transcriptome, the function `EventDetection_transcriptome` can be applied only once for each reference transcriptome. ### Get expression and PSI ($\Psi$) Given (I) the output of `EventDetection_transcriptome`, which contains events information and (II) the result of pseudo-alignment, EventPointer is able to get the expression of the path of each event and also to compute the PSI value. To first step is to load the data from pseudo-alignment. EventPointer works with both *Kallisto* and *Salmon* methods. In this example we have applied *kallisto* to the samples ERR315326, ERR315400, ERR315407 and ERR315494 using the transcriptome reference used before. These samples have been download from *EMBL-EBI* dataset. EventPointer offers the option to take advantage of bootstraps that return both Kallisto and Salmon. In case we want to leverage the Bootstrap data we have to use the function `getbootstrapdata`: ```{r getbootstrpdata, eval=TRUE, warning=FALSE, collapse=TRUE} PathSamples<-system.file("extdata",package="EventPointer") PathSamples <- paste0(PathSamples,"/output") PathSamples <- dir(PathSamples,full.names = TRUE) data_exp <- getbootstrapdata(PathSamples = PathSamples,type = "kallisto") ``` `getbootstrapdata` returns a list containing the quantification data with the bootstrap information. The list is of length equal to the number of samples. Each element of the list storage a matrix where the number of rows is equal to the number of transcripts and the number of columns is equal to the number of bootstrats + 1. (the first column corresponds to the maximum likelihood expression and the rest to the bootstrap data). In case **you don't want to use the bootstrap returned by the** **pseudo-alignmnet step (or is no available)**, you should load a matrix with the expression of the transcripts. The following code chunk shown an example of how to do this: ```{r getdata_nb, eval=TRUE, warning=FALSE, collapse=TRUE} # this code chunk is an example of how to load the data from kallisto output. # the expression of the isoforms are counts PathFiles <- system.file("extdata",package="EventPointer") filesnames <- dir(paste0(PathFiles,"/output")) PathFiles <- dir(paste0(PathFiles,"/output"),full.names = TRUE) dirtoload <- paste0(PathFiles,"/","abundance.tsv") RNASeq <- read.delim(dirtoload[1],sep = "\t", colClasses = c(NA,"NULL","NULL",NA,"NULL")) for (n in 2:length(dirtoload)){ RNASeq[,n+1] <- read.delim(dirtoload[n],sep = '\t', colClasses = c('NULL','NULL','NULL',NA,'NULL')) } rownames(RNASeq)<-RNASeq[,1] RNASeq<-RNASeq[,-1] colnames(RNASeq) <- filesnames ``` Once we have the expression of the isoforms loaded, the next step is to compute the PSI value. For both cases (with and withuot bootstrap data from psudoaligment) EventPointer provides the function `GetPSI_FromTranRef` for this purpose. The function `GetPSI_FromTranRef` returns the values of $\Psi$ and the expression of the paths of each events. This functions requires the following inputs: - **PathsxTranscript** The output of EventDetection_transcriptome. - **Samples** A matrix or list containing the expression of the samples. - **Bootstrap** Boolean variable to indicate if bootstrap data from pseudo-alignment is used. - **Filter** Boolean variable to indicate if an expression filter is applied. Defaul TRUE. - **Qn** Quantile used to filter the events (Bounded between 0-1, Q1 would be 0.25). This function also requires that the same reference transcriptome have been used in both the pseudo-alignment and event detections steps. Besides, in the variable *Samples* the type of annotation used must be equal to the one used in the variable *EventXtrans*. **Get psi if NO bootstrap from pseudo-alignmnet is used** First step is to verify if same annotation is used in *RNASeq* and *EventXtrans*: ```{r check_ann_nb, eval=TRUE, warning=FALSE, collapse=TRUE} rownames(RNASeq)[1:5] EventXtrans$transcritnames[1:5] ``` We need to change the rownames of RNASeq variable before applying `GetPSI_FromTranRef` function: ```{r PSI_Statistic, eval=TRUE, warning=FALSE, collapse=TRUE} #change rownames of RNASeq variable rownames(RNASeq) <- sapply(strsplit(rownames(RNASeq),"\\|"),function(X) return(X[1])) RNASeq<-as.matrix(RNASeq) #must be a matrix variable PSIss_nb <- GetPSI_FromTranRef(PathsxTranscript = EventXtrans, Samples = RNASeq, Bootstrap = FALSE, Filter = FALSE) PSI <- PSIss_nb$PSI Expression <- PSIss_nb$ExpEvs ``` The output of the function is a list containing two elements: a matrix with the $\Psi$ values, and a list containing as many matrices as number of events. In each matrix is stored the expression of the different paths of the correspondig event along the samples. **If bootstrap data from pseudo-alignment IS required** As done above, we need to check if same annotation is used in *data_exp* and *EventXtrans*. The difference from above is that *data_exp* is a list and not a matrix as *RNASeq*. Therefore, we are going to used the rownames of the first list of *data_exp* to check if same annotations is used: ```{r check_ann, eval=TRUE, warning=FALSE, collapse=TRUE} rownames(data_exp[[1]])[1:5] EventXtrans$transcritnames[1:5] ``` We need to change the rownames of the first element of the list *data_exp* before applying `GetPSI_FromTranRef` function: ```{r psi_with_bootstrap, eval=TRUE, warning=FALSE, collapse=TRUE} #change rownames of the first element of teh list data_exp rownames(data_exp[[1]]) <- sapply(strsplit(rownames(data_exp[[1]]),"\\|"),function(X) return(X[1])) PSIss <- GetPSI_FromTranRef(PathsxTranscript = EventXtrans, Samples = data_exp, Bootstrap = TRUE, Filter = FALSE) PSI <- PSIss$PSI Expression <- PSIss$ExpEvs ``` The output of the function is a list containing two elements: an array with the $\Psi$ values with dimension = c(number of events, number of bootstrap+1,number of samples), and a list containing as many matrices as number of events. In each matrix is stored the expression of the different paths of the corresponding event along the samples. ### Statistical Analysis (Bootstrap Test) We have implemented a bootstrap test to evaluate the difference in $\Psi$ among the conditions under study. This test can be done both for PSI values that contain information from the pseudo-alignment bootstrap and for cases in which we do not have this information. The function `EventPointer_Bootstraps` proceed to calculate the increase in PSI and its p.values by performing bootstrap statistic test. This function can be parallelized. Users may set the number of Cores (by default 1) and the ram available (by default 1 Gb). ```{r ep_bootstrp_statistic, eval=TRUE, warning=FALSE, collapse=TRUE} Dmatrix <- cbind(1,rep(c(0,1),each=2)) Cmatrix <- matrix(c(0,1),nrow=2) Fit_bots <- EventPointer_Bootstraps(PSI = PSI, Design = Dmatrix, Contrast = Cmatrix, cores = 1, ram = 1, nbootstraps = 10, UsePseudoAligBootstrap = TRUE) ``` You can extract a table of the top-ranked events from the results with the function *ResulTable* ```{r ResultTable, eval=TRUE, warning=FALSE, collapse=TRUE} ResulTable(EP_Result = Fit_bots,coef = 1,number = 5) ``` ### Statistical Analysis (NO bootstrap) The statistical analysis of the alternative splicing events is done in exactly the same way as for junction arrays (section 4). The function `EventPointer_RNASeq_TranRef` perform this statistical analysis and requires the following parameters: - **Count_Matrix** The list containing the expression data taken from the ouput of *GetPSI_FromTranRef* - **Statistic** The type of statistic to apply. Default = "LogFC" (can be "logFC,"Dif_LogFC","DRS") - **Design** The design matrix of the experiment. - **Contrast** The Contrast matrix of the experiment. ```{r PSI_Statistic2, eval=TRUE, warning=FALSE, collapse=TRUE} # Design and contrast matrix: Design <- matrix(c(1,1,1,1,0,0,1,1),nrow=4) Contrast <- matrix(c(0,1),nrow=2) # Statistical analysis: Fit <- EventPointer_RNASeq_TranRef(Count_Matrix = Expression,Statistic = "LogFC",Design = Design, Contrast = Contrast) ``` The output of this function is a data.frame with the information of the names of the event, its p.values and the corresponding z.value. If there is more than one contrast, the function returns as many data.frames as number of contrast and all these data.frame are sotred in an unique list. ```{r EP_TranRef_Res_Table, echo=FALSE} kable(Fit,digits=5,row.names=FALSE,align="c",caption = "Table 3: PSI_Statistic results") ``` ### IGV visualization EventPointer creates one GTF file that can be loaded into IGV to visualize the alternative splicing events. Figure XX displays an example result showed in IGV (5th ranked event in Table 2). Also, in the figure a reference transcriptome is displayed (blue track). To create the GTF files, the algorithm uses the `EventPointer_RNASeq_TranRef_IGV` functions with the following parameters: - **SG_List**: List with the Splicing Graph information of the events. This list is created by `EventDetection_transcriptome` function. - **pathtoeventstable**: Complete path to the table returned by `EventDetection_transcriptome` that contains the information of each event, or table with specific events that the user want to load into IGV to visualize. - **PathGTF**: Directory where to write the GTF files. As `EventDetection_transcriptome` function returns the splicing graph information, this function does not need to create the splicing graph. ```{r ep_tranref_igv, eval=TRUE, warning=FALSE, collapse=TRUE} SG_List <- EventXtrans$SG_List PathEventsTxt<-system.file('extdata',package='EventPointer') PathEventsTxt <- paste0(PathEventsTxt,"/EventsFound_Gencode24_2genes.txt") PathGTF <- tempdir() EventPointer_RNASeq_TranRef_IGV(SG_List = SG_List,pathtoeventstable = PathEventsTxt,PathGTF = PathGTF) ``` ### Domain Enrichment The main idea of protein domain analysis is to analyze whether the presence of a protein domain increases or decreases in the condition under study. To do this, we have implemented the function `Protein_Domain_Enrichment`. This function requires the following parameters: - **PathsxTranscript**: the output of EventDetection_transcriptome. - **TxD**: matrix that relates transcripts with Protein domain. Users can get it from BioMart. - **Diff_PSI**: matrix with the difference of psi of the condition under study. - **method**: a character string indicating which correlation coefficient is to be calculated. "spearman" (default) or "pearson" can be selected. As mentioned, it is first necessary to know the structure of protein domains within the transcriptome or in other words: to relate the isoforms to protein domains. This can be done using `bioMart` R package. The input needed by EP to perform this analysis is a matrix that relates transcripts to protein domains (TxD matrix). Therefore, you can work either with Pfam, Interpro or superfamily annotation. The following conde chunck shows an example of how to get this TxD matrix: ```{r biomart, eval=FALSE, warning=FALSE, collapse=TRUE} library(biomaRt) mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "mar2016.archive.ensembl.org") mart<-useDataset("hsapiens_gene_ensembl",mart) mistranscritos <- EventXtrans$transcritnames head(mistranscritos) ## we need to remove the ".x": mistranscritos <- gsub("\\..*","",mistranscritos) Dominios <- getBM(attributes = c("ensembl_transcript_id","interpro","interpro_description"), filters = "ensembl_transcript_id", values = mistranscritos, mart=mart) #we build the isoform x protein domain matrix library(Matrix) ii <- match(Dominios$ensembl_transcript_id,mistranscritos) misDominios <- unique(Dominios[,2]) jj <- match(Dominios[,2],misDominios) TxD <- sparseMatrix(i=ii,j = jj,dims = c(length(mistranscritos),length(misDominios)),x = 1) rownames(TxD) <- mistranscritos colnames(TxD) <- misDominios ``` The above code chunk build the required Transcript X Domain (TxD) sparse matrix where $TxD_{ij}$ = 1 means that Domain $j$ match to transcript $i$. The next step is to perform the enrichment analysis with the function *Protein_Domain_Enrichment* Internally, given the TxD matrix *Protein_Domain_Enrichment* relates protein domains to events and performs the enrichment analysis: **NOTE**: Check that EventXtrans\$transcritnames annotation type match to the rownames of the TxD matrix. ```{r pd_enrichmetn, eval=FALSE, warning=FALSE, collapse=TRUE} data("TxD") #check same annotation for transcripts: EventXtrans$transcritnames[1] # [1] "ENST00000611540.4" rownames(TxD)[1] # [1] "ENST00000611540" ## as is not the same, we change EventXtrans$transcritnames annotation transcriptnames <- EventXtrans$transcritnames transcriptnames <- gsub("\\..*","",transcriptnames) EventXtrans$transcritnames <- transcriptnames Result_PDEA <- Protein_Domain_Enrichment(PathsxTranscript = EventXtrans, TxD = TxD, Diff_PSI = Fit_bots$deltaPSI) ``` The function returns a list containing the results of the protein domain enrichment analysis. This list contains 3 matrices in which the rows indicate the protein domains and the columns the number of contrasts. The 3 matrices are the following: - **mycor**: correlation value between the deltaPSI and the DifProtDomain matrix (see more details in vignette) - **STATISTIC**: the values of the test statistic - **PVAL**: the pvalues of the test statistic ### Primers Design EventPointer can also be used to design primers for PCR validation. The aim of *FindPrimers* function is the design of PCR primers and TaqMan probes for detection and quantification of alternative splicing. Depending on the assay we want to carry out the the algorithm will design the primers for a conventional PCR or the primers and TaqMan probes if we are performing a TaqMan assay. In the case of a conventional PCR we will be able to detect the alternative splicing event. Besides, the algorithm gives as an output the length of the PCR bands that are going to appear. In the case of a TaqMan assay, we will not only detect but also quantify alternative splicing.

The Primers Design step has been developed with *Primer3* software and works with versions \>= 2.3.6. In order to use this option you need to install this sofware. You can download from sourceforge (for Windows, Mac OSX or Unix/Linux) or from github (for Mac OSX or Unix/Linux).

To work with Primer Design option you also need to add *Primer3* to your environment PATH. For Windows you can add environment variables in:

Control Panel -\> System and Security -\> System -\> Advanced system settings -\> Environment Variables...

For Unix/Linux or Max you need to add the path to *Primer3* to the variable PATH using terminal.

Then, as is explained below, one of the input variables for the main function is **Primer3Path** which is a string variable with the complete path where primer3_core.exe is placed. As we have added this path to the environment variable we can set down this variable with the following command as is shown later in the example: ```{r pathprimer3, eval=FALSE, warning=FALSE, collapse=TRUE} Primer3Path <- Sys.which("primer3_core") ``` The main function of this step is called `Find_Primers` and requires the following main parameters: - **SG**: Information of the graph of the gene where the selected event belongs.This information is available in the output of EventsGTFfromTranscriptomeGTF function. - **EventNum**: The "EventNum" variable can be found in the returned .txt file from the EventsGTFfromTranscriptomeGTF function in the column "EventNumber" or in the output of EventPointer_RNASeq_TranRef, the number after the "\_" character of the 'Event_ID'. - **Primer3Path**: Complete path where primer3_core.exe is placed. - **Dir**: Complete path where primer3web_v4_0_0_default_settings.txt file and primer3_config directory are placed. - **taqman**: 1 if you want to get probes and primers for TaqMan. 0 if you want to get primers for conventional PCR. Other required parameters that have a default value but can be modified by user: - **nProbes**: Number of probes for TaqMan experiments. By default 1. - **nPrimerstwo**: Number of potential exon locations for primers using two primers (one forward and one reverse). By default 3. - **ncommonForward**: Number of potential exon locations for primers using one primer in forward and two in reverse. By default 3. - **ncommonReverse**: Number of potential exon locations for primers using two primer in forward and one in reverse. By default 3. - **nExons**: Number of combinations of ways to place primers in exons to interrogate an event after sorting. By default 5. - **nPrimers**: Once the exons are selected, number of primers combination sequences to search within the whole set of potential sequences.By default 5. - **shortdistpenalty**: Penalty for short exons following an exponential function. By default 2000. - **maxLength**: Max length of exons that are between primers and for paths once we have calculated the sequence.By default 1000. - **minsep**: Distance from which it is penalized primers for being too close. By default 100. - **wminsep**: Weigh of the penalization to primers for being too close. By default 200. - **valuethreePenalty**: penalization for cases that need three primers instead of 2. By default 1000. - **minexonlength**: Minimum length that a exon has to have to be able to contain a primer. By default 25. - **wnpaths**: Penalty for each existing path. By default 200. - **qualityfilter**: Results will show as maximum 3 combinations with a punctuation higher than qualityfilter.By default 5000. The following example shows how to design primers and TaqMan probes for a specific alternative splicing event: ```{r FindPrimers, eval=FALSE, warning=FALSE, collapse=TRUE} data("EventXtrans") #From the output of EventsGTFfromTranscriptomeGTF we take the splicing graph information SG_list <- EventXtrans$SG_List #SG_list contains the information of the splicing graphs for each gene #Let's supone we want to design primers for the event 1 of the gene ENSG00000254709.7 #We take the splicing graph information of the required gene SG <- SG_list$ENSG00000254709.7 #We point the event number EventNum <- 1 #Define rest of variables: Primer3Path <- Sys.which("primer3_core") Dir <- "C:\\PROGRA~2\\primer3\\" MyPrimers_taqman <- FindPrimers(SG = SG, EventNum = EventNum, Primer3Path = Primer3Path, Dir = Dir, mygenomesequence = BSgenome.Hsapiens.UCSC.hg38::Hsapiens, taqman = 1, nProbes=1, nPrimerstwo=4, ncommonForward=4, ncommonReverse=4, nExons=10, nPrimers =5, maxLength = 1200) ``` *FindPrimers* return a data.frame. Either for conventional PCR and taqman option the data.frame has the following columns: - **For1Seq**: Sequence of the first forward primer. - **For2Seq**: Sequence of the second forward primer in case it is needed. - **Rev1Seq**: Sequence of the first reverse primer. - **Rev2Seq**: Sequence of the second reverse primer in case it is needed. - **For1Exon**: Name of the exon of the first forward primer. - **For2Exon**: Name of the exon of the second forward primer in case it is needed. - **Rev1Exon**: Name of the exon of the first reverse primer. - **Rev2Exon**: Name of the exon of the second reverse primer in case it is needed. - **FINALvalue**: Final punctuation for that combination of exons and sequences. The lower it is this score, the better it is the combination. - **DistPath1**: Distances of the bands, in base pairs, that interrogate Path1 when we perform the conventional PCR experiment. - **DistPath2**: Distances of the bands, in base pairs, that interrogate Path2 \`when we perform the conventional PCR experiment. - **DistNoPath**: Distances of the bands, in base pairs, that they do not interrogate any of the two paths when we perform the conventional PCR experiment. ```{r EP_DesiCP_Res_Table, eval=TRUE, warning=FALSE, collapse=TRUE,echo=FALSE} data("MyPrimers") kable(MyPrimers[1:5,],digits=5,row.names=FALSE,align="c",caption = "Table 4: Data.frame output of FindPrimers for conventional PCR") %>% kable_styling() %>% scroll_box(width ="660px") ``` For the tacman option, the data.frame contains three additional columns: - **SeqProbeRef**: Sequence of the TaqMan probe placed in the Reference. - **SeqProbeP1**: Sequence of the TaqMan probe placed in the Path1. - **SeqProbeP2**: Sequence of the TaqMan probe placed in the Path2. ```{r EP_DesiTP_Res_Table, eval=TRUE, warning=FALSE, collapse=TRUE,echo=FALSE} data("MyPrimers_taqman") kable(MyPrimers_taqman[1:5,],digits=5,row.names=FALSE,align="c",caption = "Table 5: Data.frame output of FindPrimers for conventional PCR") %>% kable_styling() %>% scroll_box(width ="660px") ``` #### IGV primers and probes visualization We can visualize the result in IGV using the *Find Motif* tool given the output of *FindPrimers* function. Forward primers are represented in blue whilst reverse primers are represented in red as long as the gene is transcribed from the forward strand. Otherwise the colors we will be swapped. TaqMan probes will always have the color of the forward primers. ![**Figure 7.** Transcriptome, paths and primers visualization using IGV.](Figure11.png) # Analysis of junction arrays ## Overview of junction arrays *EventPointer* is prepared to work with different Affymetrix arrays, such as: HTA 2.0, Clariom-D, RTA and MTA. To build the CDF file (to work under the aroma.affymetrix framework), EventPointer requires a GTF file with the reference transcriptome information. In case not provided, the algorithm automatically downloads the required information from different databases such as ENSEMBL or UCSC. As the probes for the HTA 2.0 array are mapped to the HG19 genome, the latests versions of the ensembl and ucsc genome, mapped to the hg19 version, will be downloaded. For the other arrays, the following genomes are used: ClariomD = GRCh38, MTA = mm10 and RTA = rn6. The required files are: 1. Exon probes genomic position (Tab separated .txt file) 2. Junction probes genomic position (Tab separated .txt file) 3. Reference transcriptome (GTF file) Files 1 and 2 contain probe information for the array. These files and the corresponding CDF files can be downloaded from the following URL: [EventPointer Dropbox](https://www.dropbox.com/sh/wpwz1jx0l112icw/AAAOU0M2QS0TjqHAhrrdEhp6a?dl=0) The format of these files is briefly explained in the following paragraphs: For the Exon Probes, the file corresponds to a tab separated .txt file composed of 11 columns that include: Probe Id, X coordinate in the array, Y coordinate in the array, Transcript Cluster Id, Probeset Id, Probeset name, Probe sequence, chromosome, start, end and strand. The Junction probes file is also a tab separated .txt composed of 10 columns: Probe Id, X coordinate in the array, Y coordinate in the array, Transcript Cluster Id, Probeset Id, Probeset name, Probe sequence, chromosome and probe alignments. For the GTF the standard format is used. (For more information see [GTF](http://mblab.wustl.edu/GTF22.html)) This vignette uses the probes and annotation file for the DONSON gene in order to run the examples in a short amount of time. To generate the corresponding CDF file for the whole genome, the files from the Dropbox link must be used. [**Note:**]{style="color:red"} It is advisable to work with reference GTF files, as the probes are annotated to them. However, if other database is used, the algorithm will only include probes that are mapped to such database. ## CDF file creation This step can be skipped if the corresponding CDF file is doownloaded from the Dropbox link. The available CDF files were generated using the GTF files for each of the arrays, if users want to generate a CDF for other databases (Ensembl or UCSC), this step should be used. The function **CDFfromGTF** generates the CDF file used afterwards in the aroma.affymetrix pre-processing pipeline. Internally, it calls flat2cdf function written by Elizabeth Purdom. More information about it can be seen in the following webpage: [Create CDF from scratch](http://www.aroma-project.org/howtos/create_CDF_from_scratch/) The required input for the function is described below: - **input** : Reference transcriptome. Available options are : "Ensembl","UCSC" , "AffyGTF" and "CustomGTF". - **inputFile**: If input is "AffyGTF" or "CustomGTF", inputFile should point to the GTF file to be used. - **PSR**: Path to the Exon probes txt file - **Junc**: Path to the Junction probes txt file - **PathCDF**: Directory where the output will be saved - **microarray**: Microarray used to create the CDF file. Must be one of: "HTA-2_0", "ClariomD", "RTA" or "MTA". This function takes a couple of hours to complete (depending on the computer), and creates the following files: 1. EventsFound.txt : Tab separated file with all the information of all the alternative splcing events found. 2. .flat file : Used to build the corresponding CDF file. 3. .CDF file: Output required for the aroma.affymetrix preprocessing pipeline. The following code chunks show examples on how to run the function using part of the Affymetrix GTF and the example data included in the package. This data corresponds to the Affymetrix HTA 2.0 GTF representing only the DONSON gene and the probes that are mapped to it. Using Affymetrix GTF as reference transcriptome ```{r CDFGTF, eval=FALSE, warning=FALSE, collapse=TRUE} # Set input variables PathFiles<-system.file("extdata",package="EventPointer") DONSON_GTF<-paste(PathFiles,"/DONSON.gtf",sep="") PSRProbes<-paste(PathFiles,"/PSR_Probes.txt",sep="") JunctionProbes<-paste(PathFiles,"/Junction_Probes.txt",sep="") Directory<-tempdir() array<-"HTA-2_0" # Run the function CDFfromGTF(input="AffyGTF",inputFile=DONSON_GTF, PSR=PSRProbes,Junc=JunctionProbes, PathCDF=Directory,microarray=array) ``` [**Note:**]{style="color:red"} Both the .flat and .CDF file take large amounts of space in the hard drive, it is recommended that the directory has at least 1.5 GB of free space. Figure 3 shows a screen shot with the output of the CDFfromGTF function ![**Figure 9.** Output of CDFfromGTF](Figure3.png) Once the file is created, the name of the cdf file can be changed. Internally, the algorithm gives the name as HTA-2_0, but the actual name of the file can be different. In the rest of the vignette, we have renamed our CDF file as EP_HTA-2_0. Once the CDF file has been created, it can be used for the analysis of different experiments. ## Statistical Analysis ### aroma.affymetrix pre-processing pipeline For microarray data, a pre-processing pipeline must be applied to the .cel files of the experiment. Usually this pre-processing is done using the aroma.affymetrix R package. This procedure normalizes and summarizes the expression of the different probesets into single values. The aroma.affymetrix R package provides users different functions to work with affymetrix arrays. The functions are used to extract all the information contained in the .cel files and to do all the required pre-processing steps such as background correction, normalization and summarization. The package requires a certain folder structure in order to work correctly. For more information about aroma.affymetrix visit the webpage:[aroma project](http://www.aroma-project.org/) The following code chunk displays the pipeline used to get the results required by EventPointer after the pre-processing using aroma.affymetrix. The code is not intended to be a runable example, but just to show users the settings and functions that can be used. In order for users to have a runable example, the corrrespoding folder structure and files are required. ```{r aroma, eval=FALSE} # Simple example of Aroma.Affymetrix Preprocessing Pipeline verbose <- Arguments$getVerbose(-8); timestampOn(verbose); projectName <- "Experiment" cdfGFile <- "EP_HTA-2_0,r" cdfG <- AffymetrixCdfFile$byChipType(cdfGFile) cs <- AffymetrixCelSet$byName(projectName, cdf=cdfG) bc <- NormExpBackgroundCorrection(cs, method="mle", tag=c("*","r11")); csBC <- process(bc,verbose=verbose,ram=20); qn <- QuantileNormalization(csBC, typesToUpdate="pm"); csN <- process(qn,verbose=verbose,ram=20); plmEx <- ExonRmaPlm(csN, mergeGroups=FALSE) fit(plmEx, verbose=verbose) cesEx <- getChipEffectSet(plmEx) ExFit <- extractDataFrame(cesEx, addNames = TRUE) ``` ### EventPointer function EventPointer is the main function of the algorithm The function requires the following parameters: - **Design** : The design matrix for the experiment. - **Contrast**: The contrast matrix for the experiment. - **ExFit**: [aroma.affymetrix] pre-processed variable after using extractDataFrame(affy, addNames=TRUE) - **Eventstxt**: Path to the EventsFound.txt file generated by CDFfromGTF function. - **Filter**: Boolean variable to indicate if an expression filter is applied. - **Qn**: Quantile used to filter the events (Bounded between 0-1, Q1 would be 0.25). - **Statistic**: Statistical test to identify differential splicing events, must be one of : "LogFC","Dif_LogFC" and "DRS". - **PSI**: Boolean variable to indicate if $\Delta \Psi$ should be calculated for every splicing event. If the `Filter` variable is `TRUE`, the following is performed: The summarized expression value of all the reference paths is obtained and the threshold is set depending on the `Qn` value used. An event is considered if at least one sample in all paths is expressed above the threshold. The rest of the events are not shown unless the `Filter` variable is set to `FALSE` ```{r EP_arrays, eval=TRUE} data(ArraysData) Dmatrix<-matrix(c(1,1,1,1,0,0,1,1),nrow=4,ncol=2,byrow=FALSE) Cmatrix<-t(t(c(0,1))) EventsFound<-paste(system.file("extdata",package="EventPointer"),"/EventsFound.txt",sep="") Events<-EventPointer(Design=Dmatrix, Contrast=Cmatrix, ExFit=ArraysData, Eventstxt=EventsFound, Filter=FALSE, Qn=0.25, Statistic="LogFC", PSI=TRUE) ``` Table 1 displays the output of EventPointer function ```{r EP_Arrays_Res_Table, echo=FALSE} kable(Events[1:5,],digits=5,row.names=TRUE,align="c",caption = "Table 1: EventPointer Arrays results") ``` The columns of the `data.frame` are: - **Gene name** : Gene identifier - **Event Type**: Type of alternative splicing event (Cassette, Alternative 3',Alternative 5', Alternative Last Exon, Alternative First Exon, Mutually Exclusive Exons or Complex Event) - **Genomic Position**: Genomic Coordinates for the event - **Splicing Z Value**: Corresponding Z value for the statistical test performed - **Splicing P Value**: Corresponding P-value for the statistical test performed - **Delta PSI**: $\Delta \Psi$ parameter for each event ## IGV visualization EventPointer creates two different GTF files to visualize the alternative splicing events. Figure 4 displays the cassette exon for the COPS7A gene (4th ranked event in Table 1). In the IGV visualization, the reference path is shown in gray color, the path 1 in red and path 2 in green. Below the transcripts, the different probes that are measuring each of the paths are represented in the same colors. ![**Figure 10.** EventPointer visualization using IGV](Figure5.png) To create the GTF files, the algorithm uses the EventPointer_IGV functions with the following parameters: - **Events** : Data.frame generated by EventPointer with the events to be included in the GTF file. - **input**: Reference transcriprome. Must be one of: "Ensembl", "UCSC" , "AffyGTF" or "CustomGTF". - **inputFile**: If input is "AffyGTF" or "CustomGTF", inputFile should point to the GTF file to be used. - **PSR**: Path to the Exon probes txt file. - **Junc**: Path to the Junction probes txt file. - **PathGTF**: Directory where to write the GTF files. - **EventsFile**: Path to EventsFound.txt file generated with CDFfromGTF function. - **microarray**: Microarray used to create the CDF file. Must be one of: HTA-2_0, ClariomD, RTA or MTA The inital process of the function is slow, as the splicing graphs must be created every time the function is executed. A progress bar is displayed to the user to inform about the progress of the function. Once the process is completed two GTF files are generated in the specified directory: 1. paths.gtf : GTF file representing the alternative splicing events. 2. probes.gtf : GTF file representing the probes that measure each event and each path. ![**Figure 11.** GTF files generated with EventPointer_IGV](Figure6.png) ```{r Arrays_IGV, eval=FALSE, collapse=TRUE} # Set Input Variables DONSON_GTF<-paste(PathFiles,"/DONSON.gtf",sep="") PSRProbes<-paste(PathFiles,"/PSR_Probes.txt",sep="") JunctionProbes<-paste(PathFiles,"/Junction_Probes.txt",sep="") Directory<-tempdir() EventsFound<-paste(system.file("extdata",package="EventPointer"),"/EventsFound.txt",sep="") array<-"HTA-2_0" # Generate Visualization files EventPointer_IGV(Events[1,,drop=FALSE],"AffyGTF",DONSON_GTF,PSRProbes,JunctionProbes,Directory,EventsFound,array) ``` # Multi-Path Events EventPointer can also identify Multi-Path events. The multi-path events are composed of more elements than a triplet where the concentration of the reference path should be equal to the sum of the concentration of the rest of paths. This is depicted in Figure 8. ![**Figure 12.** Multi-Path alternative splicing events for EventPointer](Figure10.png) EventPointer identify Multi-Path events and estimate the percent spliced in value $\Psi$. ## junctions arrays (CDF file for Multi-Path) The function *CDFfromGTF_Multipath* generates the CDF file used afterwards in the aroma.affymetrix preprocesing pipeline. This function is equivalent to the function *CDFfromGTF*. The function requires the following parameters: - **input** : Reference transcriptome. Available options are : "Ensembl","UCSC" , "AffyGTF" and "CustomGTF". - **inputFile**: If input is "AffyGTF" or "CustomGTF", inputFile should point to the GTF file to be used. - **PSR**: Path to the Exon probes txt file - **Junc**: Path to the Junction probes txt file - **PathCDF**: Directory where the output will be saved - **microarray**: Microarray used to create the CDF file. Must be one of: "HTA-2_0", "ClariomD", "RTA" or "MTA". - **paths**: Maximum number of paths of the events to find. The function *CDFfromGTF_Multipath* detects events with number of paths from 2 to variable *paths*. This function classifies the events with two paths as the *CDFfromGTF* function does and the events with more than two paths as *"Multi-Path"*. This function takes a couple of hours to complete (depending on the computer), and creates the same files as the function *CDFfromGTF*. The following code chunks show examples on how to run the function using part of the Affymetrix GTF and the example data included in the package. his data corresponds to the Affymetrix HTA 2.0 GTF representing only the DONSON gene and the probes that are mapped to it. Using Affymetrix GTF as reference transcriptome ```{r CDFGTF_MP, eval=FALSE, warning=FALSE, collapse=TRUE} # Set input variables PathFiles<-system.file("extdata",package="EventPointer") DONSON_GTF<-paste(PathFiles,"/DONSON.gtf",sep="") PSRProbes<-paste(PathFiles,"/PSR_Probes.txt",sep="") JunctionProbes<-paste(PathFiles,"/Junction_Probes.txt",sep="") Directory<-tempdir() array<-"HTA-2_0" # Run the function CDFfromGTF_Multipath(input="AffyGTF",inputFile=DONSON_GTF, PSR=PSRProbes,Junc=JunctionProbes, PathCDF=Directory,microarray=array,paths=3) ``` ##RNA-Seq (Event detection for Multi-Path) As for two-paths events, the only required files are the corresponding .BAM files from the experiment. After the *BAM Preparation* step explained in *section 4.2* we have all the elements needed to analyze each of the splicing graphs. To detect Multi-Path events, the function *EventDetectionMultipath* is used. This function requires the following parameters: - **Input** : Output of the PrepareBam_EP function - **cores**: Number of cores used for parallel processing - **Path**: Directory where to write the EventsFound.txt file - **paths**: Maximum number of paths of the events to find. The function *EventDetectionMultipath* detects events with number of paths from 2 to variable *paths*. This function classifies the events with two paths as the *EventDetection* function does and the events with more than two paths as *"Multi-Path"*. ```{r EventDetection_MP, eval=FALSE} # Run EventDetection function data(SG_RNASeq) TxtPath<-tempdir() AllEvents_RNASeq_MP<-EventDetectionMultipath(SG_RNASeq,cores=1,Path=TxtPath,paths=3) ``` This function retireves a list with all the events found for all the genes present in the experiment. It also generates a file called EventsFound_RNASeq.txt with the information for every detected event. The list is organized in the following way: ```{r ListofLists_MP, eval=FALSE} Events[[i]][[j]] ``` The list will have as many $i$ values as genes and $j$ values as many events detected for the $i_{th}$ gene. In other words, the command above will display the $j_{th}$ event detected for the $i_{th}$ gene. # Advanced Use ## Events Reclassification As described above, EventPointer is able to detect splicing events that cannot be classified as canonical splicing events. EventPointer classifies these events as "Complex Event". Within this category one can observe simple events such as 'Multiple Skipping Exons' and events whose structure is very complex. Thus, EP is classifying in the same category events that have nothing to do with each other.
EventPointer includes a function called `Events_ReClassification` to reclassify complex events. The input to this function is: - **EventsTable**: a data.frame with the event information. This data.frame can be generated by loading the .txt obtained using the EventDetection_transcriptome function (see example). - **SplicingGraph**: A list with the splicing graph of all the genes of a reference transcriptome. a reference transcriptome. This data is returned by the function EventDetection_transcriptome. - **cores**: Number of cores using in the parallel processing (by default = 1) Thus, EventPointer reclassifies the complex events according to how similar the event is to the struture canonical events. The same complex event can have several types (see). Further, EP adds a new type of event: "multiple skipping exon". These events are characterized by presenting several exons in a row as alternative exons. I.e., If there is only one alternative exon we would be talking about a "Casstte Exon".
The following code shows how this function works for a subset of 5 splicing events: ```{r reclassification, eval=TRUE} #load splicing graph data("SG_reclassify") #load table with info of the events PathFiles<-system.file("extdata",package="EventPointer") inputFile <- paste(PathFiles,"/Events_found_class.txt",sep="") EventTable <- read.delim(file=inputFile) #this table has the information of 5 complex events. EventTable_new <- Events_ReClassification(EventTable = EventTable, SplicingGraph = SG_reclassify) ``` The following figures show the structure of these events and their corresponding gene: **Example 1** ![**Figure 13.** Structure of example 1](example_1_alt5_ce.png) ## Statistical Tests EventPointer provides three different statistical tests that can be used to determine the statistical significance to the alternative splicing events. In Table 5, the most relevatn coefficients from the statistical and events information point of view are shown ![**Table 6.** Coefficients of the linear model used to detect alternative splicing](Figure9.png) There are a number of alternatives to detect AS using these coefficients. Either of $\beta_4$ , $\beta_5$ , $\beta_4$ + $\beta_5$ is theoretically able to detect AS events. Some of them are more sensitive than others depending on the relative concentrations of the isoforms. For example, if isoform 2 is much more highly expressed than isoform 1 in both conditions, $\beta_4$ will be more sensitive than $\beta_4$ + $\beta_5$ since in the latter case, the numerator and denominator of the logarithms of both terms are similar, and hence their logs are close to zero. A contrast on $\beta_5$ would seem to be more sensitive than a contrast on $\beta_4$ or $\beta_4$ + $\beta_5$ ; however, in practice, this contrast proved to be "too sensitive" and led to many false positives especially in weakly expressed isoforms. If one of the paths is not expressed in any condition, its signal will be similar in either condition (the background level) and a change in the expression of the other isoform will drive to a false positive detection. This contrast can be used only if the signals are filtered to guarantee that they are above the background. In the PCR validation, the contrast that provided the best performance was the combination of the fold changes of both isoforms (i.e. $\beta_3$ + $\beta_4$ and $\beta_3$+$\beta_4$ + $\beta_5$ in Table 4) plus the requirement that the fold-changes have opposite directions, i.e. if isoform 1 significantly increases its expression, isoform 2 must significantly decrease its expression and vice versa. Therefore, if this test requires that the detected AS events show a significant change of the expression both paths and this change must be in opposite direction. In order to compute this contrast, we summed up the p-values (one-tailed) for both contrasts. If the null hypothesis holds, the expected null distribution is triangular from 0 to 2 with the peak at 1, and the summation of the p-values must be close to 0 or close to 2 for genes with differential AS. Using this triangular distribution, it is possible to assign an overall p-value to their sum. We preferred this combination rather than the classical Fisher method since in the latter a single good p-value yields a good summary p-value for the event. Using this approach, both p-values must be close to zero or one in order to generate a significant overall p-value. The different options availabe in EventPointer are: **1. LogFC** : Compute the contrast using $\beta_3$ + $\beta_4$ and $\beta_3$+$\beta_4$ + $\beta_5$ **2. Dif_LogFC** : Compute the contrast using $\beta_4$ and $\beta_4$ + $\beta_5$ **3. DRS**: Compute the constast using $\beta_5$ ## Alpha parameter for PrepareBam_EP function Alpha is a parameter used by SGSeq R package to predict the features that are along the different bam files that are being analyzed. As stated in the help menu for the predictTxtFeatures function: **Alpha** Minimum FPKM required for a splice junction to be included. The user can change the value to be more or less restrictive when deciding if a feature is included or not. As the alpha value increases, the algorithm will slow down as the splicing graphs would became more complex. ## Percent Spliced In EventPointer estimates the abundance of the isoforms mapped to each of the paths, in an splicing event, to obtain the PSI values. With this values, a simple linear model, using the provided design and contrast matrices, is solved and this increment is returned to the user in the data.frame (if PSI argument is TRUE). It is possible to obtain the estimated PSI values using the internal functions *getPSI*,*getPSImultipath* for junction arrays, *getPSI_RNASeq* or *getPSI_RNASeq_MultiPath* for RNA-Seq data (data from the pipeline described in section *4.2: statistical analysis from de BAM files*). These functions not only calculate the value of $\Psi$ but also the residuals of the simple linear model used to estimate the values of $\Psi$. ```{r PSI_ADV, eval=TRUE, collapse=TRUE} # Microarrays (two paths) data(ArraysData) PSI_Arrays_list<-EventPointer:::getPSI(ArraysData) PSI_Arrays <- PSI_Arrays_list$PSI Residuals_Arrays <- PSI_Arrays_list$Residuals # Microarrays (Multi-Path) data(ArrayDatamultipath) PSI_MP_Arrays_list <- EventPointer:::getPSImultipath(ArrayDatamultipath) PSI_multipath_Arrays <- PSI_MP_Arrays_list$PSI Residuals_multipath_Arrays <- PSI_MP_Arrays_list$Residuals # RNASeq (two paths) data(AllEvents_RNASeq) PSI_RNASeq_list<-EventPointer:::getPSI_RNASeq(AllEvents_RNASeq) PSI_RNASeq <- PSI_RNASeq_list$PSI Residuals_RNASeq <- PSI_RNASeq_list$Residuals # RNASeq (Multi-Path) data(AllEvents_RNASeq_MP) PSI_MP_RNASeq_list <- EventPointer:::getPSI_RNASeq_MultiPath(AllEvents_RNASeq_MP) PSI_multipath_RNASeq <- PSI_MP_RNASeq_list$PSI Residuals_multipath_RNASeq <- PSI_MP_RNASeq_list$Residuals ``` We can apply the function *PSI_Statistic* to the values of $\Psi$ (only for two-paths events). This function takes as input the values of PSI and performs a statistical analysis based on permutation test. ```{r PSI_ADV2, eval=TRUE, collapse=TRUE} Dmatrix<-matrix(c(1,1,1,1,0,0,1,1),nrow=4,ncol=2,byrow=FALSE) Cmatrix<-t(c(0,1)) table <- PSI_Statistic(PSI = PSI_Arrays,Design = Dmatrix,Contrast = Cmatrix,nboot = 20) ``` The residual obtained in the linear model used to estimate the values of $\Psi$ must be independent from the Design matrix of the experiment. The data of the residuals that returns the internal functions *getPSI* or *getPSI_RNASeq* are useful to validate if this is true. The next code shows an example of how to perform a statistical analysis of the residuals. ```{r PSI_ADV3, eval=TRUE, collapse=TRUE} Dmatrix<-matrix(c(1,1,1,1,0,0,1,1),nrow=4,ncol=2,byrow=FALSE) Cmatrix<-t(t(c(0,1))) Ress <- vector("list", length = ncol(Cmatrix)) fitresiduals <- limma::lmFit(Residuals_Arrays,design = Dmatrix) fitresiduals2 <- limma::contrasts.fit(fitresiduals, Cmatrix) fitresiduals2 <- limma::eBayes(fitresiduals2) # repeated if there is more than one contrast for (jj in 1:ncol(Cmatrix)) { TopPSI <- limma::topTable(fitresiduals2, coef = jj, number = Inf)[, 1, drop = FALSE] colnames(TopPSI)<-"Residuals" Ress[[jj]] <- TopPSI } ``` # References - Romero, Juan P., et al. "EventPointer: an effective identification of alternative splicing events using junction arrays." BMC genomics 17.1 (2016): 467. - Romero, Juan P., et al. "Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm." BMC genomics 19.1 (2018): 703. - Goldstein, Leonard D., et al. "Prediction and Quantification of Splice Events from RNA-Seq Data." PloS one 11.5 (2016): e0156132. - Seshagiri, Somasekar, Eric W Stawiski, Steffen Durinck, Zora Modrusan, Elaine E Storm, Caitlin B Conboy, Subhra Chaudhuri, et al. 2012. "Recurrent R-spondin fusions in colon cancer." Nature 488 (7413): 660--64. \*Untergasser, A., Cutcutache, I., Koressaar, T., Ye, J., Faircloth, B. C., Remm, M., & Rozen, S. G. (n.d.). Primer3-new capabilities and interfaces. # Session Information ```{r} sessionInfo() ```