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#title: "IsoformSwitchAnalyzeR"
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```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
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<!-- Title --> 
<center> <h1>IsoformSwitchAnalyzeR</h1> </center>
<center> <h2>Enabling Identification and Analysis of Isoform Switches with Functional Consequences from RNA-sequencing data</h2> </center>
<center> <h3>Kristoffer Vitting-Seerup</h3> </center>
<center> <h4>`r Sys.Date()`</h4> </center>

<!-- Abstract -->
## Abstract
Recent breakthrough in bioinformatics now allows us to accurately reconstruct and quantify full-length gene isoforms from RNA-sequencing data (via tools such as Cufflinks, Kallisto and Salmon). These tools made it possible to start analyzing alternative isoform usage, but unfortunately RNA-sequencing data is still underutilized since such analyses are both hard to make and therfore only rarely done.

To solve this problem we developed IsoformSwitchAnalyzeR. IsoformSwitchAnalyzeR is an easy to use R package that facilitates statistical identification of isoform switching from RNA-seq derived quantification of novel and/or annotated full-length isoforms. IsoformSwitchAnalyzeR furthermore facilitate integration of many sources of annotation including features such as Open Reading Frame (ORF), protein domains (via Pfam), signal peptides (via SignalP), coding potential (via CPAT) as well as sensitivity to Non-sense Mediated Decay (NMD). The combination of identified isoform switches and their annotation also enables IsoformSwitchAnalyzeR to predict potential functional consequence of the identified isoform switches - such as loss of protein domains or coding potential - thereby identifying isoform switches of particular interest. Lastly, IsoformSwitchAnalyzeR provide article ready visualization of isoform switches as well as multiple layers of summary statistics describing the genome wide occurence of isoform switches and their consequences.

In summary IsoformSwitchAnalyzeR enables analysis of RNA-seq data with isoform resolution with a focus on isoform switching (with predicted consequences) thereby expanding the usability of RNA-seq data.

<br />

<!-- Tabel of content -->
## Table of Content
[Abstract]

[Preliminaries]

- [Background and Package Description]
- [Installation]
- [How To Get Help]

[What To Cite] (please remember)

[Quick Start]

- [Workflow Overview]
- [Short Example Workflow] (aka the "To long - didn't read" section)


[Detailed Workflow]

- [Overview]
    + [IsoformSwitchAnalyzeR Background Information]
- [Importing Data Into R]
    + [Data from Cufflinks/Cuffdiff]
    + [Data from Kallisto, Salmon or RSEM]
    + [Data From Other Full-length Transcript Assemblers]
- [Filtering]
- [Identifying Isoform Switches]
    + [Testing Isoform Switches with IsoformSwitchAnalyzeR]
    + [Testing Isoform Switches via DRIMSeq]
    + [Testing Isoform Switches with other Tools]
- [Analyzing Open Reading Frames]
- [Extracting Nucleotide and Amino Acid Sequences]
- [Advise for Running External Sequence Analysis Tools]
- [Importing External Sequences Analysis]
- [Predicting Intron Retentions]
- [Predicting Switch Consequences]
- [Post Analysis of Isoform Switches with Consequences]

[Other workflows]

- [Augmenting ORF Predictions with Pfam Results]
- [Analyze Small Upstream ORFs]
- [Remove Sequences Stored in SwitchAnalyzeRlist]
- [Adding Uncertain Category to Coding Potential Predictions]
- [Quality control of ORF of known annotation]
- [Analyzing the Biological Mechanisms Behind Isoform Switching]

[Frequently Asked Questions and Problems]

[Final Remarks]

[Sessioninfo]

<br />

<!-- Preliminaries  -->
## Preliminaries
### Background and Package Description
The combination of alternative Transcription Start sites (aTSS), Alternative Splicing (AS) and alternative Transcription Termination Sites (aTTS) is often referred to as alternative transcription and is considered the major factors in modifying the pre-RNA and contributing to the complexity of higher organisms. Alternative transcription is widely used as recently demonstrated by The ENCODE Consortium, which found that an average of 6.3 different transcripts were generated per gene, although the individual number of transcripts from a single gene have been reported anywhere from one to thousands.

The importance of analyzing isoforms instead of genes has been highlighted by many examples showing functionally important changes that cannot be detected at gene level. One of these examples is the pyruvate kinase. In normal adult homeostasis, cells use the adult isoform (M1), which supports oxidative phosphorylation. But almost all cancers use the embryonic isoform (M2), which promotes aerobic glycolysis, one of the hallmarks of cancer. Such a shift in isoform usage has been termed isoform switching and frequently will not be detected at the gene level.

In 2010 a breakthrough in bioinformatics with the emergence of tools such as Cufflinks, which allows researchers to reconstruct and quantify full length transcripts from RNA-seq data. Tools for fast transcript quantification such as Salmon and Kallisto were the next breakthrough making it very fast to perform isoform quantification. Such data has the potential to facilitate both genome wide analysis of alternative isoform usage and identification of isoform switching - but unfortunately these types of analysis are still only rarely done.

We hypothesis that there are multiple reasons why RNA-seq data is not used to its full potential:

1) There is still a lack of tools that can identify isoform switches with isoform resolution - thereby identifying the exact isoforms involved in a switch.
2) Although there are many very good tools to perform sequence analysis there is no common framework, which allows for integration of the analysis provided by these tools.
3) There is a lack of tools facilitating easy and article ready visual visualization of isoform switches.

To solve these problems we developed IsoformSwitchAnalyzeR.

IsoformSwitchAnalyzeR is an easy to use R package that enables the user to import the (novel) full-length derived isoforms from an RNA-seq experiment into R. If annotated transcripts are analyzed, IsoformSwitchAnalyzeR offers integration with the multi-layer information stored in a GTF file including the annotated coding sequences (CDS). If transcript structure were predicted,  de-novo or guided, IsoformSwitchAnalyzeR offers a highly accurate tool for identifying the dominant ORF of the isoforms. The knowledge of isoform positions for the CDS/ORF furthermore allows for prediction of sensitivity to Nonsense Mediated Decay (NMD) - the mRNA quality control machinery that degrades isoforms with pre-mature termination codons (PTC).

Next, IsoformSwitchAnalyzeR enables identification of isoform switches via newly developed statistical methods that test each individual isoform for differential usage and thereby identifies the exact isoforms involved in isoform switch.

Since we know the exon structure of the full-length isoform, IsoformSwitchAnalyzeR can extract the underlying nucleotide sequence from a reference genome. This enables integration with the Coding Potential Assessment Tool (CPAT) which predicts the coding potential of an isoform and can furthermore be used to increase accuracy of ORF predictions. By combining the CDS/ORF isoform positions with the nucleotide sequence, we can also extract the most likely amino acid (AA) sequence of the CDS/ORF. The AA sequence enables integration of analysis of protein domains (via Pfam) and signal peptides (via SignalP) - both of which are supported by IsoformSwitchAnalyzeR. Lastly, since the structures of all expressed isoforms from a given gene are known, one can also annotate intron retentions (via spliceR).

Combined, IsoformSwitchAnalyzeR enables annotation of isoforms with intron retentions, ORF, NMD sensitivity, coding potential, protein domains as well as signal peptides, resulting in the identification of important functional consequences of the isoform switches.

IsoformSwitchAnalyzeR contains tools that allow the user to create article ready visualization of both individual isoform switches as well as general common consequences of isoform switches. These visualizations are easy to understand and integrate all the information gathered throughout the workflow. An example of visualization can be found here [Examples of visualization].

Lastly IsoformSwitchAnalyzeR is based on standard Bioconductor classes such as GRanges and BSgenome, whereby it supports all species and annotation versions facilitated in the Bioconductor annotation packages.

Back to [Table of Content].

<!-- Installation -->
### Installation
IsoformSwitchAnalyzeR is part of the Bioconductor repository and community which means it is distributed with, and dependent on, Bioconductor. Installation of IsoformSwitchAnalyzeR is very easy and can be done from within the R terminal. If it is the first time you use Bioconductor, simply copy-paste the following into your R session to install the basic bioconductor packages:

    source("http://bioconductor.org/biocLite.R")
    biocLite()

If you already have installed Bioconductor, running these two commands will check whether updates for installed packages are available.

<br />

After you have installed the basic bioconductor packages you can install IsoformSwitchAnalyzeR by copy pasting the following into your R session:

    source("http://bioconductor.org/biocLite.R")
    biocLite("IsoformSwitchAnalyzeR")

This will install the IsoformSwitchAnalyzeR package as well as other R packages that are needed for IsoformSwitchAnalyzeR to work.

<!-- Help -->
### How To Get Help
This R package comes with a lot of documentation. Much information can be found in the R help files (which can easily be accessed by running the following command in R "?functionName", for example "?isoformSwitchTest"). Furthermore this vignette contains a lot of information, make sure to read both sources carefully as it will contain the answer to the most Frequently Asked Questions and Problems.

If you have unanswered questions or comments regarding IsoformSwitchAnalyzeR please post them on the associted google group: https://groups.google.com/forum/#!forum/isoformswitchanalyzer (after making sure the question have not already been answered there). 

If you want to report a bug (found in the newest version of the R package) please make an issue with a reproducible example at github https://github.com/kvittingseerup/IsoformSwitchAnalyzeR - remember to add the appropriate label.

If you have suggestions for improvements also put them on github (https://github.com/kvittingseerup/IsoformSwitchAnalyzeR) this will allow other people to upvote you idea by reactions thereby showing us there is wide support of implementing your idea.

Back to [Table of Content].

<br />

<!-- Quick start -->
## What To Cite

The IsoformSwitchAnalyzeR tool is only made possible by a string of other tools and scientific discoveries - please read this section thoroughly and cite the appropriate articles. Note that due to the references being divided into sections some references appear more than once.

If you are using the 

- **Import of data from Salmon/Kallisto/RSEM** : Please cite refrence _10_
- **Inter-library normalization of abundance values** : Please cite refrence _10_ and _11_
- **Prediction of consequences** please cite refrence _1_
- **isoform switch test implemented in IsoformSwitchAnalyzeR** : Please cite both refrence _1_ and _2_
- **isoform switch test implemented in the DRIMSeq package (default)** : Please cite both refrence _1_ and _3_
- **visualizations** (plots) implemented in the IsoformSwitchAnalyzeR package : Please cite refrence _1_
- **Intron Retention analysis** : Please cite both refrence _1_ and _4_
- **prediction of open reading frames (ORF) analysis** : Please cite refrence _1_ and _4_
- **prediction of pre-mature termination codons (PTC) and thereby NMD-sensitivity** : Please cite refrence _1_, _4_, _5_ and _6_
- **CPAT** : Please cite refrence _7_
- **Pfam** : Please cite refrence _8_
- **SignalP** : Please cite refrence _9_

<br />

Refrences:

1. _Vitting-Seerup et al. **The Landscape of Isoform Switches in Human Cancers.** Cancer Res. (2017)_
2. _Ferguson et al. **P-value calibration for multiple testing problems in genomics.** Stat. Appl. Genet. Mol. Biol. 2014, 13:659-673._
3. _Nowicka et al. **DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics.** F1000Research, 5(0), 1356._
4. _Vitting-Seerup et al. **spliceR: an R package for classification of alternative splicing and prediction of coding potential from RNA-seq data**. BMC Bioinformatics 2014, 15:81._
5. _Weischenfeldt et al. **Mammalian tissues defective in nonsense-mediated mRNA decay display highly aberrant splicing patterns**. Genome Biol 2012, 13:R35_
6. _Huber et al. **Orchestrating high-throughput genomic analysis with Bioconductor**. Nat. Methods, 2015, 12:115-121._ 
7. _Wang et al. **CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model**. Nucleic Acids Res. 2013, 41:e74._
8. _Finn et al. **The Pfam protein families database**. Nucleic Acids Research (2014)  Database Issue 42:D222-D230_ 
9. _Petersen et al. **SignalP 4.0: discriminating signal peptides from transmembrane regions**. Nature Methods, 8:785-786, 2011_
10. _Soneson et al. **Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.** F1000Research 4, 1521 (2015)._
11. _Robinson et al. **A scaling normalization method for differential expression analysis of RNA-seq data**. Genome Biology (2010)_
## Quick Start
### Workflow Overview

The idea behind IsoformSwitchAnalyzeR is to make it easy to do advanced post analysis of full length RNA-seq derived transcripts with a focus on finding, annotating and visualizing isoform switches with functional consequences. IsoformSwitchAnalyzeR therefore performs 3 specific tasks:

- Identify isoform switches.
- Annotate the transcripts involved in the isoform switches.
- Visualize the consequences of the isoform switches, both individually and combined.

A normal workflow for identification and analysis of isoform switches with functional consequences can be divide into two parts (also illustrated below in Figure 1).  

<br /> 

**1) Extract Isoform Switches and Their Sequences.** This part includes importing the data into R, identifying isoform swithces, annotating those switches with open reading frames (ORF) and extract both the nucleotide and peptide sequence. The later step enables the usage of external sequence analysis tools such as

* CPAT : The Coding-Potential Assessment Tool, which can be run either locally or via their [webserver](http://lilab.research.bcm.edu/cpat/).
* Pfam : Prediction of protein domains, which can be run either locally or via their [webserver](http://pfam.xfam.org/search#tabview=tab1).
* SignalP : Prediction of Signal Peptides, which can be run either locally or via their [webserver](http://www.cbs.dtu.dk/services/SignalP/).

All of the above steps is performed by the high level function:

    isoformSwitchAnalysisPart1()
    
See below for example of usage, and [Detailed Workflow] for details on the individual steps.

<br /> 

**2) Plot All Isoform Switches and Their annotation.** This part involves importing and incorporating the results of the external sequence analyssi, identifying intron retentions, predicting functional consequences and lastly plotting all genes with isoform switches as well as summarizing general consequences of switching.

All of this can be done using the function:

    isoformSwitchAnalysisPart2()

See below for usage example, and [Detailed Workflow] for details on the individual steps.

<br /> 

**Alternatively** if one does not plan to incorporate external sequence analysis, it is possible to run the full workflow using:

    isoformSwitchAnalysisCombined()

Which correspond to running _isoformSwitchAnalysisPart1()_ and _isoformSwitchAnalysisPart2()_ without adding the external results.



<embed src="Overview_figure_simple.jpg" width="910" height="730">

**Figure 1: Workflow overview.** The grey transparent boxes indicate the two parts of a normal workflow for analyzing isoform switches. The individual steps in the two sub-workflows are indicated by arrows. The speech bubble summarizes how this full analysis can be done in a two step process using the high level functions (_isoformSwitchAnalysisPart1()_ and _isoformSwitchAnalysisPart2()_). 

Back to [Table of Content].

<br />

<!--                    -->
<!-- Short workflow  -->
<!--                    -->
### Short Example Workflow

A full, but less customizable, analysis of isoform switches can be done using the two high level functions _isoformSwitchAnalysisPart1()_ and _isoformSwitchAnalysisPart2()_. This section aims to show how these functions are used as well as illustrate what IsoformSwitchAnalyzeR can be used for.

Lets start by loading the R package
```{r, results = "hide", message = FALSE, warning=FALSE}
library(IsoformSwitchAnalyzeR)
```

<br />
Note that newer versions of RStudio supports auto-completion of package names.

#### Importing the Data
The first step is to import all the data needed for the analysis and store them in the switchAnalyzeRlist object. IsoformSwitchAnalyzeR contains different functions for importing data from a couple of tools such as Salmon/Kallisto/RSEM/Cufflinks but can be used with all isoform level expression data using the general purpouse functions also implemented. See [Importing Data Into R]) for details about this. For the purpose of illustrating data import, lets use Cufflinks/Cuffdiff data as an example: 

    cuffDB <- prepareCuffExample()

This function is just a wrapper for _readCufflinks()_ which makes the sql database from the example Cufflinks/Cuffdiff result data included in the R package "cummeRbund".

Once you have a CuffSet (the object type generated by _readCufflinks()_ and _prepareCuffExample()_), a switchAnalyzeRlist is then created by:

    aSwitchList <- importCufflinksCummeRbund(cuffDB)


<br />
Unfortunately, this example dataset is not ideal for illustrating the usability of IsoformSwitchAnalyzeR, as it only has two replicates, and the isoform switch tests relies on replicates. To illustrate the workflow, lets instead use some of the test data from the IsoformSwitchAnalyzeR package:

```{r}
data("exampleSwitchList")
exampleSwitchList
```

This data corresponds to a small subset of the result of using \code{importCufflinksCummeRbund} to get the result of the data described in ?exampleSwitchList. Note that although a switch identification analysis was performed, no genes were significant. This small data subset is ideal to showcase use due to the short runtimes. 


<br />

#### Part 1
We can now run the first part of the isoform switch analysis workflow which filters for non-expressed genes/isoforms, identifies isoform switches, annotates open reading frames (ORF) switches with and extracts both the nucleotide and peptide (amino acid) sequence.

```{r, results = "hide", message = FALSE, warning=FALSE}
### isoformSwitchAnalysisPart1 needs the genomic sequence to predict ORFs. 
# These are readily available from Biocindoctor as BSgenome objects: 
# http://bioconductor.org/packages/release/BiocViews.html#___BSgenome
# Here we use Hg19 - which can be download by copy/pasting the following two lines into the R terminal:
# source("https://bioconductor.org/biocLite.R")
# biocLite("BSgenome.Hsapiens.UCSC.hg19")

library(BSgenome.Hsapiens.UCSC.hg19)

exampleSwitchList <- isoformSwitchAnalysisPart1(
    input=exampleSwitchList, 
    genomeObject = Hsapiens, 
    dIFcutoff = 0.4,         # Set high for short runtime in example data
    outputSequences = FALSE # keeps the function from outputting the fasta files from this example
) 
```
Note that:

1. It is possible to supply the CuffSet (the object which links to the cufflinks/cuffdiff sql database, created with _readCufflinks{cummeRbund}_) directly to the _input_ argument instead of creating the switchAnalyzeRlist first.

2. The _isoformSwitchAnalysisPart1()_ function has an argument, _overwritePvalues_, which overwrites the result of user supplied p-values (such as those imported by cufflinks) with the result of running _isoformSwitchTestDRIMSeq()_.

3. The switchAnalyzeRlist produced by _isoformSwitchAnalysisPart1()_ has been subset to only contain genes where an isoform switch (as defined by the _alpha_ and _dIFcutoff_ arguments) were identified. This enables much faster runtimes for the rest of the pipeline, as only isoforms from a gene with a switch are analyzed.

The number of switching features is easily summarized via:

```{r}
extractSwitchSummary(exampleSwitchList, dIFcutoff = 0.4) # supply the same cutoff to the summary function
```

<br />

#### Part 2
The second part of the isoform switch analysis workflow, which includes importing and incorporating external sequence annotation, predicting functional consequences and visualizing both the general effects of isoform switches as well as the individual isoform switches. The combined analysis can be done by:

```{r, results = "hide", message = FALSE}
exampleSwitchList <- isoformSwitchAnalysisPart2(
    switchAnalyzeRlist      = exampleSwitchList, 
    dIFcutoff               = 0.4,   # Set high for short runtime in example data
    n                       = 10,    # if plotting was enabled it would only output the top 10 switches
    removeNoncodinORFs      = TRUE,  # Because ORF was predicted de novo
    pathToCPATresultFile    = system.file("extdata/cpat_results.txt"   , package = "IsoformSwitchAnalyzeR"),
    pathToPFAMresultFile    = system.file("extdata/pfam_results.txt"   , package = "IsoformSwitchAnalyzeR"),
    pathToSignalPresultFile = system.file("extdata/signalP_results.txt", package = "IsoformSwitchAnalyzeR"),
    codingCutoff            = 0.725, # the coding potential cutoff we suggested for human 
    outputPlots             = FALSE  # keeps the function from outputting the plots from this example
)
```

The numbers of isoform switches with functional consequences are easily extracted:
```{r}
extractSwitchSummary(exampleSwitchList, filterForConsequences = TRUE, dIFcutoff = 0.4) # supply the same cutoff to the summary function
```

For each of these genes a switchPlot has been generated - lets here take a closer look at the top candidate:

The top genes with isoform switches are:
```{r, message = FALSE}
extractTopSwitches(exampleSwitchList, filterForConsequences = TRUE, n=5)
```

<br />

#### Examples of visualization
Lets take a look at the isoform switch in the MSTP9 gene via the switchPlot:
```{r, fig.width=12, fig.height=7}
switchPlot(exampleSwitchList, gene='KIF1B')
```

From this plot, we firstly note that the gene expression is not significantly changed. Next we se the large significant switch in isoform usage. In the hESC it is primarly the long isoforms that are used, but as the cells are differentiated to Fibroblasts, threre is a switch towards mainly using the short isoform. Interestingly, this isoform switch results in the loss of several domains including the PH domain which plays a role in anchoring proteins to cell membranes. This could lead to a hypothesis that KIF1B would loss its ability to bind vesicles - a hypothesis that naturally would need to be tested. 

Note that if you want to save this plot as a pdf file via the _pdf_ command, you need to specify "onefile = FALSE". Here is a code chunk that will generate a nicely sized figure:

    pdf(file = '<outoutDirAndFileName>.pdf', onefile = FALSE, height=5, width = 8)
    switchPlot(exampleSwitchList, gene='KIF1B')
    dev.off()

<br />

Furthermore, note that if you are analyzing multiple conditions, you will also need to specify the 'condition1' and 'condition2' arguments in the _switchPlot()_ function to indicate specifically which comarison you want to plot. \cr

To illustrate the genome wide analysis of consequences of isoform switching in the different comparisons, we will use a larger dataset:

```{r}
data("exampleSwitchListAnalyzed")
exampleSwitchListAnalyzed
```

```{r, fig.width=12, fig.height=8}
extractConsequenceSummary(exampleSwitchListAnalyzed, asFractionTotal = FALSE)
```

From this summary plot we can see many things. First of all, the most frequent changes are changes in ORF length. Secondly, from iPS to both Fibroblast and hESC we see a larger number of domain gains than domain loss. Most notably is the uneven distribution of intron retention gain/loss in the comparison of iPS to both Fibroblast and hESC.

Since all the data is saved in the switchAnalyzeRlist, we can also make a couple of overview plots:

```{r, fig.width=8, fig.height=6, warning=FALSE}
data("exampleSwitchListAnalyzed")

### Vulcano like plot:
ggplot(data=exampleSwitchListAnalyzed$isoformFeatures, aes(x=dIF, y=-log10(isoform_switch_q_value))) +
     geom_point(
        aes( color=abs(dIF) > 0.1 & isoform_switch_q_value < 0.05 ), # default cutoff
        size=1
    ) +
    geom_hline(yintercept = -log10(0.05), linetype='dashed') + # default cutoff
    geom_vline(xintercept = c(-0.1, 0.1), linetype='dashed') + # default cutoff
    facet_grid(condition_1 ~ condition_2, scales = 'free') +
    scale_color_manual('Signficant\nIsoform Switch', values = c('black','red')) +
    theme_bw()
```

From which it is clearly seen why cutoffs both on the dIF and the q-value are nesseary.

Another interesting overview plot can be made as follow:

```{r, fig.width=8, fig.height=6, warning=FALSE}
### Switch vs Gene changes:

ggplot(data=exampleSwitchListAnalyzed$isoformFeatures, aes(x=gene_log2_fold_change, y=dIF)) +
    geom_point(
        aes( color=abs(dIF) > 0.1 & isoform_switch_q_value < 0.05 ), # default cutoff
        size=1
    ) + 
    facet_grid(condition_1 ~ condition_2, scales = 'free') +
    geom_hline(yintercept = 0, linetype='dashed') +
    geom_vline(xintercept = 0, linetype='dashed') +
    scale_color_manual('Signficant\nIsoform Switch', values = c('black','red')) +
    labs(x='Gene log2 fold change', y='dIF') +
    theme_bw()
```

From which it is clearly seen that changes in gene expression and isoform switching are not nessesaryly mutually exclusive as there are many examples of which are both differentially expressed and contain isoform switches. This just highlights the necessity of also analyzing RNA-seq data at isoform-level resolution.

Back to [Table of Content].
<br />

<!--                    -->
<!-- Detailed workflow  -->
<!--                    -->
## Detailed Workflow
### Overview
Before you start on this section, we recommend you to read though the [Quick Start] section, as it gives the large overview, introduces some basic concepts and give a couple of tips not repeated here.

Compared to the workflow presented in [Quick Start], a full workflow for analyzing isoform switches naturally have a lot of sub-steps which each can be customized/optimized. In this section, we will go more into depth with each of the steps as well as provide tips and shortcuts for working with IsoformSwitchAnalyzeR. Specifically each of the main functions behind these steps will be described and illustrated. For a more comprehensive and detailed description of each individual function please refer to the individual function documentation (via ?functionName).

The first section contains [IsoformSwitchAnalyzeR Background Information], followed by a detailed isoform switch analysis workflow and lastly an overview of useful [Other Tools in IsoformSwitchAnalyzeR] is provided. 

Back to [Table of Content]
<br />

The detailed workflow consists of the following steps (illustrated in Figure 2) which, just like before, can be divided into two parts:

**Part 1) Extract Isoform Switches and Their Sequences.**

* [Importing Data Into R]
    + [Data from Cufflinks/Cuffdiff]
    + [Data from Kallisto, Salmon or RSEM]
    + [Data From Other Full-length Transcript Assemblers]
* [Filtering]
* [Identifying Isoform Switches]
* [Analyzing Open Reading Frames]
* [Extracting Nucleotide and Amino Acid Sequences]
* [Advise for Running External Sequence Analysis Tools]

This corresponds to running the following functions in sequential order (which incidentally is just what the _isoformSwitchAnalysisPart1()_ function does):

```
### Import data and create SwitchAnalyzeRlist
# either:
mySwitchList <- importCufflinksData()
# or:
mySwitchList <- importIsoformExpression()
mySwitchList <- importRdata()

### Run analysis
mySwitchList <- preFilter(mySwitchList)

mySwitchList <- isoformSwitchTestDRIMSeq(mySwitchList) # OR 
mySwitchList <- isoformSwitchTest(mySwitchList)

mySwitchList <- analyzeORF(mySwitchList)
mySwitchList <- extractSequence(mySwitchList)
```

**Part 2) Plot All Isoform Switches and Their annotation.**

- [Importing External Sequences Analysis]
- [Predicting Intron Retentions]
- [Predicting Switch Consequences]
- [Post Analysis of Isoform Switches with Consequences]

This corresponds to running the following functions in sequential order (just like the _isoformSwitchAnalysisPart2()_ function does):

```
### Add annotation
mySwitchList <- analyzeCPAT(mySwitchList)
mySwitchList <- analyzePFAM(mySwitchList)
mySwitchList <- analyzeSignalP(mySwitchList)
mySwitchList <- analyzeIntronRetention(mySwitchList)

### Analyse consequences
mySwitchList <- analyzeSwitchConsequences(mySwitchList)

### visualize results
switchPlotTopSwitches(mySwitchList)
extractConsequenceSummary(mySwitchList)
```

The combined workflow is visualized here:

<embed src="Overview_figure_detailed.jpg" width="910" height="870">

**Figure 2: Detailed workflow overview.** The grey transparent boxes indicate the two parts of a normal workflow for analyzing isoform switches. The individual steps in the two sub-workflows are indicated by arrows along with a description and the main R functions for performing the steps. 

<br />

<!-- Background information -->
### IsoformSwitchAnalyzeR Background Information
#### The switchAnalyzeRlist
The switchAnalyzeRlist object is created to specifically contain and summarize all relevant information about the isoforms involved in isoform switching. The switchAnalyzeRlist is a named list, meaning each entry in the list can be accessed by its name via the '$' symbol or by using "[['entryName']]". A newly created switchAnalyzeRlist object contains 6 entries, and as the isoforms are gradually annotated and analyzed more entries are added.

```{r}
data("exampleSwitchList")         # A newly created switchAnalyzeRlist + switch analysis
names(exampleSwitchList)

data("exampleSwitchListAnalyzed") # A fully analyzed switchAnalyzeRlist
names(exampleSwitchListAnalyzed)
```

The first entry 'isoformFeatures' is a data.frame where all relevant data about each comparison of an isoform (between conditions), as well as the analysis performed and annotation added via IsoformSwitchAnalyzeR is stored. Amongst the default information is isoform and gene id, gene and isoform expression as well as the _isoform\_switch\_q_value_ and _isoform\_switch\_q_value_ where the result of the differential isoform analysis is stored. The comparisons made can be identified from 'condition\_1' to 'condition\_2', meaning 'condition\_1' is considered the ground state and 'condition\_2' the changed state. This also means that a positive dIF value indicates the isoform usage is increased in 'condition\_2' compared to 'condition\_1'. Since the 'isoformFeatures' entry is the most relevant part of the switchAnalyzeRlist object, the most used standard methods have also been implemented to works directly on isoformFeatures.

```{r}
### Preview
head(exampleSwitchList, 2)

identical(
    head(exampleSwitchList), head(exampleSwitchList$isoformFeatures)
)

identical(
    tail(exampleSwitchList), tail(exampleSwitchList$isoformFeatures)
)

### Dimentions
dim(exampleSwitchList$isoformFeatures)

nrow(exampleSwitchList)
ncol(exampleSwitchList)
dim(exampleSwitchList)
```

<br />

A very useful functionality implemented in IsoformSwitchAnalyzeR is _subsetSwitchAnalyzeRlist()_ function, which allows for removal of isoforms and all their associated information across all entries in a switchAnalyzeRlist. The function subsets the switchAnalyzeRlist based on a vector of logicals matching the isoformFeatures entry of the list.


```{r}
exampleSwitchListAnalyzed

### Subset
subsetSwitchAnalyzeRlist(
    exampleSwitchListAnalyzed,
    exampleSwitchListAnalyzed$isoformFeatures$gene_name == 'ARHGEF19'
)
```


<br />
Transcript structure information is stored in the exon entry of the switchAnalyzeRlist and contains the genomic coordinates for each exon in each isoform, as well as a column indicating which isoform it originates from. This information is stored as GenomicRanges (GRanges), which is very useful for overlapping genomic features and interacting with other Bioconductor packages.

```{r}
head(exampleSwitchList$exons,2)
```

A full description of the initial switchAnalyzeRlist can be found via _?switchAnalyzeRlist_ and each additional entry added is described in the "value" part of the documentation for the specific function adding the entry.

Back to [Table of Content]

<br />

#### Function overview
In this section we will give a brief overview of the functions implemented in IsoformSwitchAnalyzeR. With a few exceptions, these functions can be divided into 5 groups:

1. **_import\*()_**: Functions for importing data from known data sources into a switchAnalyzeRlist (see details above), for example _importIsoformExpression()_ for importing data from Kallisto/Salmon or RSEM.
2. **_analyze\*()_**: Functions for analyzing and annotating the switchAnalyzeRlist. For example _analyzeORF()_ for analyzing the ORFs of the isoforms in the switchAnalyzeRlist.
3. **_extract\*()_**: Functions for extracting (summarized) data from the switchAnalyzeRlist., e.g. _extractConsequenceSummary()_ for extracting a summary of the number of genes and isoforms with isoform switches including predicted functional consequences.
4. **_switchPlot\*()_**: Functions that allows for visualization of the data in various ways, e.g. _switchPlotTranscript()_ for visualizing the transcripts along with the compiled annotation or _switchPlot()_ for making the Isoform Switch Analysis Plot (see [Examples of visualization])
5. **_isoformSwitchAnalysis\*()_**: High level functions (described in [Short Example Workflow]) which offers an easy, but less customizable, way of performing the full workflow for analyzing isoform switches, e.g. _isoformSwitchAnalysisPart1()_ for running the first (of two) part of the workflow.

The exceptions to these categories are the following:

    createSwitchAnalyzeRlist()
    preFilter()
    isoformSwitchTest()
    isoformSwitchTestDRIMSeq()

Where:

*  _createSwitchAnalyzeRlist()_ is the central constructor of the switchAnalyzeRlist which is both used by all the _import\<description\>()_ functions and for construction of switchAnalyzeRlist from user supplied data sources. Note that this function also adds the 'iso_ref' and 'gene_ref collumns to the isoformFeatures entry of a switchAnalyzeRlist. These reference indexes give a unique id to each gene and isoform in each comparison and thereby allow for easy integration of the different downstream analysis.
* _preFilter()_ allows for removal of filtered data in a switchAnalyzeRlist to remove non-interesting data such as non-expressed isoforms or genes with only one annotated isoform.
* _isoformSwitchTest()_ implements the test for differential isoform usage introduced in Vitting-Seerup 2017 (see [What To Cite]).
* isoformSwitchTestDRIMSeq()_ is a wrapper for the switch analysis performed in Nowicka et al 2016 (see [What To Cite]).

As well as some addtional tools not essetial for the workflow (more about them later).

Note that we have tried to be systematic with function argument names for cutoff, meaning that if the argument name contains "cutoff" the value supplied is not included whereas if it contains "min" or "max" the given value is included.

Back to [Table of Content]

<!-- Importing data  -->
<br />

### Importing Data Into R
IsoformSwitchAnalyzeR can analyze the output from any tool that quantify (de-novo/guided deconvoluted) full-length isoforms. All it requires is the following 3 sets of data:

* The estimated read count for isoforms in multiple samples from each condition (replicates are essential if differential isoform usage should be identified)
* The genomic coordinates of the transcript exon structure.
* Annotation of which isoform belong to which gene.

And furthermore it is highly recommended to also obtain the estimated replicate abundances and also use them in the construction of the switchAnalyzeRlist.

From these data, a minimum switchAnalyzeRlist (see [The switchAnalyzeRlist] for description) can be constructed. To facilitate the usage of IsoformSwitchAnalyzeR, several dedicated wrappers for constructing the switchAnalyzeRlist from different data sources are included in switchAnalyzeRlist. See the following sections for details about the specific import methods:

* [Data from Cufflinks/Cuffdiff]
* [Data from Kallisto, Salmon or RSEM]
* [Data from other Full-length Transcript Assemblers]

<br />

#### Data from Cufflinks/Cuffdiff
The data from Cufflinks/Cuffdiff is of special interest because Cuffdiff is amongst the most advanced tools for analyzing RNA-seq data with isoform resolution. Furthermore, Cuffdiff also supports identification of isoform switching by: a) Cuffdiff have a test of isoform switches amongst isoforms with shared promoter (isoforms from same pre-mRNA), b) Cuffdiff gives better estimates of the expression uncertainties (variance) than estimated from the raw data, which can be utilized with one of the isoform switch test (at isoform level) implemented (see [Identifying Isoform Switches]).

There are two ways of obtaining a switchAnalyzeRlist from cufflinks.

    importCufflinksCummeRbund()
    importCufflinksFiles()
    
* _importCufflinksCummeRbund()_ is made to import the data from the SQL backend that can be constructed from the Cuffdiff output via Cufflinks auxiliary R package "cummeRbund".
* _importCufflinksFiles()_ is made to use files outputted by a Cufflinks/Cuffdiff. This also allows the user to run the computational heavy RNA-seq pipeline with mapping, transcript deconvolution, transcript quantification and differential expression on a cloud based server (for example [galaxy](https://usegalaxy.org)), and then do the post-analysis on isoform switching locally by simply downloading the required files.

Creating a switchAnalyzeRlist via cummeRbund is a two-step process. First use the cummeRbund function _readCufflinks()_ to create the SQL backend (which in the following example is done by _prepareCuffExample()_ on the cummeRbund example data).

```{r, warning=FALSE, message=FALSE}
cuffDB <- prepareCuffExample()
cuffDB
```

This SQL backend is also extremely useful since it, via other functions implemented in cummeRbund, allows for a lot of (necessary!) quality control (QC) and exploratory data analysis (EDA). See section 5 in the [cummeRbund manual](http://bioconductor.org/packages/release/bioc/vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf) for more information).

<br />

Now we have the sql connection, simply use _importCufflinksCummeRbund()_ to create the switchAnalyzeRlist.

```{r, warning=FALSE}
aSwitchList <- importCufflinksCummeRbund(cuffDB)
aSwitchList
```

<br />

An alternative way of importing data from Cufflinks/Cuffdiff is to directly use the files outputted by Cuffdiff. This can be done with the _importCufflinksFiles()_ function. In the example below we link to the example files in the cummeRbund package via the system.file() function. To analyze your own data simply supply the (full) path to the required files as a string (aka do not use the system.file() function).

```{r, warning=FALSE}
testSwitchList <- importCufflinksFiles(
    pathToGTF                      = system.file('extdata/chr1_snippet.gtf',             package = "cummeRbund"),
    pathToGeneDEanalysis           = system.file('extdata/gene_exp.diff',                package = "cummeRbund"),
    pathToIsoformDEanalysis        = system.file('extdata/isoform_exp.diff',             package = "cummeRbund"),
    pathToGeneFPKMtracking         = system.file('extdata/genes.fpkm_tracking',          package = "cummeRbund"),
    pathToIsoformFPKMtracking      = system.file('extdata/isoforms.fpkm_tracking',       package = "cummeRbund"),
    pathToIsoformReadGroupTracking = system.file('extdata/isoforms.read_group_tracking', package = "cummeRbund"),
    pathToSplicingAnalysis         = system.file('extdata/splicing.diff',                package = "cummeRbund"),
    pathToReadGroups               = system.file('extdata/read_groups.info',             package = "cummeRbund"),
    pathToRunInfo                  = system.file('extdata/run.info',                     package = "cummeRbund"),
    fixCufflinksAnnotationProblem=TRUE,
    quiet=TRUE
)
testSwitchList
```


Note that both cufflinks import functions per default:

1. Corrects the annotation problem caused by cufflinks having to consider islands of overlapping transcripts - this means that sometimes multiple genes (defined by gene name) are combined into one cufflinks gene (XLOC\_XXXXXX) and this gene is quantified and tested for differential expression. Setting _fixCufflinksAnnotationProblem=TRUE_ will make the import function modify the data so that false conclusions are not made in downstream analysis. More specifically this cause the function to re-calculate expression values, set gene standard error (of mean (measurement), s.e.m) to NA and the p-value and q-value of the differential expression analysis to 1, whereby false conclusions can be prevented.
2. Imports the result of Cuffdiff's splicing test and adds it to the isoformSwitchList. This can be disabled by setting _addCufflinksSwichTest=FALSE_.


The resulting switchAnalyzeRlist can then be used with the rest of the isoform switch analysis pipeline. The next step is typically [Filtering].

<br />


#### Data from Kallisto, Salmon or RSEM
While RSEM is a well-established and robust isoform quantification tool, a new generation of tools often refered to as pseudo/quasi-aligners, such as Salmon and Kallisto, are on the rise due to their speed and increased accuracy. We therefore naturally support analysis of quantifications made with all three tools by allowing for easy import of the isoform replicate count matrix via the _importIsoformExpression()_ function. 

This function is a wrapper for the tximport R package with extra functionalities. By using txtimport it allows the isoform counts to be estimated from the abundance estimates - which is recomended as it will incooperate the bias correction performed by the tools into the switch identification. The _importIsoformExpression_ can furthermore perform a inter-library normalization of the abundance estimats which is nessesary for a fair comparison of libraries (for in deapth discussion of normalization and why it is needed see section 2.7 of the [edgeR user guide](http://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf)).

Using _importIsoformExpression_ results in the creation of a list which contains both a count and an abundance matrix can then easily be used to generate a switchAnalyzeRlist with the _importRdata()_ function (as described in the next section).

<br />

#### Data from other Full-length Transcript Assemblers
As described above, all you need to create a switchAnalyzeRlist is:

* The isoform count expression in multiple samples from multiple conditions
* The genomic coordinates of the isoform exon structure.
* Annotation of which isoform belongs to which gene.

Note that:

1) IsoformSwichtAnalyzeR also supports the analysis of isoform switches found via other tools/test - see [Testing Isoform Switches with other Tools].
2) that the overall unit of isoforms analyzed together is defined by the gene annotation provided. In principle, this overall unit does not have to be genes but could be for example a TSS shared by multiple isoforms - to utilize this simply provide TSS\_ids instead of gene\_ids in the isoform annotation.

Once you have imported the isoform expression estimates (via _importIsoformExpression()_ or otherwise) as well as the isoform structre and the isoform annotation into R an switchAnalyzeRlist can be constructed using the general-purpose wrapper:

    importRdata()

All you need to supply to the _importRdata()_ is the estimated replicate isoform count expression, optionally the replicate isoform abundance estimates, the isoform annotation and a design matrix indicating which samples are from the same condition. The _importRdata()_ function will then: 

* If necessary, calculate isoform FPKM/RPKM values (ommited if abundances is supplied).
* Sum up abundance values from all isoforms belonging to a gene to get the gene expression. 
* For each gene/isoform in each condition (as indicate by designMatrix) the mean expression and standard error (of mean (measurement), s.e.m) expression are calculated.
* For each pairwise comparison of condition (or as controlled by the _comparisonsToMake_ argument) the mean gene and isoform expression values are then used to calculate log2 fold changes and Isoform Fraction (IF) values. The whole analysis is then, along with the genomic annotation, concatenated into a SwitchAnalyzeRlist.

Note that:

1) _importRdata()_ allows the user to supply an abundance matrix (via the _isoformRepExpression_ argument) that can be used (instead of them being calculated directly from the count matrix). This is highly recomended and also nessesary if additional normalization is wanted.
2) CPM/TPM (Tags Per Million) values should NOT under any circumstances be supplied to _importRdata()_ since they do not take the length into account, meaning that isoform switches where there are a difference in the length of the involved isoforms, will be biased by TPM/CPM values (Please note that Transcripts Per Million (here called TxPM but somthimes denoted TPM) are not problematic). To illustrate the problem with TPM/CPM, a simple thought example can be made: Consider two isoforms where isoform 1 exist in 5 copies and isoform 2 exist in 10 copies (a 1:2 relation). If isoform 1 is twice as long as isoform 2, an RNA-seq experiment will generate (approximatly) twice as many reads from it - meaning if CPM/TPM values are used a false 1:1 realtion will be estimated. This is naturally not the case for TxPM/RPKM/FPKM, since the isoform lengths are taken into account.
3) If a GTF file is suppled to the "isoformExonAnnoation" argument it is possible also to import the CDS stored in the GTF file (by setting "addAnnotatedORFs=TRUE") as the Open Reading Fram (ORF) IsoformSwitchAnalyzeR uses for downstream analysis. This can be advantageous if the data provided is a quantification of known annotated transcripts (aka isoforms are not the result of any de-novo or guided assembly) since the uncertainties of predicting the ORF is then avoided.


```{r, warning=FALSE, message=FALSE}
### Construct data needed from example data in cummeRbund package
### (The recomended way of analyzing Cufflinks/Cuffdiff datat is via importCufflinksCummeRbund()
### This is jus an easy way to get some example data ).
cuffDB <- prepareCuffExample()
```
```{r}
isoRepCount <- repCountMatrix(isoforms(cuffDB))
isoRepCount$isoform_id <- rownames(isoRepCount)

### Design matrix
designMatrix <- cummeRbund::replicates(cuffDB)[,c('rep_name','sample_name')]
colnames(designMatrix) <- c('sampleID','condition')

localAnnotaion <- import(system.file("extdata/chr1_snippet.gtf", package="cummeRbund"))[,c('transcript_id','gene_id')]
colnames(localAnnotaion@elementMetadata)[1] <- 'isoform_id'


### Take a look at the data
head(isoRepCount, 2)

designMatrix

head(localAnnotaion, 3)

### Create switchAnalyzeRlist
aSwitchList <- importRdata(
    isoformCountMatrix=isoRepCount,
    designMatrix=designMatrix,
    isoformExonAnnoation=localAnnotaion,
    showProgress=FALSE
)
aSwitchList
```

This switchAnalyzeRlist can then be used with the rest of the pipeline. The next step in a typical workflow [Filtering].

<br />

Alternatives to using _importRdata()_ are either _importGTF()_ or _createSwitchAnalyzeRlist()_.
    
If the _importGTF()_ function is used it will generate a switchAnalyzeRlist with dummy variables:

```{r, warning=FALSE, message=FALSE}
aSwitchList <- importGTF(pathToGTF = system.file("extdata/chr1_snippet.gtf", package = "cummeRbund"))
```

```{r}
aSwitchList

head(aSwitchList,2)
head(aSwitchList$conditions,2)
```

From above, it is observed that dummy variables have been inserted in both _isoformFeatures_ and _conditions_ entries of the switchAnalyzeRlist. This approach is well suited if you just want to annotate a transcriptome and are not interested in expression. If you are interested in expression estimates, it is probably easier to use _importRdata_. 

Back to [Table of Content]

<br />

### Filtering
Once you have a switchAnalyzeRlist, there is a good chance that it contains a lot of genes/isoforms that are irrelevant for an analysis of isoform swhiches. Examples of such could be single isoform gene or non-expressed isoforms. These extra genes/isoforms will make the downstream analysis take (much) longer than necessary. Therfore we have implemented a pre-filtering step to remove thse features before continuing with the analysus. Furthermore, filtering can enhance the reliability of the downstream analysis as described below.

By using _preFilter()_ it is possible to remove genes and isoforms from all aspects of the  switchAnalyzeRlist via filtering on:

* Multi isoform genes
* Gene expression
* Isoform expression
* Isoform Fraction (isoform usage)
* Unwanted isoform classes
* Genes without differential isoform usage

Removal of single isoform genes is the default setting in _preFilter()_ since these genes per definition cannot have changes in isoform usage. 

Filtering on isoforms expression allows removal of non-used isoforms that only appear in the switchAnalyzeRlist because they were in the isoform/gene annotation used. Furthermore, the expression filtering allows removal oflowly expressed isoforms where the expression levels might be untrustworthy. The filtering on gene expression allows for removal of lowly expressed genes which causes the calculations of the Isoform Fractions (IF) to become untrustworthy. 

The filter on Isoform Fraction allows removal of isoforms that only contribute minimally to the gene expression thereby speeding up and simplifying the rest of the downstream analysis.

Filtering on unwanted isoform classes is only an option available if using data form Cufflinks/Cuffdiff since the Tuxedo workflow includes classification of transcript types. This allows for removal of for example transcripts classified as "Possible polymerase run-on fragment" or "Repeat". See full description of Cufflinks/Cuffdiff transcript classification [here](http://cole-trapnell-lab.github.io/cufflinks/cuffcompare/#transfrag-class-codes)

The _preFilter()_ function is used as follows:

```{r}
data("exampleSwitchList")
exampleSwitchList

exampleSwitchListFiltered <- preFilter(exampleSwitchList, geneExpressionCutoff = 1, isoformExpressionCutoff = 0, removeSingleIsoformGenes = TRUE)

exampleSwitchListFilteredStrict <- preFilter(exampleSwitchList, geneExpressionCutoff = 10, isoformExpressionCutoff = 3, removeSingleIsoformGenes = TRUE)
```

Back to [Table of Content]

<br />

### Identifying Isoform Switches
As identification of isoform switches are essential for IsoformSwitchAnalyzeR, multipe ways of identifying isoform switches are supported. Currently IsoformSwitchAnalyzeR directly supports three different approachs:

* DRIMSeq : Using the DRIMSeq package. See [Testing Isoform Switches via DRIMSeq].
* IsoformSwitchAnalyzeR : The isoform level test implemented in this package. See [Testing Isoform Switches with IsoformSwitchAnalyzeR].
* Cufflinks/Cuffdiff : the splicing analysis perfromed by Cuffdiff is automatically imported when importing Cufflinks/Cuffdiff data. See [Data from Cufflinks/Cuffdiff].

However results of other tools for which can test for differential isoform usage at gene and/or isoform level can also be used. See [Testing Isoform Switches with other Tools].

No matter which method is used, all the downstream functionality in IsoformSwitchAnalyzeR (e.g. not only the test) uses tow paramters to defines a significant isoform switch:

1. The _alpha_ argument indicating the FDR corrected p-value (q-value) cutoff.
2. The _dIFcutoff_ argument indicating the minimum (absolute) change in isoform usage (dIF).

This combination is used since a q-value is only a measure of the statistical certainty of the difference between two groups and thereby not reflects the effect size which however is measured by the dIF values.

IsoformSwitchAnalyzeR supports tests for differential isoform usage performed both at isoform and gene resolution. Note however that for gene level analysis you reliy on cutoffs on dIF values for identifying the isoforms involved in a switch - something that, when compared to the isoform level analysis, could give false positives. We therefore recommend the use of isoform level analysis whenever possible.

#### Testing Isoform Switches via DRIMSeq
A recent breakthrough in the analysis of isoform usage was when the DRIMSeq package (By Nowicka et al, see [What To Cite] - please remember to cite the tool) was updated to not only analyze genes for differential isoform usage but provide a statistical test for each isoform analyzed thereby allowing for identification of the excat isoforms involved in a switch.

IsoformSwitchAnalyzeR supports identification of isoform switches with DRIMSeq via the wrapper _isoformSwitchTestDRIMSeq()_ which performs the full analysis of all isoforms and comparisons in switchAnalyzeRlist. Afterwards, the results are integrated back into the switchAnalyzeRlist which is returned to the user as usual. Note that we support both the use of the gene-level and isoform-level analysis perfromed by DRIMSeq as controlled by the 'testIntegration' argument. Using this argument, it is also possible to be even more stringent by requiring that both the gene- and isoform-level analysis must be significant.

The _isoformSwitchTestDRIMSeq()_ function is used as follows:

```{r, warning=FALSE}
# Load example data and prefilter
data("exampleSwitchList")
exampleSwitchList <- preFilter(exampleSwitchList) # preFilter for fast runtime

# Perfom test (takes ~10 sec)
exampleSwitchListAnalyzed <- isoformSwitchTestDRIMSeq(
    switchAnalyzeRlist = exampleSwitchList,
    testIntegration='isoform_only',
    reduceToSwitchingGenes=TRUE
)

# Summarize swiching geatures
extractSwitchSummary(exampleSwitchListAnalyzed)
```

An important argument in _isoformSwitchTestDRIMSeq()_ is the 'reduceToSwitchingGenes'. This is simply an option to reduce the switchAnalyzeRlist to only genes that contains at least one isoform passing the supplied _alpha_ and _dIFcutoff_ cutoffs. This option ensures the rest of the workflow goes significantly faster since isoforms from genes without isoform switching is not analyzed. 

Please note:

1. Currently the DRIMSeq approach can be a bit slow and might require some patience if a large number of isoforms are analyzed (aka not something you want to sit and wait for). We have experienced runtimes of up to 90 minutes per comparison made for a full size datasets.
2. That if an switchAnalyzeRlist already contains the result of a switch test this will be overwritten by _isoformSwitchTestDRIMSeq()_.
3. The _isoformSwitchTestDRIMSeq()_ function allows the user to pass arguments to the individual sets of a DRIMSeq workflow - especially the "dmFilterArgs" option could be of interest since sometimes additional filtering can be needed to make DRIMSeq run (if you get the "non-finite value supplied by optim" error)

The DRIMSeq approach utilizes Bayesian information sharing across genes/isoforms. We therfore recommend most users to use the DRIMSeq approach as it is much more sensitive when having a small number of samples. The exception to this recommendation is if a large number of samples (per condition) are analyzed. For such cases we recommend the statistical test implemented in IsoformSwitchAnalyzeR as it fast and the gain of the information sharing in DRIMSeq decreases as the number of samples increases.

#### Testing Isoform Switches with IsoformSwitchAnalyzeR
IsoformSwitchAnalyzeR contains an implementation of a test for differential isoform usage (in the function _isoformSwitchTest()_) described in Vitting-Seerup et al (2017) (see [What To Cite]). Here, we will introduce two sections describing the test for differential isoform usage implemented in _isoformSwitchTest()_. First, the idea behind the test will be introduced and afterwards we will show how to use it (jump to [Usage of The Statistical Test]).

##### The Idea Behind The Statistical Test
One of the better ways of descripting isoform usage is as Isoform Fractions (IF). These are simply values describing how large a fraction of the expression of a given gene originate from a given isoform. So the Isoform Fraction of isoform _x_ ($IF_x$) is given by: $IF_x = i_x / g$ where $i_x$ is the expression of isoform _x_ and _g_ is the parent gene expression. Note that the parent gene expression is the sum of the expression of all the associated isoforms. The difference between two conditions, condition 1 and 2, is then given by $dIF= IF_2 - IF_1$.

The Idea Behind The Statistical Test implemented in IsoformSwitchAnalyzeR is:

1. Use the uncertainty (e.g. variance) in the gene and isoform expression estimates (obtained from biological replicates) to estimate the uncertainty of the isoform usage (e.g. the variance of the IF values). See figure 3A.
2. Use the uncertainty of the IF estimate to statistically test whether changes in isoform usage (measured as IF values) between conditions are valid. This also means one test per isoform (per comparison) is performed. See figure 3B.
3. Calibrate and correct for multiple testing. The test implemented, although statistically sound, is conservative when small sample sizes are used. Therefore, we have incorporated the p-value calibration described and implemented in Ferguson et al (2014) (see [What To Cite]) as an optional step, in accordance with the specification suggested by the author (see details in ?isoformSwitchTest), making the test more sensitive for smaller sample sizes.

More information can be found in the documentation page of _isoformSwitchTest()_.


<embed src="statistics_illustration.jpg" width="700" height="520">

**Figure 3: Visualization of the rationale behind the statistical test implemented in IsoformSwitchAnalyzeR.** A) Visualization of how the uncertainties in gene and isoform expression is combined to calculate the uncertainty of isoform fractions. B) Shows how the uncertainties in Isoform Fractions allows for statistical comparison of the Isoform Usage of an isoform in different conditions.

<br />

For the mathematical details of how this test is derived, please refer to the supplementary data of Vitting-Seerup et al (2017) (see [What To Cite]).

<br />

##### Usage of The Statistical Test
The test described above in [The Idea Behind The Statistical Test] section is implemented in IsoformSwitchAnalyzeR as the function:

    isoformSwitchTest()

```{r}
### Show arguments of function
args(isoformSwitchTest)
```

If a single isoform from a given gene is termed significant, we define this gene as having an isoform switch, since if there is a change in the isoform usage of one isoform there must be changes in another (or multiple) isoforms compensating for the identified change. To extrapolate this to gene level, we define the q-value at the gene level (in the 'gene\_switch\_q\_value' column) as equivalent to the smallest q-value of the associated isoforms (in the 'isoform\_switch\_q\_value column).

Another important argument in _isoformSwitchTestDRIMSeq()_ is the 'reduceToSwitchingGenes'. This is simply an option to reduce the switchAnalyzeRlist to only genes that contains at least one isoform passing the given _alpha_ and _dIFcutoff_ cutoffs. This option ensures that rest of the workflow runs significantly faster, since isoforms from genes without isoform switching are not analyzed. 

Note that if a switchAnalyzeRlist already contains the result of a switch test this will be completely overwritten by _isoformSwitchTestDRIMSeq()_.

The DRIMSeq approach utilizes Bayesian information sharing across genes/isoforms. We therfore recommend most users to use the DRIMSeq approach as it is much more sensitive when having a small number of samples. The exception to this recommendation is if a large number of samples (per condition) are analyzed. For such cases we recommend the statistical test implemented in IsoformSwitchAnalyzeR as it fast and the gain of the information sharing in DRIMSeq decreases as the number of samples increases.


```{r}
# Load example data and prefilter
data("exampleSwitchList")
exampleSwitchList <- preFilter(exampleSwitchList) # preFilter for fast runtime

# Perfom test
exampleSwitchListAnalyzed <- isoformSwitchTest(exampleSwitchList)

# Summarize swiching geatures
extractSwitchSummary(exampleSwitchListAnalyzed)
```
<br />

As suggested by the Ferguson et al (2014) the p-value calibration is not always performed (even though _calibratePvalues=TRUE_, see details in ?isoformSwitchTest). It is therefore useful to check in which comparison the p-value calibration was performed. To this end _extractCalibrationStatus()_ have been implemented.

```{r}
extractCalibrationStatus(exampleSwitchListAnalyzed)
```

From which we see that we should be careful with comparing the two conditions since they have been treated differently. To fix this problem we can simply turn the calibration off:

```{r}
# Perfom test
exampleSwitchListAnalyzed <- isoformSwitchTest(exampleSwitchList, calibratePvalues = FALSE)

# Summarize swiching geatures
extractSwitchSummary(exampleSwitchListAnalyzed)

# check callibration status
extractCalibrationStatus(exampleSwitchListAnalyzed)
```

<br />

Back to [Table of Content]

<br />



#### Testing Isoform Switches with other Tools
IsoformSwichtAnalyzeR also supports the analysis of isoform switches found via other tools. All you need to do is to generate a switchAnalyzeRlist with the corresponding data via one of the import options (see [Importing Data Into R]) and then fill in either the _isoform\_switch\_q\_value_ or _gene\_switch\_q\_value_ columns in the _isoformFeatures_ entry of the switchAnalyzeRlist with the multiple testing corrected p-values (q-values) from the testing tool. 

If the external tool used has isoform resolution (one test per isoform), the q-values should be added to the _isoform\_switch\_q\_value_ column and the smallest q-value of a given gene (in a specific comparison) should be added to all the isoforms for that gene in the _gene\_switch\_q\_value_ column. If the external tool has lower resolution (pre-mRNA or gene level resolution) the q-values should only be added to the _gene\_switch\_q\_value_ column (and the _isoform\_switch\_q\_value_ should be set to NA). 

In this way, IsoformSwitchAnalyzeR can also support non-isoform resolution testing - note however that:

1) Via the isoform resolution level tests we often find cases where the significant isoform is not the same isoform as the ones with the largest changing (aka large dIF values) and vice versa indicating that a non-isoform resolution might be inadequate.
2) If you supply the result of a non-isoform resolution isoform switch test (to the _gene\_switch\_q\_value_ column) the number of isoforms reported by summary tools such as _extractSwitchSummary_ or _extractConsequenceSummary_ will naturally not be correct and should therefore be ignored.

<br />

### Analyzing Open Reading Frames
Once the isoform switches have been found, the next step is to annotate the isoforms involved in the isoform switches. The first step in such annotation is to obtain Open Reading Frames (ORFs).

If known annotated isoforms were quantified (aka you did not perform a (guided) de novo isoform reconstruction), you should use the annotated CDS (Coding Sequence) form the annotation - which can be imported and integrated though one of the implemented methods, see [Importing Data Into R].

If you performed (guided) de-novo isoform reconstruction (isoform deconvolution), prediction of ORFs can be done with high accuracy from the transcript sequence alone (see supplementary data in Vitting-Seerup et al 2017). Such prediction can be don with:

    analyzeORF()
    
This function utilizes that we know the genomic coordinates of each transcript to extract the transcript nucleotide sequence from a reference  genome (supplied via the _genomeObject_ argument). In _analyzeORF_ four different methods for predicting the ORF  are implemented, suitable for different purposes and circumstances. The four methods are:

1. The 'longest' method. This method identifies the longest ORF based on finding the canonical start and stop codons in the transcript nucleotide sequence. This approach is what the CPAT tool uses in its analysis of coding potential. This is the default as it is the most common use case and have the highest accuracy in benchmark against known annotated ORFs (>90% accuracy against GENCODE data, Vitting-Seerup et al 2017).
2. The 'mostUpstream' method. This method identifies the most upstream ORF based on finding the canonical start and stop codons in the transcript nucleotide sequence.
3. The 'longestAnnotated' method. This method identifies the longest ORF downstream of an annotated translation start site. It requires known translational start sites are supplied to the _cds_ argument.
4. The 'mostUpstreamAnnoated' method. This method identifies ORF downstream of the most upstream overlapping annoated translation start site. It requires known translational start sites are supplied to the _cds_ argument.

One important argument is the _minORFlength_, which will ensure that only ORFs longer than _minORFlength_ nucleotides are annotated. Besides predicting the ORF, information about the most likely stop codon also allows for prediction of Pre-mature Termination  Codon (PTC) and thereby sensitivity to degradation via the Nonsense Mediated Decay (NMD) machinery. This analysis is also implemented in the _analyzeORF()_ function and is controlled by the _PTCDistance_ argument.

Note that the ORF prediction can be integrated with both the CPAT results (via the _removeNoncodinORFs_ paramter, see ?analyzeCPAT) as well as Pfam results (see [Augmenting ORF Predictions with Pfam Results])

The _analyzeORF()_ function can be used as follows:

```{r}
### This example relies on the example data from the 'Usage of The Statistical Test' section above 

### analyzeORF needs the genomic sequence to predict ORFs. 
# These are readily advailable from Biocindoctor as BSgenome orbjects: 
# http://bioconductor.org/packages/release/BiocViews.html#___BSgenome
# Here we use Hg19 - which can be download by copy/pasting the following two lines into the R termminal:
# source("https://bioconductor.org/biocLite.R")
# biocLite("BSgenome.Hsapiens.UCSC.hg19")

library(BSgenome.Hsapiens.UCSC.hg19)

exampleSwitchListAnalyzed <- analyzeORF(exampleSwitchListAnalyzed, genomeObject = Hsapiens, showProgress=FALSE)

head(exampleSwitchListAnalyzed$orfAnalysis, 3)

```

As seen above, including genomic and transcript coordinates of ORF start and stop as well as PTC status, is added to the 'orfAnalysis' entry of the switchAnalyzeRlist.

If the user wants to use the _longestAnnotated_ or _mostUpstreamAnnoated_ methods, the _analyzeORF()_ function requires known CDS to be supplied as described above. The CDS must be stored as a CDSSeq (see ?CDSSet) and a wrapper for downloading the CDS of the most frequently used datasets from UCSC genome browser is available via:

    getCDS()

<br />

### Extracting Nucleotide and Amino Acid Sequences
Now that we know the ORF of a transcript, we can obtain the corresponding protein amino acid sequence of the ORF simply by translating the nucleotide sequence of the ORF into amino acids. This opens the possibility for also performing both internal and external sequence analysis to annotate the isoforms involved in isoform switches further. To facilitate this we have implemented:

    extractSequence()

Which allows for the extraction of both nucleotide and amino acid sequences from the switchAnalyzeRlist. To facilitate external sequence analysis _extractSequence_ can output fasta files (one per sequence type) and to facilitate internal sequence analysis the sequences can be added to the switchAnalyzeRlist. An example of the internal sequence analysis is a pairwise comparison of the ORFs in two switching isoforms (see [Predicting Switch Consequences] below).

```{r}
### This example relies on the example data from the 'Analyzing Open Reading Frames' section above 

exampleSwitchListAnalyzed <- extractSequence(
    exampleSwitchListAnalyzed, 
    genomeObject = Hsapiens,
    pathToOutput = '<insert_path>',
    writeToFile=FALSE # to avoid output when running this example data
)

head(exampleSwitchListAnalyzed$ntSequence,2)

head(exampleSwitchListAnalyzed$aaSequence,2)
```

Back to [Table of Content]

<br />

### Advise for Running External Sequence Analysis Tools
The two fasta files outputted by _extractSequence()_ (if writeToFile=TRUE) can be used as input to amongst others:

* CPAT : The Coding-Potential Assessment Tool which is a tool for predicting whether an isoform is coding or not. CPAT can be run either locally or via their [webserver](http://lilab.research.bcm.edu/cpat/).
* Pfam : Prediction of protein domains, which can be run either locally or via their [webserver](http://pfam.xfam.org/search#tabview=tab1).
* SignalP : Prediction of Signal Peptides, which can be run either locally or via their [webserver](http://www.cbs.dtu.dk/services/SignalP/).

These three tools are the once currently supported but if you have additional ideas please do not hesitate to contact us as described in [How To Get Help].

We suggest the external sequence analysis tools are run as follows:

* CPAT : Use default parameters and the nucleotide fasta file (_nt.fasta). If the webserver (http://lilab.research.bcm.edu/cpat/) was used download the tab-delimited result file (from the bottom of the result page). If a stand-alone version was used just supply the path to the result file. 
* Pfam : Use default parameters and the amino acid fasta file (_AA.fasta). If the webserver (https://www.ebi.ac.uk/Tools/hmmer/search/hmmscan) is used you need to copy paste the result part of the mail you receive into a empty plain text document (notepat, sublimetext TextEdit or similar (not word)) and save that to a plain text (txt) file. The path to that file should be supplied here. If a stand-alone version was used, just supply the path to the result file. A more detailed walkthrough is found under details in the documentation of the \code{analyzePFAM()} function (?analyzePFAM).
* SignalP : Use the amino acid fasta file (_AA.fasta). When using a stand-alone version SignalP should be run with the '-f summary' option. If using the web-server (http://www.cbs.dtu.dk/services/SignalP/) SignalP should be run with the parameter "standard" under "Output format" and "No graphics" under "Graphics output". When using the web-server the results (everything between the two hoizontal lines) should be copy pasted into a empty plain text document (notepat, sublimetext TextEdit or similar (not word)) and save that to plain text (txt) file. This file is then used as input to the function. If a stand-alone version was used, just supply the path to the summary result file.

Back to [Table of Content]

<br />

### Importing External Sequences Analysis
After the external sequence analysis with CPAT, Pfam, and SignalP have been performed (please remember to cite as describe in [What To Cite]), the results can be extracted and added to the switchAnalyzeRlist via respectively

    analyzeCPAT()
    analyzePFAM()
    analyzeSignalP()

The functions are used as:

```{r}
### Load test data (maching the external sequence analysis results)
data("exampleSwitchListIntermediary")
exampleSwitchListIntermediary

### Add PFAM analysis
exampleSwitchListAnalyzed <- analyzePFAM(
    switchAnalyzeRlist   = exampleSwitchListIntermediary,
    pathToPFAMresultFile = system.file("extdata/pfam_results.txt", package = "IsoformSwitchAnalyzeR"),
    showProgress=FALSE
    )

### Add SignalP analysis
exampleSwitchListAnalyzed <- analyzeSignalP(
    switchAnalyzeRlist       = exampleSwitchListAnalyzed,
    pathToSignalPresultFile = system.file("extdata/signalP_results.txt", package = "IsoformSwitchAnalyzeR")
    )
    
### Add CPAT analysis
exampleSwitchListAnalyzed <- analyzeCPAT(
    switchAnalyzeRlist   = exampleSwitchListAnalyzed,
    pathToCPATresultFile = system.file("extdata/cpat_results.txt", package = "IsoformSwitchAnalyzeR"),
    codingCutoff         = 0.725, # the coding potential cutoff we suggested for human
    removeNoncodinORFs   = TRUE   # because ORF was predicted de novo
    )

exampleSwitchListAnalyzed
```

Where the _pathTo\<tool\_name>resultFile_ points to the result files constructed as suggested in [Advise for Running External Sequence Analysis Tools] and in the detailed documentation of each of the individual functions. 

Of particular interest is the _removeNoncodinORFs_ argument in _analyzeCPAT()_ since it allows to integrate the CPAT and ORF analysis by removing ORFs from isoforms not predicted to be coding by CPAT. This can be particular useful if isoforms and ORFs have been predicted de novo (both guided or non-guided). Note that if enabled (by setting to TRUE) it will affect all downstream analysis and plots as both analysis of domains and signal peptides requires that ORFs are annotated (e.g. _analyzeSwitchConsequences_ will for example not consider the domains (potentially) found by Pfam if the ORF have been removed).


Back to [Table of Content]

<br />

### Predicting Intron Retentions
Another annotation we easily can obtain, since we know the exon structure of all isoforms in a given gene (with isoform switching), is intron retentions. This can be done via the *spliceR* R package via the wrapper implemented in _analyzeIntronRetention()_.

```{r}
### This example relies on the example data from the 'Importing External Sequences Analysis' section above 

exampleSwitchListAnalyzed <- analyzeIntronRetention(exampleSwitchListAnalyzed, quiet=TRUE)

### overview of number of intron retentions (IR)
table(exampleSwitchListAnalyzed$isoformFeatures$IR)
```

Meaning 25 isoforms each with one intron retention (IR) and 2 isoforms with 2 IRs was identified.

Back to [Table of Content]

<br />

### Predicting Switch Consequences
If an isoform has a significant change in its contribution to the gene expression, there must per definition be a reciprocal changes in one (or more) isoforms in the opposite direction compensating for the initial change. We utilize this by extracting the isoforms that are significantly differentially used and compare them to the isoforms that are compensating. By comparing all the information gathered during the workflow described above for the isoform used more (positive dIF) to the isoforms used less (negative dIF) we can identify differences in the functional annotation of the isoforms and thereby identify potential function consequences of the isoform switch.

Specifically, IsoformSwitchAnalyzeR contains a function _analyzeSwitchConsequences()_ which extract the isoforms with significant changes in their isoform usage (defined by the _alpha_ and _dIFcutoff_ parameters, see [Identifying Isoform Switches] for details) as well as the isoforms, with a lage opposite change in isoform usage (also controlled via the _dIFcutoff_ parameters), that compensate for the changes. Note that if an isoform level test was not used the gene is require to be significant (defined by the _alpha_ paramter) but isoforms are then selected purely based on their changes in dIF values.

These isoforms are then divided into the isoforms that increases their contribution to gene expression (positive dIF values larger than _dIFcutoff_) and the isoforms that decrease their contribution (negative dIF values smaller than \-_dIFcutoff_). The isoforms with increased contribution are then (in a pairwise manner) compared to the isoform with decreasing contribution. In each of these comparisons the isoforms compared are analyzed for differences in their annotation (controlled  by the _consequencesToAnalyze_ parameter). Currently 22 different features of the isoforms can be compared, which include features such as intron retention, coding potential, NMD status, protein domains and the sequence similarity of the amino acid sequence of the annotated ORFs. For a full list see under details at the manual page for _analyzeSwitchConsequences()_ (?analyzeSwitchConsequences). 

A more strict analysis can be performed by enabling the _onlySigIsoforms_ argument, which causes _analyzeSwitchConsequences()_ to only consider significant isoforms (defined by the _alpha_ and _dIFcutoff_ parameters) meaning the compensatory changes in isoform usage are ignored unless they themselves are significant.

The analysis of consequences can be performed as follows:

```{r}
### This example relies on the example data from the 'Predicting Intron Retentions' section above 

# the consequences highlighted in the text above
consequencesOfInterest <- c('intron_retention','coding_potential','NMD_status','domains_identified','ORF_seq_similarity')

exampleSwitchListAnalyzed <- analyzeSwitchConsequences(
    exampleSwitchListAnalyzed,
    consequencesToAnalyze = consequencesOfInterest, 
    dIFcutoff = 0.4, # very high cutoff for fast runtimes
    showProgress=FALSE
)

extractSwitchSummary(exampleSwitchListAnalyzed, dIFcutoff = 0.4, filterForConsequences = FALSE)
extractSwitchSummary(exampleSwitchListAnalyzed, dIFcutoff = 0.4, filterForConsequences = TRUE)
```

Meaning that ~5/7 genes with switches (with dIF > 0.4) have isoform switches with functional consequences. For a larger dataset, it looks more like:

```{r}
### Load already analyzed data
data("exampleSwitchListAnalyzed")

extractSwitchSummary(exampleSwitchListAnalyzed, filterForConsequences = FALSE)
extractSwitchSummary(exampleSwitchListAnalyzed, filterForConsequences = TRUE)
```

Where the combined row indicates the number of unique features across all comparisons.

Please note that the _analyzeSwitchConsequences()_ function contains a lot of parameters and cutoffs for deciding when a difference in the annoatation is sufficiently different (e.g. supplying 'ntCutoff=50' ensures that differences in nucleotide lengths of two ORF are larger than 50 nucleotides (aka not just 3 nucleotide)).

Back to [Table of Content]

<br />

### Post Analysis of Isoform Switches with Consequences
Now that we have performed a genome-wide analysis of isoform switches with potential consequences, there are two types of post-analysis we can perform:

1) [Analysis of Individual Isoform Switching].
2) [Genome Wide Analysis] to analyze the overall patterns of isoform switches with functional consequences

IsoformSwitchAnalyzeR contains tools for both, as illustrated in each of the following sections - starting with the analysis of individual genes.

#### Analysis of Individual Isoform Switching
When analyzing the individual genes with isoform switches, the genes/isoforms with the largest changes in isoform usage (aka "most" switching genes/isoforms) are of particular interest. IsoformSwitchAnalyzeR can help you obtain these, either by sorting for the smallest q-values (getting the most significant genes) or the largest absolute dIF values (getting the largest effect sizes (switches) that are still significant). Both methods are implemented for both genes and isoforms in the _extractTopSwitches()_ function and are controlled via the _sortByQvals_ argument.

```{r}
### Load already analyzed data
data("exampleSwitchListAnalyzed")

### Lets reduce the switchAnalyzeRlist to only one condition
exampleSwitchListAnalyzedSubset <- subsetSwitchAnalyzeRlist(
    exampleSwitchListAnalyzed, 
    exampleSwitchListAnalyzed$isoformFeatures$condition_1 == 'iPS' &
    exampleSwitchListAnalyzed$isoformFeatures$condition_2 == 'hESC'
)
exampleSwitchListAnalyzedSubset

### Extract top 2 switching genes (by dIF values)
extractTopSwitches(
    exampleSwitchListAnalyzedSubset, 
    filterForConsequences = TRUE, 
    n = 2, 
    sortByQvals = FALSE
)

### Extract top 2 switching genes (by q-value)
extractTopSwitches(
    exampleSwitchListAnalyzedSubset, 
    filterForConsequences = TRUE, 
    n = 2, 
    sortByQvals = TRUE
)
```

Let us take a look at the switching isoforms in the TBC1D22B gene which plays a role in protecting from apoptosis:


```{r}
### Extract data.frame with all switching isoforms
switchingIso <- extractTopSwitches( 
    exampleSwitchListAnalyzedSubset, 
    filterForConsequences = TRUE, 
    n = NA,                  # n=NA: all features are returned
    extractGenes = FALSE,    # when FALSE isoforms are returned
    sortByQvals = TRUE
)

subset(switchingIso, gene_name == 'TNFRSF1B')
```

The isoform switch can also be visually analyzed by the Isoform Switch Analysis Plot. Note that since there is only one comparison in the switchAnalyzeRlist (after the subset), it is not necessary to specify the conditions.

```{r, fig.width=12, fig.height=7}
switchPlot(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B')
```

From this plot, it is clear to see that in the iPS cells it is mainly the isoform 'TCONS_00000307' that is used. The isoform is predicted to be NMD sensitive (due to the inclusion of a premature termination codon (PTC) in exon 7). The identified isoform switch does however result in a switch away from the NMD sensitive isoform whereby only the protein coding isoforms are used in normal human Embryonic Stemm Cells (hESC) and not in iPS cells. Note that no differential expression was identified by Cufflinks/Cuffdiff meaning that a gene-level analysis might have missed this change.

<br />

One advantage of the Isoform Switch Analysis Plot is that it contains all information needed to judge the potential impact of an isoform switch. This also means a systematic screening of the top isoform switches can be made by generating this plot for each of those genes.

To facilitate such screening we have implemented _switchPlotTopSwitches()_ which will extract the top _n_ genes (controlled by the _n_ argument) with significant switches (as defined by _alpha_ and _dIFcutoff_) and output a pdf or png version of the corresponding Isoform Switch Analysis Plot to the directory given by the _outputDestination_ argument. The function furthermore automatically ranks (by p-value or switch size) the switches and supports to either filter for isoform switches with predicted functional consequences or to output those with and those without consequences to separate folders.

    switchPlotTopSwitches(
        switchAnalyzeRlist = exampleSwitchListAnalyzed, 
        n= 10,
        filterForConsequences = FALSE, 
        splitFunctionalConsequences = TRUE
    )

If you want to output a plot for all genes simply set "n=NA".

Back to [Table of Content]

#### Genome Wide Analysis
A genome wide analysis is both useful for getting an overview of the extent of isoform switching as well as discovering larger patterns.

The _extractConsequenceSummary()_ function can globally summarize isoform switches with consequences as shown here: 


```{r, fig.width=12, fig.height=6}
### Load the large example dataset
data("exampleSwitchListAnalyzed")

### Extract summary
consequenceSummary <- extractConsequenceSummary(
    exampleSwitchListAnalyzed, 
    returnResult = TRUE, 
    plotGenes = TRUE
)

subset(consequenceSummary, featureCompared=='Intron retention')
```

Illustrating that both a tabular and a visual summary can be obtained thereby providing a general overview of the isoform switches. 

From this summary plot, we can see for large fraction of isoform switches where the upregulated isoforms more frequently have intron retentions.

Note furthermore that the focus of the plot generated is on the _number of genes_ with isoform switches with functional consequences (controlled by plotGenes = TRUE and asFractionTotal=FALSE), whereas in [Short Example Workflow] the y-axis was the _fraction of isoforms_ involved in isoform switching with functional consequences.

If the difference between conditions are substantial such effects could indicate a more systematic change on genome wide scale. This can also be annalyzed with IsoformSwitchAnalyzeR by simultaniously analyzing all isoforms with a specific feature (fx intron retention) for changes in isoform usage:

```{r, fig.width=12, fig.height=6}
symmaryStatistics <- extractGenomeWideAnalysis(
    switchAnalyzeRlist = exampleSwitchListAnalyzed,
    featureToExtract = 'isoformUsage', # default - alternatives are 'isoformExp', 'geneExp' and 'all'
    plot=TRUE,
    returnResult = TRUE
)
```
(Note that the 3 dots in each violin plot correspond to the 25th, 50th (median) and 75th percentiles).

Which shows that, although many isoform switches are identified, no global changes are identified (in this toy data).

Back to [Table of Content]

<br />

### Other Tools in IsoformSwitchAnalyzeR
Apart from the analysis presented above, IsoformSwitchAnalyzeR also contains a couple of other tools which will be presented in this section

The central visualization is the Isoform Switch Analysis Plot, created with _switchPlot()_ as shown above. This plot is made from 4 subplots, which can be created individually using respectively:

    switchPlotTranscript() # Visualizes the transcripts and their annotation
    switchPlotGeneExp()    # Visualizes the gene exression
    switchPlotIsoExp()     # Visualizes the isoform exression
    switchPlotIsoUsage()   # Visualizes the isoform usage

As illustrated here:

```{r, fig.width=12, fig.height=3}
### Load already analyzed data
data("exampleSwitchListAnalyzed")

switchPlotTranscript(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B')
```
```{r, fig.width=3, fig.height=3}
switchPlotGeneExp (exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B', condition1='iPS', condition2='hESC')
```
```{r, fig.width=4, fig.height=3}
switchPlotIsoExp(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B', condition1='iPS', condition2='hESC')
```

```{r, fig.width=4, fig.height=3}
switchPlotIsoUsage(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B', condition1='iPS', condition2='hESC')
```

<br />

Another usefull tool implemented is the _isoformToGeneExp()_ function which takes a data.frame of isoform expression and summarizes it up to gene level expression values. This function is quite usefull in conjunction with the _importIsoformExpression()_ function since the _importIsoformExpression()_ function imports isoform-level data - which can then be summarized to gene level data with _isoformToGeneExp()_. This is quite analogous to what tximport can do - except it allows for incooperation of the additional features, such as inter-library normalization, implemented in _importIsoformExpression()_.

<br />

The last tool currently build into IsoformSwitchAnalyzeR is the extractExpressionMatrix() function. The expression information stored in the switchAnalyzeRlist's isoformFeatures is ideal for comparing multiple annotation in a specific comparison of two conditions, but is not well suited for the comparison of multiple conditions. The _extractExpressionMatrix()_ function solves this by converting the average expression information (gene expression, isoform expression or Isoform Fraction values as controlled via the _feature_ argument) into a matrix format as illustrated here:


```{r}
data("exampleSwitchListIntermediary")
ifMatrix <- extractExpressionMatrix(exampleSwitchListAnalyzed)

head(ifMatrix)

dim(ifMatrix)

```

<br />

Such a matrix can be used for global comparisons of multiple condtions and potential analysis are sample correlations:

```{r, fig.width=4, fig.height=3}
# correlation plot
ggplot(melt(cor(ifMatrix)), aes(x=Var1, y=Var2, fill=value)) + 
    geom_tile() +
    scale_fill_continuous('Correlation') +
    labs(x='Condition', y='Condition') +
    theme_minimal()
```

Or expression (via a heatmap):

```{r, fig.width=8, fig.height=4}
library(pheatmap)
pheatmap(t(ifMatrix), show_colnames=FALSE)
```


Back to [Table of Content]

<br />

## Other workflows
### Augmenting ORF Predictions with Pfam Results
The workflow described here is an extension of the workflow enabled in the _analyzeCPAT()_ function via the "removeNoncodinORFs" argument which if TRUE remove ORF information from the isoforms where the CPAT analysis classifies them as non-coding. Here we will "rescue"" the ORF of isoforms predicted to be noncoding by CPAT but which have a predicted protein domain. This workflow is an alternative apporach to setting "removeNoncodinORFs=TRUE" and should be used instead of that option.

Note that this is only recommended if ORFs were predicted (aka not imported from a GTF file). After this procedure, isoforms with ORFs will be isoforms with an ORF longer than _minORFlength_ (if specified in _analyzeORF_) which are predicted to be coding by CPAT OR have a predicted protein domain (by Pfam).

Since the ORF information is stored in the 'orfAnalysis' analysis entry of the switchList we can remove it (by replacing it with NAs) as follows:

```
### Test data
data("exampleSwitchListAnalyzed")
exampleSwitchListAnalyzed

### Extract coding isoforms
nonCodingIsoforms <- unique(exampleSwitchListAnalyzed$isoformFeatures$isoform_id[
    which( ! exampleSwitchListAnalyzed$isoformFeatures$codingPotential )
    ])

### Rescue those with protein domains
nonCodingIsoformsRescued <- setdiff(nonCodingIsoforms, exampleSwitchListAnalyzed$domainAnalysis$isoform_id)

# nr rescued
length(nonCodingIsoforms) - length(nonCodingIsoformsRescued)


### Remove noncoding isoforms ORF annotation
sum(is.na(exampleSwitchListAnalyzed$orfAnalysis$orfTransciptStart))

exampleSwitchListAnalyzed$orfAnalysis[
    which( exampleSwitchListAnalyzed$orfAnalysis$isoform_id %in% nonCodingIsoformsRescued), 2:ncol(exampleSwitchListAnalyzed$orfAnalysis)
    ] <- NA
    
exampleSwitchListAnalyzed$isoformFeatures$PTC[which(exampleSwitchListAnalyzed$isoformFeatures$isoform_id %in% nonCodingIsoforms)] <- NA

sum(is.na(exampleSwitchListAnalyzed$orfAnalysis$orfTransciptStart))
```

Back to [Table of Content]

<br />

### Analyze Small Upstream ORFs
Recent research suggests that small upstream ORFs are far more frequent that previously assumed. It is therefore of particular interesting to start analyzing these and here we have indirectly presented a tool which can do just so: _analyzeORF()_. 

Here we show how one could start such an analysis of small upstream ORFs.

```
# run ORF analysis on longest ORF
exampleSwitchListAnalyzed <- analyzeORF(exampleSwitchListAnalyzed, genomeObject = Hsapiens, method='longest')
mean(exampleSwitchListAnalyzed$orfAnalysis$orfTransciptLength)

# run ORF analysis on most upstream ORF
exampleSwitchListAnalyzed2 <- analyzeORF(exampleSwitchListAnalyzed, genomeObject = Hsapiens, orfMethod = 'mostUpstream', minORFlength = 50)
mean(exampleSwitchListAnalyzed2$orfAnalysis$orfTransciptLength)

# calculate pairwise difference
summary(
    exampleSwitchListAnalyzed2$orfAnalysis$orfTransciptLength -
        exampleSwitchListAnalyzed$orfAnalysis$orfTransciptLength[
            match(exampleSwitchListAnalyzed2$orfAnalysis$isoform_id ,exampleSwitchListAnalyzed$orfAnalysis$isoform_id)
        ]
)
```

Back to [Table of Content]

<br />

### Remove Sequences Stored in SwitchAnalyzeRlist
The sequences stored in the SwitchAnalyzeRlist are not needed after consequences have been predicted and can thereby be removed thereby reducing the object size as well as loading/saving times. This has for example been done for the example dataset 'exampleSwitchListAnalyzed'. This is simply done as follows

```
summary(exampleSwitchListIntermediary)

exampleSwitchListIntermediary$ntSequence <- NULL
exampleSwitchListIntermediary$aaSequence <- NULL

summary(exampleSwitchListIntermediary)
```

Back to [Table of Content]

<br />

### Adding Uncertain Category to Coding Potential Predictions
There has been quite a bit of debate about whether the default parameters for the codingPotential calculated for CPAT are to lenient (aka to low). This will always be a problem by having a single cutoff. One possible solution is to introduce an "unknown" class with medium size coding potential which we can then disregard.

Lets start by looking at the distribution of coding potential values

```
data("exampleSwitchListAnalyzed")
hist(exampleSwitchListAnalyzed$isoformFeatures$codingPotentialValue)
```
These coding potential values are summarized by the cutoff supplied in the 'codingPotential' column - which is what IsoformSwitchAnalyzeR uses in the downstream analysis
```
### These are summarized by the cutoff supplied in the 'codingPotential' column - which is what is used by IsoformSwitchAnalyzeR in the downstream analysis
table(exampleSwitchListAnalyzed$isoformFeatures$codingPotential, exclude = NULL)
```

By simply setting the mid-range values to NA it will cause IsoformSwitchAnalyzeR to ignore them thereby removing the isoforms with "unknown" coding potential. This can be done as follows:

```
exampleSwitchListAnalyzed$isoformFeatures$codingPotential <- NA
exampleSwitchListAnalyzed$isoformFeatures$codingPotential[which(exampleSwitchListAnalyzed$isoformFeatures$codingPotentialValue > 0.75)] <- TRUE
exampleSwitchListAnalyzed$isoformFeatures$codingPotential[which(exampleSwitchListAnalyzed$isoformFeatures$codingPotentialValue < 0.25)] <- FALSE

table(exampleSwitchListAnalyzed$isoformFeatures$codingPotential, exclude = NULL)
```

Back to [Table of Content]

<br />

### Quality control of ORF of known annotation
As we have shown there are quite a lot of problems with known CDS annotation (see supplementary data in Vitting-Seerup et al 2017) we have implemented two ways to ensure a high quality of the CDS imported from a GTF annotation file:

1) When you import the GTF file (via _importRdata()_ or _importGTF()_) you can enable the 'onlyConsiderFullORF' argument which makes sure you only add the ORF stored in the GTF file if it is annotated with both a start and stop codon.
2) When you extract the biological sequences (nucleotide and amino acid) with _extractSequence()_ you can enable the argument 'removeORFwithStop' which will remove ORFs which contains un-annotated stop codons, defined as * when the ORF nucleotide sequences is translated to the amino acid sequence. If enabled the ORFs will both be removed from the switchAnalyzeRList and from the sequences outputted by _extractSequence()_.

<br />

### Analyzing the Biological Mechanisms Behind Isoform Switching
The difference between the isoforms involved in an isoform switch can arise by changes in three distinct biological mechanisms:

1) Alternative Transcription Start Site (aTSS)
2) Alternative Splicing (AS)
3) Alternative Transcription Termination Site (aTTS)

Since we how the structure of the isoforms involved in a isoform switch we can also analyze which (combination) of these biological mechanisms gives rise to the difference between the two isoforms involved in a isoform switches.

This is simply done by, in addition to running _analyzeSwitchConsequences_ with the consequences you find interesting, you make a separate consequence analysis of consequences (also with _analyzeSwitchConsequences_) where the consequences you analyze (supplied to the _consequencesToAnalyze_ argument) are:

1) 'tss' - which will analyze the isoforms for aTSS
2) 'intron_structure' - which will analyze the isoforms for AS
3) 'tts' - which will analyze the isoforms for aTSS

Then we can simply compare the result of this analysis to the isoform switches with consequences we already have identified to be of interest and thereby identify which biological mechanisms gives rise to the isoform switches with consequence you are interested in. One suggestion for such an analysis are illustrated here:

```{r, results = "hide", message = FALSE}
### Load example data
data("exampleSwitchListAnalyzed")

### Reduce datasize for fast runtime
randomGenes <- sample(unique(exampleSwitchListAnalyzed$isoformFeatures$gene_id), size = 40)
exampleSwitchListAnalyzedSubset <- subsetSwitchAnalyzeRlist(
    exampleSwitchListAnalyzed,
    exampleSwitchListAnalyzed$isoformFeatures$gene_id %in% randomGenes
)

### analyze the biological mechanismes
bioMechanismeAnalysis <- analyzeSwitchConsequences(
    exampleSwitchListAnalyzedSubset, 
    consequencesToAnalyze = c('tss','tts','intron_structure'),
    showProgress = FALSE
)$switchConsequence # only the consequences are interesting here

### subset to those with differences
bioMechanismeAnalysis <- bioMechanismeAnalysis[which(bioMechanismeAnalysis$isoformsDifferent),]

### extract the consequences of interest alerady stored in the switchAnalyzeRlist
myConsequences <- exampleSwitchListAnalyzedSubset$switchConsequence
myConsequences <- myConsequences[which(myConsequences$isoformsDifferent),]
myConsequences$isoPair <- paste(myConsequences$isoformUpregulated, myConsequences$isoformDownregulated) # id for specific iso comparison

### Obtain the mechanisms of the isoform switches with consequences
bioMechanismeAnalysis$isoPair <- paste(bioMechanismeAnalysis$isoformUpregulated, bioMechanismeAnalysis$isoformDownregulated)
bioMechanismeAnalysis <- bioMechanismeAnalysis[which(bioMechanismeAnalysis$isoPair %in% myConsequences$isoPair),]  # id for specific iso comparison
```

This result is best summarized in a Venn diagram:

```{r, fig.width=3.5, fig.height=3.5}
### Create list with the isoPair ids for each consequencee
AS   <- bioMechanismeAnalysis$isoPair[ which( bioMechanismeAnalysis$featureCompared == 'intron_structure')]
aTSS <- bioMechanismeAnalysis$isoPair[ which( bioMechanismeAnalysis$featureCompared == 'tss'             )]
aTTS <- bioMechanismeAnalysis$isoPair[ which( bioMechanismeAnalysis$featureCompared == 'tts'             )]

mechList <- list(
    AS=AS,
    aTSS=aTSS,
    aTTS=aTTS
)

### Create venn diagram
library(VennDiagram)
myVenn <- venn.diagram(mechList, col='transparent', alpha=0.4, fill=brewer.pal(n=3,name='Dark2'), filename=NULL)

### Plot the venn diagram
grid.newpage() ; grid.draw(myVenn)

```

From which the relative importance of each of the three mechanisms, as well as the combination of these, can be seen.

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<br />

## Frequently Asked Questions and Problems
None yet but we will continously add the most common problems and questions raised on github or in the google group (see [How To Get Help]).

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<br />

## Final Remarks
With this vignette, we hope to provide a thorough introduction to IsoformSwitchAnalyzeR as well as give some examples of what IsoformSwitchAnalyzeR can be used for.

We aim to continuously keep IsoformSwitchAnalyzeR up to date and update it. The update aspect includes the integration of new tools as they are developed (isoform quantification, isoform switch identification, statistical ORF detection, external sequence analysis etc.) so please feel free to suggest new tools to us (see the [How To Get Help] section for info of how to get in contact). Tools should preferably either be light weight (runnable on an old laptop) and/or available via web-services as that will allow a larger audience to use it.

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## Sessioninfo
```{r}
sessionInfo()
```

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<br />
