3.3.1 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

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.

# 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)

3.3.2 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

   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)

## 21:13:31 Running EventPointer:  
##  ** Statistical Analysis: Logarithm of the fold change of both isoforms 
##  ** Delta PSI will be calculated 
##  ** No expression filter 
##          ---------------------------------------------------------------- 
##  ** Calculating PSI...Done 
##  ** Running Statistical Analysis...Done 
## 
##  21:13:31  Analysis Completed

Table 1 displays the output of EventPointer function

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

4.1.1 Overview of RNA-Seq

EventPointer is also able to identify alternative splicing events from RNA-Seq data. The only required files are the corresponding .BAM files from the experiment.

Each time an experiment is analyzed with EventPointer, the complete process needs to be executed which may cause long waiting times to get the results. To avoid this issue, we have divided every step of the algorithm in different functions so as the user can reuse previous result and thus reduce computational time.

For the examples in this part of the vignette, we will use .bam files depicted in the SGSeq vignette that correspond to paired-end RNA-seq data from four tumor and four normal colorectal samples, which are part of a data set published in Seshagiri et al. 2012. As stated in SGSeq vignette the bam files only include reads mapping to a single gene of interest (FBXO31).

Note: For sequencing data, there are two requirements for the BAM files in order to get EventPointer 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 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.

4.1.2 BAM Preparation

The first step to analyze alternative splicing events in RNA-Seq data, is the creation of the splicing graphs. This step relies entirely on SGSeq R package.

The function PrepareBam_EP transforms all the information found in the bam files into splicing graph features and counts

• Samples : Name of the .bam files to be analyzed (Sample1.bam,Sample2.bam,…,etc)
• SamplePath: Path where the bam files are stored
• Ref_Transc: Reference transcriptome used to name the genes found. Options are: “Ensembl”,“UCSC” or “GTF”.
• fileTransc: Path to the GTF reference transcriptome if Ref_Transc is “GTF”.
• cores: Number of cores used for parallel processing
• Alpha: Internal SGSeq parameter to include or exclude regions (See Advanced Use)

# Obtain the samples and directory for .bam files

# the object si contains example sample information from the SGSeq R package 
# use ?si to see the corresponding documentation 
   
   BamInfo<-si
   Samples<-BamInfo[,2]
   PathToSamples <- system.file("extdata/bams", package = "SGSeq")
   PathToGTF<-paste(system.file("extdata",package="EventPointer"),"/FBXO31.gtf",sep="")

  # Run PrepareBam function
   SG_RNASeq<-PrepareBam_EP(Samples=Samples,
                            SamplePath=PathToSamples,
                            Ref_Transc="GTF",
                            fileTransc=PathToGTF,
                            cores=1)

The output of PrepareBam_EP function is a SGFeaturesCounts object, for more information check SGSeq Vignette. 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.

4.1.3 Event Detection

After running PrepareBam_EP, we have all the elements to analyze each of the splicing graphs. The next step is to identify and classify all the events, that are present in the BAM files.

For this purpose, the function EventDetection is used.

• Input : Output of the PrepareBam_EP function
• cores: Number of cores used for parallel processing
• Path: Directory where to write the EventsFound.txt file

  # Run EventDetection function
   data(SG_RNASeq)
   TxtPath<-tempdir()
   AllEvents_RNASeq<-EventDetection(SG_RNASeq,cores=1,Path=TxtPath)

## 
##  Obtaining Events
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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:

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.

4.1.4 Statistical Analysis

The statistical analysis of the alternative splicing events is done in exactly the same way as for junction arrays. With the Design and Contrast matrices, the algorithm gives the statistical significance and \(\Delta \Psi\).

The function for the statistical analysis using EventPointer method.

• Events : Output from EventDetection function
• Design: The design matrix for the experiment.
• Contrast: The contrast matrix for the experiment.
• 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.

The algorithm displays the different parameters that are selected to perform the analysis.

Following our example, the code chunk to obtain the results:

   Dmatrix<-matrix(c(1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1),ncol=2,byrow=FALSE)
   Cmatrix<-t(t(c(0,1)))
   Events <- EventPointer_RNASeq(AllEvents_RNASeq,Dmatrix,Cmatrix,Statistic="LogFC",PSI=TRUE)

## 21:13:36 Running EventPointer:  
##  ** Statistical Analysis: Logarithm of the fold change of both isoforms 
##  ** Delta PSI will be calculated 
##          ---------------------------------------------------------------- 
##  ** Calculating PSI...Done
##  Analysis Finished
##  Done 
## 
##  21:13:36  Analysis Completed

Table 2 displays the output of EventPointer function

4.1.5 IGV visualization

EventPointer creates one GTF file that can be loaded into IGV to visualize the alternative splicing events. Figure 6 displays an example result showed in IGV (5th ranked event in Table 2). Also, in the figure a reference transcriptome is displayed (blue track), and it can be seen that the displayed event corresponds to a novel event discovered with sequencing data and that it will not be detected using junction arrays.

Figure 6. EventPointer visualization using IGV

To create the GTF files, the algorithm uses the following code.

• Events : Data.frame generated by EventPointer_RNASeq with the events to be included in the GTF file.
• SG_RNASeq: Output from PrepareBam_EP function. Contains splicing graphs components.
• EventsTxt: Path to EventsFound.txt file generated with EventDetection function
• PathGTF: Directory where to write the GTF files.

A progress bar is displayed to the user to inform about the progress of the function.

Once the process is completed the GTF file is generated in the specified directory:

• paths_RNASeq.gtf : GTF file representing the alternative splicing events.

   # IGV Visualization
   EventsTxt<-paste(system.file("extdata",package="EventPointer"),"/EventsFound_RNASeq.txt",sep="")
   PathGTF<-tempdir()
   EventPointer_RNASeq_IGV(Events,SG_RNASeq,EventsTxt,PathGTF)
## 
##  Generating GTF Files...
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4.2.1 Event Detection

We use EventGTFfromTrancriptomeGTF 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.
• Transcriptome = the name of the transcriptome.
• Pathtxt = Directory to save the .txt file of the events found.
• PathGTF = Directory to save the .GTF file of the events found.

Following code shows how to apply EventGTFfromTrancriptomeGTF 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).

# Set input variables
PathFiles<-system.file("extdata",package="EventPointer")
inputFile <- paste(PathFiles,"/gencode.v24.ann_2genes.gtf",sep="")
Transcriptome <- "Gencode24_2genes"
Pathtxt <- tempdir()
PathGTF <- tempdir()

# Run the function
EventXtrans <- EventsGTFfromTrancriptomeGTF(inputFile = inputFile,
                                           Transcriptome = Transcriptome,
                                           Pathtxt=Pathtxt,
                                           PathGTF=PathGTF)
## Creating SG Information...
## Import genomic features from the file as a GRanges object ... OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK
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## 
## Creating .txt ...
## .txt created
## Creating .GTF ...
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## 
## .txt created
## Creating the sparseMatrix of paths x transcripts...
## 
##  ******FINISHED******

This function takes a couple of hours to complete (depending on the computer), and creates the following files:

• EventsFound_.txt A .txt file with the information of the events.
• paths_RNASeq.gtf A .GTF file with the information of the events required to be visualized in IGV.

This function also returns a list containing four 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.

names(EventXtrans)
## [1] "ExTP1"          "ExTP2"          "ExTPRef"        "transcritnames"

4.2.2 Quantification

Given the output of EventGTFfromTrancriptomeGTF we can quantify the expression of the alternative splicing events in our experiment as long as the quantification ot the experiment was done in Kallisto using the reference annotation used in EventGTFfromTrancriptomeGTF.

The function GetPSI_FromTranRef obtains the values of \(\Psi\) and needs the following parameters:

• PathsxTranscript the outpu of EventGTFfromTrancriptomeGTF.
• Samples A matrix containing the isoform quantification.
• Filter Boolean variable to indicate if an expression filter is applied. Defaul T.
• Qn Quantile used to filter the events (Bounded between 0-1, Q1 would be 0.25).

This function also needs that the reference annotation used in Kallisto and in the function EventGTFfromTrancriptomeGTF must be the same. Also in the variable Samples the type of annotation used must be equal to the one used in the variable PathsxTranscript.

In this example we have applied kallisto to the samples ERR315326, ERR315400, ERR315407 and ERR315494 using the same reference used before. These samples have been download from EMBL-EBI dataset.

#first: load data from kallisto output

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","NULL",NA))
for (n in 2:length(dirtoload)){
  RNASeq[,n+1] <- read.delim(dirtoload[n],sep = '\t', colClasses = c('NULL','NULL','NULL','NULL',NA))
}
rownames(RNASeq)<-RNASeq[,1]
RNASeq<-RNASeq[,-1]
colnames(RNASeq) <- filesnames

#second: the annotation of the transcript names must be equal in RNASeq and PathsxTranscript

rownames(RNASeq) <- sapply(strsplit(rownames(RNASeq),"\\|"),function(X) return(X[1]))
RNASeq<-as.matrix(RNASeq) #must be a matrix variable

#third: Obtain values of PSI
PSIss <- GetPSI_FromTranRef(PathsxTranscript = EventXtrans,Samples = RNASeq,Filter = FALSE)

PSI <- PSIss$PSI
Expression <- PSIss$ExpEvs

The output of the function is a list containing two elements: a matrix with the values of \(\Psi\) and a list containing as many matrices as number of events. In each matrix is stored the expression of the different paths of an event along the samples.

4.2.3 Statistical Analysis

The statistical analysis of the alternative splicing events is done in exactly the same way as for junction arrays and RNA-Seq data from BAM files.

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.

# 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)
## Done
##  Analysis Finished
##  Done 
## 
##  21:13:45  Analysis Completed

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.