IsoformSwitchAnalyzeR

Background and Package Description

The usage of Alternative Transcription Start sites (aTSS), Alternative Splicing (AS) and alternative Transcription Termination Sites (aTTS) are collectively collectively results in the production of different isoforms. Alternative isoforms are widely used as recently demonstrated by The ENCODE Consortium, which found that on average, 6.3 different transcripts are generated per gene; a number which may vary considerably per gene.

The importance of analyzing isoforms instead of genes has been highlighted by many examples showing functionally important changes. One of these examples is the pyruvate kinase. In normal adult homeostasis, cells use the adult isoform (M1), which supports oxidative phosphorylation. However, almost all cancer cells use the embryonic isoform (M2), which promotes aerobic glycolysis, one of the hallmarks of cancer. Such shifts in isoform usage are termed ‘isoform switching’ and cannot be detected at when only analyzing data on gene level.

On a more systematic level several recent studies suggest that isoform switches are quite common since they often identify hundres of switche events.

Tools such as Cufflinks, Salmon and Kallisto allows for reconstruction and quantification of full-length transcripts from RNA-seq data. Such data has the potential to facilitate genome-wide analysis of alternative isoform usage and identification of isoform switching — but unfortunately these types of analyses are still only rarely done; most analyses are on gene level only.

We hypothesize 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
2. Although there are many excellent 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 visualization of isoform switches.

To solve all these problems, we developed IsoformSwitchAnalyzeR.

IsoformSwitchAnalyzeR is an easy-to-use R package that enables the user to import (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 structures are predicted (either de-novo or guided) IsoformSwitchAnalyzeR offers an accurate tool for identifying the dominant ORF of the isoforms. The knowledge of isoform positions for the CDS/ORF allows for prediction of sensitivity to Nonsense Mediated Decay (NMD) — the mRNA quality control machinery that degrades isoforms with pre-mature termination codons (PTC).

IsoformSwitchAnalyzeR facilitates identification of isoform switches via newly developed statistical methods that tests each individual isoform for differential usage and thereby identifies the exact isoforms involved in an 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 be used to increase accuracy of ORF predictions. By combining the CDS/ORF isoform positions with the nucleotide sequence, we can extract the most likely amino acid sequence of the CDS/ORF. The amino acid sequence enables integration of analysis of protein domains (via Pfam) and signal peptides (via SignalP) — both supported by IsoformSwitchAnalyzeR. Lastly, since the structures of all expressed isoforms from a given gene are known, one can also annotate alternative splicing - including retentions - a functionality also implemented in IsoformSwitchAnalyzeR.

Thus, in summary, IsoformSwitchAnalyzeR enables annotation of isoforms with intron retention, ORF, NMD sensitivity, coding potential, protein domains and signal peptides (and many more), resulting in the ability to predict important functional consequences of isoform switches.

IsoformSwitchAnalyzeR contains tools that allow the user to create article-ready visualizations of:

1. Individual isoform switches
2. Genome-wide analysis of isoform switches and their predicted consequences
3. Genome-wide analysis of alternative splicing and isoform switches and their predicted consequences.

These visualizations are easy to understand and integrate all information gathered throughout the workflow. Example of visualizations can be found in the Examples Visualizations section.

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

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Installation

IsoformSwitchAnalyzeR is part of the Bioconductor repository and community which means it is distributed with, and dependent on, Bioconductor. Installation of IsoformSwitchAnalyzeR is 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 an R session to install the basic Bioconductor packages:

install.packages("BiocManager")
BiocManager::install()

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

After you have installed the basic Bioconductor packages you can install IsoformSwitchAnalyzeR by copy-pasting the following into an R session:

BiocManager::install("IsoformSwitchAnalyzeR")

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

If you need to install from the developmental branch of Bioconductor it is nesseary to explicitely specify that. Please note that this is for advanced uses and should not be done unless you have good reason to. Installation from the developmental branch can be done by copy/pasting:

BiocManager::install("IsoformSwitchAnalyzeR", version = "devel")

How To Get Help

This R package comes with plenty 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 “?importRdata”) - here a lot of information can be found in the individual argument description as well as in the details section. This vignette contains a lot of information and will be continously updated, so make sure to read both sources carefully as it contains the answers to the most Frequently Asked Questions, Problems and Errors.

If you want to report a bug/error (found in the newest version of the R package!) please make an issue with a reproducible example at github.

If you have unanswered questions or comments regarding IsoformSwitchAnalyzeR or how to run it please post them on the associated google group (after make sure the question was not already answered).

If you have suggestions for improvements, please put them on github. This will allow other people to upvote your idea, thereby showing us there is wide support of implementing your idea.

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Overview of Isoform Switch Workflow

The idea behind IsoformSwitchAnalyzeR is to make it easy to do advanced post-analysis of full-length RNA-seq derived transcripts quantification with a focus on finding, annotating and visualizing isoform switches with functional consequences. Furthermore IsoformSwitchAnalyzeR also allows for analysis of alternative splicing. Here we will go though the isoform switch workflow. For the workflow of alternative splicing see Analyzing Alternative Splicing. If you want to know more about recomendations for bioinformatic tools which can perform the initial isoform quantifications please refere to What Quantification Tool(s) Should I Use?.

From the isoform quantifications IsoformSwitchAnalyzeR performs three high-level tasks:

• Statistical identification of isoform switches.
• Annotation of the transcripts involved in the isoform switches.
• Visualization of predicted consequences of the isoform switches, for individual switches and globally.

Please note that just like any other statistical tool IsoformSwitchAnalyzeR requires independent biological replicates (see What constitute an independent biological replicate?) and that recent benchmarks highlights that at least three independent replicates are needed for good statistical performance - most tools have a hard time controling the False Discovery Rate (FDR) with only two replicates so extra caution is needed for interpreting results based on few replicates.

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

Part 1) Extract Isoform Switches and Their Sequences. This part includes importing the data into R, identifying isoform switches, annotating those switches with open reading frames (ORF) and extracting the nucleotide and amino acid (peptide) sequences. The latter 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.
• Pfam : Prediction of protein domains, which can be run either locally or via their webserver.
• SignalP : Prediction of Signal Peptides, which can be run either locally or via their webserver.

All of the above steps are performed by the high-level function:

isoformSwitchAnalysisPart1()

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

Part 2) Plot All Isoform Switches and Their annotation. This part involves importing and incorporating the results of the external sequence analysis, identifying intron retention, predicting functional consequences and plotting i) all genes with isoform switches and ii) summaries of 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.

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

isoformSwitchAnalysisCombined()

This corresponds to running isoformSwitchAnalysisPart1() and isoformSwitchAnalysisPart2() without adding the external results.

Figure 1: Workflow overview. 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. Speech bubbles summarize how this full analysis can be done in a two-step process using the high-level functions (isoformSwitchAnalysisPart1() and isoformSwitchAnalysisPart2()).

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Example Workflow

As indicted above, 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

library(IsoformSwitchAnalyzeR)

Note that newer versions of RStudio support auto-completion of package names.

### Please note
# The way of importing files in the following example with
# "system.file('pathToFile', package="IsoformSwitchAnalyzeR") is
# specialiced way of accessing the example data in the IsoformSwitchAnalyzeR package
# and not somhting you need to do - just supply the string e.g.
# "/mySalmonQuantifications/" pointing to the parent directory (where 
# each sample is a seperate sub-directory) to the function.

### Import quantifications
salmonQuant <- importIsoformExpression(
    parentDir = system.file("extdata/",package="IsoformSwitchAnalyzeR")
)
#> Step 1 of 3: Identifying which algorithm was used...
#>     The quantification algorithm used was: Salmon
#> Step 2 of 3: Reading data...
#>     Found 6 quantification file(s) of interest
#> reading in files with read_tsv
#> 1 2 3 4 5 6 
#> Step 3 of 3: Normalizing FPKM/TxPM values via edgeR...
#> Done

head(salmonQuant$abundance, 2)
#>       isoform_id Fibroblasts_0 Fibroblasts_1 hESC_0    hESC_1    iPS_0
#> 1 TCONS_00000001      11.36932       14.0215      0 0.0000000  0.00000
#> 2 TCONS_00000002       0.00000        0.0000      0 0.6904076 13.07866
#>       iPS_1
#> 1 11.303557
#> 2  6.838089

head(salmonQuant$counts, 2)
#>       isoform_id Fibroblasts_0 Fibroblasts_1 hESC_0    hESC_1    iPS_0
#> 1 TCONS_00000001      12.30707      14.02487      0 0.0000000  0.00000
#> 2 TCONS_00000002       0.00000       0.00000      0 0.1116201 21.10248
#>      iPS_1
#> 1 18.13313
#> 2 10.96964

myDesign <- data.frame(
    sampleID = colnames(salmonQuant$abundance)[-1],
    condition = gsub('_.*', '', colnames(salmonQuant$abundance)[-1])
)
myDesign
#>        sampleID   condition
#> 1 Fibroblasts_0 Fibroblasts
#> 2 Fibroblasts_1 Fibroblasts
#> 3        hESC_0        hESC
#> 4        hESC_1        hESC
#> 5         iPS_0         iPS
#> 6         iPS_1         iPS

### Please note
# The way of importing files in the following example with
# "system.file("extdata/example.gtf.gz", package="IsoformSwitchAnalyzeR")"" is
# specialiced way of accessing the example data in the IsoformSwitchAnalyzeR package
# and not somhting you need to do - just supply the string e.g.
# "/myAnnotation/myQuantified.gtf" to the isoformExonAnnoation argument

### Create switchAnalyzeRlist
aSwitchList <- importRdata(
    isoformCountMatrix   = salmonQuant$counts,
    isoformRepExpression = salmonQuant$abundance,
    designMatrix         = myDesign,
    isoformExonAnnoation = system.file("extdata/example.gtf.gz", package="IsoformSwitchAnalyzeR"),
    showProgress = FALSE
)
aSwitchList
#> This switchAnalyzeRlist list contains:
#>  1092 isoforms from 362 genes
#>  3 comparison from 3 conditions

data("exampleSwitchList")
exampleSwitchList
#> This switchAnalyzeRlist list contains:
#>  259 isoforms from 84 genes
#>  1 comparison from 2 conditions
#> 
#> Switching features:
#>            Comparison switchingIsoforms switchingGenes
#> 1 hESC vs Fibroblasts                 0              0
#> 
#> Feature analyzed:
#> [1] "Isoform Swich Identification"

### isoformSwitchAnalysisPart1 needs the genomic sequence to predict ORFs

# Genome sequences are available from Bioconductor as BSgenome objects: 
# http://bioconductor.org/packages/release/BiocViews.html#___BSgenome
# Here we use the hg19 reference genome - which can be downloaded by
# copy/pasting the following two lines into the R terminal:
# BiocManager::install("BSgenome.Hsapiens.UCSC.hg19")

library(BSgenome.Hsapiens.UCSC.hg19)

exampleSwitchList <- isoformSwitchAnalysisPart1(
    switchAnalyzeRlist = exampleSwitchList,
    genomeObject = Hsapiens, # the reference to the human BS genome
    dIFcutoff = 0.3,         # Cutoff for finding switches - set high for short runtime in example data
    # pathToOutput = 'path/to/where/ouput/should/be/'
    outputSequences = FALSE # prevents outputting of the fasta files used for external sequence analysis
)

extractSwitchSummary(
    exampleSwitchList,
    dIFcutoff = 0.3 # supply the same cutoff to the summary function
)
#>            Comparison nrIsoforms nrGenes
#> 1 hESC vs Fibroblasts         31      18

# Please note that in the following the part of the examples using
# the "system.file()" commandis not nesseary when using your own
# data - just supply the path as a string
# (e.g. pathToCPATresultFile = "/myFiles/myCPATresults.txt" )

exampleSwitchList <- isoformSwitchAnalysisPart2(
    switchAnalyzeRlist      = exampleSwitchList, 
    dIFcutoff               = 0.3,   # Cutoff for finding switches - 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
)

extractSwitchSummary(
    exampleSwitchList,
    filterForConsequences = TRUE,
    dIFcutoff = 0.3               # supply the same cutoff to the summary function
) 
#>            Comparison nrIsoforms nrGenes
#> 1 hESC vs Fibroblasts         24      12

extractTopSwitches(exampleSwitchList, filterForConsequences = TRUE, n=3)
#>            gene_ref           gene_id gene_name condition_1 condition_2
#> 1 geneComp_00000237 XLOC_000202:SRRM1     SRRM1        hESC Fibroblasts
#> 2 geneComp_00000098 XLOC_000088:KIF1B     KIF1B        hESC Fibroblasts
#> 3 geneComp_00000389       XLOC_001345  ARHGEF19        hESC Fibroblasts
#>   gene_switch_q_value switchConsequencesGene Rank
#> 1       1.553471e-185                   TRUE    1
#> 2        1.879647e-87                   TRUE    2
#> 3        1.312385e-83                   TRUE    3

Examples Visualizations

Let’s take a look at the isoform switch in the SRRM1 gene via the switch plot produced by IsoformSwitchAnalyzeR:

switchPlot(exampleSwitchList, gene='SRRM1')

From this plot, we first note that the gene expression is not significantly changed (bottom left). Next, we see the large significant switch in isoform usage across conditions (bottom right). By comparing which isoforms are changing (bottom right) to the isoform structure (top) it can be deduced that in hESC it is primarly the short isoforms that is used, but as the cells are differentiated to fibroblasts, there is a change towards mainly using the long isoform. Interestingly, this isoform switch seems to result in a truncation of the PWI domain which is important for DNA/RNA binding. SRRM1 is a splice factor and this switch could lead to the hypothesis that SRRM1 is nonfunctional in hESC but functional in Fibroblasts even though the total output from the gene is the same — a hypothesis which would naturallt have to be confirmed experimentally.

Note that if you want to save this plot as a pdf file via the pdf command, you need to specify “onefile = FALSE”. The folloing code chunk will produce a nicely-sized figure:

pdf(file = '<outoutDirAndFileName>.pdf', onefile = FALSE, height=5, width = 8)
switchPlot(exampleSwitchList, gene='SRRM1')
dev.off()

Furthermore, note that:

• If the switchAnalyzeRList contains multiple comparison, you will also need to specify the ‘condition1’ and ‘condition2’ arguments in the switchPlot() function to indicate specifically which comparison you want to plot.
• The differential isoform/gene expression analysis is not a part of the IsoformSwitchAnalyzeR workflow but can easily be added as described in Adding differential gene expression.
• the switchPlot() function have a argument called IFcutoff which requires the Isoform Fraction of an isforom to be larger than IFcutoff (default 0.01) to be included in the plot. Increasing this cutoff can result in “cleaner” plots as minor isoforms will be removed.

To illustrate the genome-wide analysis of consequences of isoform switching in the different comparisons, we will use a larger dataset which is a subset of two of the TCGA Cancer types analyzed in Vitting-Seerup et al 2017.

data("exampleSwitchListAnalyzed")

The first step is to take a look at the number and overlap of isoform switches. The numbers can be extracted via the extractSwitchSummary() function and by setting “filterForConsequences=TRUE” we only extract the features involved in a switch which is predicted to have a functional consequence:

extractSwitchSummary(
    exampleSwitchListAnalyzed,
    filterForConsequences=TRUE
)
#>                 Comparison nrIsoforms nrGenes
#> 1 COAD_ctrl vs COAD_cancer        690     368
#> 2 LUAD_ctrl vs LUAD_cancer        422     218
#> 3                 combined       1008     529

The number, and overlap, between condtions can be viusalized with the extractSwitchOverlap() function:

extractSwitchOverlap(
    exampleSwitchListAnalyzed,
    filterForConsequences=TRUE
)

We can look more into the details of the consequences by considering each type consequence seperately as follows:

extractConsequenceSummary(
    exampleSwitchListAnalyzed,
    consequencesToAnalyze='all',
    plotGenes = FALSE,           # enables analysis of genes (instead of isoforms)
    asFractionTotal = FALSE      # enables analysis of fraction of significant features
)

Note the “consequencesToAnalyze” argument enables analysis of only a subset of features.

From this summary plot many conclusions are possible. First of all, the most frequent changes are changes affecting protein domains and ORFs. Secondly, intron retention is more commonin LUAD than in COAD. Lastly, when considering oppositeconsequence (e.g. the gain vs loss of protein domains) its quite easy to see they are unevenly distributed (e.g. more protein domain loss than protein domain gain). This uneven distribution can be systematically analyzed using the build in enrichment analysis:

extractConsequenceEnrichment(
    exampleSwitchListAnalyzed,
    consequencesToAnalyze='all',
    analysisOppositeConsequence = TRUE
)

For each pair of oppositeconsequences (e.g. protein domain loss vs gain) (y-axis) this plot shows the fraction of switches, affected by either of the opposing consequence, that results in the consequnce indicated (e.g. protein domain loss) (x-axis). If this fraction is significantly different from 0.5 it indicates there is a systematic biases in which consequence is detected.

From the analysis above, it is therefore quite clear, that many of the oposing consequences are significantly unevenly distributed. In other words, many types of consequences seems to be used in a group-specific manner.

When comparing the two cancer types (right vs left plot) the overall pattern seems similar - but there might be differences. This can formally be analyzed with the extractConsequenceEnrichmentComparison() function. This function with for each oposing set of consequence (e.g. protein domain loss vs gain), in a pairwise manner contrasts the individual comparisons to assess whether the ratio of loss/gains are indeed significantly different:

extractConsequenceEnrichmentComparison(
    exampleSwitchListAnalyzed,
    consequencesToAnalyze=c('domains_identified','intron_retention','coding_potential'),
    analysisOppositeConsequence = TRUE
)

#>                                             comparisonsCompared
#> 1 COAD_ctrl vs COAD_cancer compared to LUAD_ctrl vs LUAD_cancer
#> 2 COAD_ctrl vs COAD_cancer compared to LUAD_ctrl vs LUAD_cancer
#> 3 COAD_ctrl vs COAD_cancer compared to LUAD_ctrl vs LUAD_cancer
#>                                                  consequence
#> 1                      Domain loss (paired with Domain gain)
#> 2  Intron retention loss (paired with Intron retention gain)
#> 3 Transcript is coding (paired with Transcript is Noncoding)
#>   propUp_comparison_1 propUp_comparison_2 fisherQvalue Significant
#> 1           0.8220065           0.6405229 7.891585e-05        TRUE
#> 2           0.3661972           0.2881356 3.575517e-01       FALSE
#> 3           0.1851852           0.3529412 1.920463e-01       FALSE

The analysis shows that compared to LUAD, COAD have a significantly higher fraction of switches resulting in protein domain loss, but there is no difference in terms of intron retention loss or switches from noncoding to to coding transcripts.

Such a results leads to the question what cellular mechanism underlies behind these changes. Due to the detailed alternative splicing analysis performed we can analyze this using the built-in summary function:

extractSplicingEnrichment(exampleSwitchListAnalyzed)

Which is equivivalent to the consequnce enrichment analysis described above. From this analysis, we see that although the patterns of consequences looked somewhat slimiar (as illustrated above), the underlying consequences are quite different. COAD utilizes alternative 3’ acceptor sites (A3), multiple exon skipping (MES) and alternative transcription start sites (ATSS) while LUAD utilize intron retention (IR) and alternative transcription termination sites (ATTS) more. For more details (and functionality) regarding splicing analysis see the Genome-Wide Analysis of Alternative Splicing section.

Since all the data about isoform and gene expression is saved in the switchAnalyzeRlist, we can also make a set of overview plots:

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_wrap( ~ condition_2) +
    #facet_grid(condition_1 ~ condition_2) + # alternative to facet_wrap if you have overlapping conditions
    scale_color_manual('Signficant\nIsoform Switch', values = c('black','red')) +
    labs(x='dIF', y='-Log10 ( Isoform Switch Q Value )') +
    theme_bw()

As there are many dIF values (effect size) very close to zero, which have a significant isoform switch (black dots above dashed hoizontal line) this nicely illustrates why a cutoffs both on the dIF and the q-value are necessary.

Another interesting overview plot can be made as follows:

### 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_wrap(~ condition_2) +
    #facet_grid(condition_1 ~ condition_2) + # alternative to facet_wrap if you have overlapping conditions
    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()

Here, it is clear that changes in gene expression and isoform switches are not necessarily mutually exclusive, as there are many genes which are both differentially expressed (large gene log2FC) and contain isoform switches (color). This also highlights the importance of also analyzing RNA-seq data at isoform-level resolution.

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Overview of Detailed Workflow

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 gives a couple of tips not repeated here.

Compared to the workflow presented in Quick Start, a full workflow for analyzing isoform switches has many 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 build into the R package (easily accessed via ?functionName).

First we will start by getteing an overview of the detailed workflow before diving into the individual parts. Then we will introduce the IsoformSwitchAnalyzeR Background Information - all you need to know before doing the detailed analysis. This is followed by a detailed step-by-step isoform switch analysis workflow and lastly an overview of useful Other Tools in IsoformSwitchAnalyzeR is provided.

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
  • Importing Data from Kallisto, Salmon, RSEM or StringTie
  • Importing Data from Cufflinks/Cuffdiff
  • Importing Data from other Full-length Transcript Assemblers
• Filtering
• Identifying Isoform Switches
• Analyzing Open Reading Frames
• Extracting Nucleotide and Amino Acid Sequences
• Advice 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 into R via one of:
myQantifications <- importIsoformExpression() # RSEM/Kallisto/Salmon/StringTie
myCuffDb         <- readCufflinks()           # Cufflinks/Cuffdiff

### Create SwitchAnalyzeRlist
mySwitchList <- importRdata()         # OR
mySwitchList <- importCufflinksData()

### Filter
mySwitchList <- preFilter( mySwitchList )

### Test for isoform switches
mySwitchList <- isoformSwitchTestDEXSeq( mySwitchList )   # OR
mySwitchList <- isoformSwitchTestDRIMSeq( mySwitchList ) 

### If novel isoforms (else use CDS from ORF as explained in importRdata() )
mySwitchList <- analyzeORF( mySwitchList )

### Extract Sequences
mySwitchList <- extractSequence( mySwitchList )

### Summary
extractSwitchSummary(mySwitchList)

Part 2) Plot All Isoform Switches and Their annotation.

• Importing External Sequence Analysis
• Predicting Alternative Splicing
• Predicting Switch Consequences
• Post Analysis of Isoform Switches with Consequences
  • Analysis of Individual Isoform Switching
  • Genome-Wide Analysis of Isoform Switching

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 <- analyzeAlternativeSplicing( mySwitchList )

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

### Visual analysis
# Indiviudal switches
switchPlotTopSwitches( mySwitchList )

### Summary
extractSwitchSummary(mySwitchList, filterForConsequences = TRUE)

For a normal workflow this would then typically be followed by the global analysis of alternative splcing and consequences consequences:

# global consequence analysis
extractConsequenceSummary( mySwitchList )
extractConsequenceEnrichment( mySwitchList )
extractConsequenceGenomeWide( mySwitchList )

# global splicing analysis
extractSplicingSummary( mySwitchList )
extractSplicingEnrichment( mySwitchList )
extractSplicingGenomeWide( mySwitchList )

The combined workflow is visualized here:

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.

IsoformSwitchAnalyzeR Background Information

data("exampleSwitchList")         # A newly created switchAnalyzeRlist + switch analysis
names(exampleSwitchList)
#> [1] "isoformFeatures"       "exons"                 "conditions"           
#> [4] "designMatrix"          "isoformCountMatrix"    "sourceId"             
#> [7] "isoformSwitchAnalysis"

data("exampleSwitchListAnalyzed") # A fully analyzed switchAnalyzeRlist
names(exampleSwitchListAnalyzed)
#> [1] "isoformFeatures"             "exons"                      
#> [3] "conditions"                  "sourceId"                   
#> [5] "orfAnalysis"                 "domainAnalysis"             
#> [7] "signalPeptideAnalysis"       "AlternativeSplicingAnalysis"
#> [9] "switchConsequence"

### Preview
head(exampleSwitchList, 2)
#>             iso_ref          gene_ref     isoform_id     gene_id
#> 19 isoComp_00000007 geneComp_00000005 TCONS_00000007 XLOC_000005
#> 22 isoComp_00000008 geneComp_00000005 TCONS_00000008 XLOC_000005
#>    condition_1 condition_2 gene_name nearest_ref_id class_code
#> 19        hESC Fibroblasts      <NA>     uc009vjk.2          =
#> 22        hESC Fibroblasts      <NA>     uc001aau.2          =
#>    TSS_group_id length              locus gene_status gene_overall_mean
#> 19         TSS2   2750 chr1:322036-328580          OK          193.8339
#> 22         TSS3   4369 chr1:322036-328580          OK          171.6605
#>    gene_value_1 gene_value_2 gene_stderr_1 gene_stderr_2
#> 19      696.704      48.0566      3.592857      2.307488
#> 22      696.704      48.0566      3.592857      2.307488
#>    gene_log2_fold_change gene_p_value gene_q_value gene_significant
#> 19              -3.85774  2.66665e-09  3.20379e-08              yes
#> 22              -3.85774  2.66665e-09  3.20379e-08              yes
#>    iso_status iso_overall_mean iso_value_1 iso_value_2 iso_stderr_1
#> 19         OK         372.3803     358.383    29.28480     2.091049
#> 22         OK         372.3803     338.308     5.01291     1.322809
#>    iso_stderr_2 iso_log2_fold_change iso_p_value iso_q_value
#> 19    17.489950             -3.61328 4.83698e-05 0.000548629
#> 22     6.999295             -6.07655 2.64331e-03 0.015898000
#>    iso_significant IF_overall       IF1       IF2         dIF
#> 19             yes  0.5618896 0.5143978 0.6093814  0.09498364
#> 22             yes  0.2949481 0.4855835 0.1043126 -0.38127092
#>    isoform_switch_q_value gene_switch_q_value
#> 19                     NA                   1
#> 22                     NA                   1
# tail(exampleSwitchList, 2)

### Dimentions
dim(exampleSwitchList$isoformFeatures)
#> [1] 259  38

nrow(exampleSwitchList)
#> [1] 259
ncol(exampleSwitchList)
#> [1] 38
dim(exampleSwitchList)
#> [1] 259  38

exampleSwitchList
#> This switchAnalyzeRlist list contains:
#>  259 isoforms from 84 genes
#>  1 comparison from 2 conditions
#> 
#> Switching features:
#>            Comparison switchingIsoforms switchingGenes
#> 1 hESC vs Fibroblasts                 0              0
#> 
#> Feature analyzed:
#> [1] "Isoform Swich Identification"

### Subset
subsetSwitchAnalyzeRlist(
    exampleSwitchList,
    exampleSwitchList$isoformFeatures$gene_name == 'ARHGEF19'
)
#> This switchAnalyzeRlist list contains:
#>  3 isoforms from 1 genes
#>  1 comparison from 2 conditions
#> 
#> Switching features:
#>            Comparison switchingIsoforms switchingGenes
#> 1 hESC vs Fibroblasts                 0              0
#> 
#> Feature analyzed:
#> [1] "Isoform Swich Identification"

head(exampleSwitchList$exons,2)
#> GRanges object with 2 ranges and 2 metadata columns:
#>       seqnames        ranges strand |     isoform_id     gene_id
#>          <Rle>     <IRanges>  <Rle> |    <character> <character>
#>   [1]     chr1 322037-322228      + | TCONS_00000007 XLOC_000005
#>   [2]     chr1 324288-324345      + | TCONS_00000007 XLOC_000005
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

createSwitchAnalyzeRlist()
preFilter()

Importing Data Into R

IsoformSwitchAnalyzeR can analyze the output from any tool that quantifies (annoated and/or de-novo/guided deconvoluted) full-length isoforms. All it requires are the following four sets of data:

• Isoform quantification (preferably both counts and abundance estimates (RPKM/FPKM/TPM)) in multiple samples from each condition (independent replicates are essential if differential isoform usage should be identified, see What constitute an independent biological replicate?)
• The genomic coordinates of the isoform exon structure.
• Annotation of which isoform belongs to which gene.
• A design matrix indicating which samples belong to which conditions.

It is highly recommended to obtain the estimated replicate abundances and use them in the construction of the switchAnalyzeRlist as they better allow for effect size estimates.

From these data, a switchAnalyzeRlist (see The switchAnalyzeRlist for description) can be constructed. An important addition is that the design matrix (a data.frame with two mandatory columns: sampleID and condition) also allows for incorporation of additional cofactors (sources of variation not of interest for the the analysis you are doing) in the experimental setup (see How to handle cofounding effects (including batches) for more information).

To facilitate the usage of IsoformSwitchAnalyzeR, several dedicated wrappers for constructing a switchAnalyzeRlist from different data sources are included in IsoformSwitchAnalyzeR. See the following sections for details about the specific import methods:

• Importing Data from Kallisto, Salmon, RSEM or StringTie
• Importing Data from Cufflinks/Cuffdiff
• Importing Data from other Full-length Transcript Assemblers

For recomendations of bioinformatic tools which can perform the quantifications please refere to What Quantification Tool(s) Should I Use?

### Please note
# The way of importing files in the following example with
# "system.file('pathToFile', package="IsoformSwitchAnalyzeR") is
# specialiced way of accessing the example data in the IsoformSwitchAnalyzeR package
# and not somhting you need to do - just supply the string e.g.
# "/mySalmonQuantifications/" pointing to the parent directory (where 
# each sample is a seperate sub-directory) to the function.

### Import Salmon example data in R package
salmonQuant <- importIsoformExpression(
    parentDir = system.file("extdata/", package="IsoformSwitchAnalyzeR"),
    addIsofomIdAsColumn = TRUE
)
#> Step 1 of 3: Identifying which algorithm was used...
#>     The quantification algorithm used was: Salmon
#> Step 2 of 3: Reading data...
#>     Found 6 quantification file(s) of interest
#> reading in files with read_tsv
#> 1 2 3 4 5 6 
#> Step 3 of 3: Normalizing FPKM/TxPM values via edgeR...
#> Done

### Make design matrix
myDesign <- data.frame(
    sampleID = colnames(salmonQuant$abundance)[-1],
    condition = gsub('_.*', '', colnames(salmonQuant$abundance)[-1])
)

### Create switchAnalyzeRlist
aSwitchList <- importRdata(
    isoformCountMatrix   = salmonQuant$counts,
    isoformRepExpression = salmonQuant$abundance,
    designMatrix         = myDesign,
    isoformExonAnnoation = system.file("extdata/example.gtf.gz", package="IsoformSwitchAnalyzeR"),
    showProgress = FALSE
)
#> Step 1 of 6: Checking data...
#> Step 2 of 6: Obtaining annotation...
#>     importing GTF (this may take a while)
#> Warning in importRdata(isoformCountMatrix = salmonQuant$counts, isoformRepExpression = salmonQuant$abundance, :    No CDS annotation was found in the GTF files meaning ORFs could not be annotated.
#>      (But ORFs can still be predicted with the analyzeORF() function)
#> Step 3 of 6: Calculating gene expression and isoform fraction...
#>      111 ( 9.23%) isoforms were removed since they were not expressed in any samples.
#> Step 4 of 6: Merging gene and isoform expression...
#> Step 5 of 6: Making comparisons...
#> Step 6 of 6: Making switchAnalyzeRlist object...
#> Done

importCufflinksFiles()

library(cummeRbund)

### Please note
# The way of importing file in this example with
# "system.file('pathToFile', package="cummeRbund") is
# specialiced to access the example data in the cummeRbund package
# and not something you need to do - just supply the string e.g.
# "/myCuffdiffFiles/myFile.filetype" to the argument

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

aSwitchList <- importGTF(pathToGTF = system.file("extdata/example.gtf.gz", package="IsoformSwitchAnalyzeR"))

aSwitchList
#> This switchAnalyzeRlist list contains:
#>  1203 isoforms from 400 genes
#>  1 comparison from 2 conditions
#> 
#> Feature analyzed:
#> [1] "ORFs"

head(aSwitchList,2)
#>            iso_ref          gene_ref     isoform_id     gene_id
#> 3 isoComp_00000001 geneComp_00000001 TCONS_00000001 XLOC_000001
#> 2 isoComp_00000002 geneComp_00000001 TCONS_00000002 XLOC_000001
#>    condition_1  condition_2 gene_name class_code gene_overall_mean
#> 3 plaseholder1 plaseholder2      <NA>          =                 0
#> 2 plaseholder1 plaseholder2      <NA>          =                 0
#>   gene_value_1 gene_value_2 gene_stderr_1 gene_stderr_2
#> 3            0            0            NA            NA
#> 2            0            0            NA            NA
#>   gene_log2_fold_change gene_p_value gene_q_value iso_overall_mean
#> 3                     0            1            1                0
#> 2                     0            1            1                0
#>   iso_value_1 iso_value_2 iso_stderr_1 iso_stderr_2 iso_log2_fold_change
#> 3           0           0           NA           NA                    0
#> 2           0           0           NA           NA                    0
#>   iso_p_value iso_q_value IF_overall IF1 IF2 dIF isoform_switch_q_value
#> 3           1           1         NA  NA  NA  NA                     NA
#> 2           1           1         NA  NA  NA  NA                     NA
#>   gene_switch_q_value PTC
#> 3                  NA  NA
#> 2                  NA  NA
head(aSwitchList$conditions,2)
#>      condition nrReplicates
#> 1 plaseholder1            1
#> 2 plaseholder2            1

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 switches. Examples of such could be single isoform genes or non-expressed isoforms. These extra genes/isoforms will make the downstream analysis take (much) longer than necessary. Therefore we have implemented a pre-filtering step to remove these features before continuing with the analysus. Importantly, filtering can enhance the reliability of the downstream analysis as described in detail below.

By using preFilter() it is possible to remove genes and isoforms from all aspects of the switchAnalyzeRlist by 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 isoform 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 of lowly 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 without losing important isoforms.

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

The preFilter() function is used as follows:

data("exampleSwitchList")

exampleSwitchListFiltered <- preFilter(
    switchAnalyzeRlist = exampleSwitchList,
    geneExpressionCutoff = 1,
    isoformExpressionCutoff = 0,
    removeSingleIsoformGenes = TRUE
)
#> The fitering removed 52 ( 20.08% of ) transcripts. There is now 207 isoforms left

exampleSwitchListFilteredStrict <- preFilter(
    switchAnalyzeRlist = exampleSwitchList,
    geneExpressionCutoff = 10,
    isoformExpressionCutoff = 3,
    removeSingleIsoformGenes = TRUE
)
#> The fitering removed 87 ( 33.59% of ) transcripts. There is now 172 isoforms left

Back to Table of Content

Identifying Isoform Switches

As identification of isoform switches are essential for IsoformSwitchAnalyzeR, multiple ways of identifying isoform switches are supported. Currently IsoformSwitchAnalyzeR directly supports three different approaches:

• DEXSeq : Using DEXSeq (state-of-the art) to test for differentil isoform usage. DEXSeq was originally designed for testing for differential exon usage but have recently been shown to perform exceptionally well for differential isoform usage. The default and recommend in IsoformSwitchAnalyzeR. See Testing for Isoform Switches via DEXSeq.
• DRIMSeq : Using the DRIMSeq package. See Testing for Isoform Switches via DRIMSeq.
• Cufflinks/Cuffdiff : the splicing analysis performed by Cuffdiff is automatically imported when importing Cufflinks/Cuffdiff data. See Importing Data from Cufflinks/Cuffdiff.

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

No matter which method is used, all the downstream functionality in IsoformSwitchAnalyzeR (e.g. not only the test) uses two parameters to define 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 does not reflect the effect size which 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 rely 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.

# Load example data and pre-filter
data("exampleSwitchList")
exampleSwitchList <- preFilter(exampleSwitchList) # preFilter to remove lowly expressed features
#> The fitering removed 52 ( 20.08% of ) transcripts. There is now 207 isoforms left

# Perform test
exampleSwitchListAnalyzed <- isoformSwitchTestDEXSeq(
    switchAnalyzeRlist = exampleSwitchList,
    reduceToSwitchingGenes=TRUE
)
#> Step 1 of 3: Calculating isoform abundances (from counts)...
#> Step 2 of 3: Calculating Isoform Fraction matrix...
#> Step 3 of 3: Testing each pariwise comparisons with DEXSeq (this might be a bit slow)...
#>     Estimated time (for dataset with ~30.000 isoforms): 2 min
#> Step 4 of 3: Integrating result into switchAnalyzeRlist...
#>     Isoform switch analysis was performed for 62 gene comparisons (100%).
#> Done

# Summarize switching features
extractSwitchSummary(exampleSwitchListAnalyzed)
#>            Comparison nrIsoforms nrGenes
#> 1 hESC vs Fibroblasts         91      44

# Load example data and pre-filter
data("exampleSwitchList")
exampleSwitchList <- preFilter(exampleSwitchList, geneExpressionCutoff = 4) # preFilter for fast runtime
#> The fitering removed 69 ( 26.64% of ) transcripts. There is now 190 isoforms left

# Perform test (takes ~10 sec)
exampleSwitchListAnalyzed <- isoformSwitchTestDRIMSeq(
    switchAnalyzeRlist = exampleSwitchList,
    testIntegration='isoform_only',
    reduceToSwitchingGenes=TRUE
)
#> Please consider using isoformSwitchTestDEXSeq() instead. 
#> The DEXSeq implementation have been shown to superiour to DRIMSeq 
#> on all paramters (except runtime for large number of samples) 
#> by Love et al 2018 (F1000) - results we can replicate.
#> Step 1 of 6: Creating DM data object...
#> Step 2 of 6: Filtering DM data object...
#> Step 3 of 6: Estimating precision paramter (this may take a while)...
#> Step 4 of 6: Fitting linear models (this may take a while)...
#> Step 5 of 6: Testing pairwise comparison(s)...
#> Step 6 of 6: Preparing output...
#> Result added switchAnalyzeRlist
#> An isoform switch analysis was performed for 56 gene comparisons (98.2%).
#> Done

# Summarize switching features
extractSwitchSummary(exampleSwitchListAnalyzed)
#>            Comparison nrIsoforms nrGenes
#> 1 hESC vs Fibroblasts         80      39

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 (i.e. you did not perform a (guided) de novo isoform reconstruction), we advide that you use the annotated CDS (Coding Sequence) from the annotation — which can be imported and integrated though one of the implemented import 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 for benchmark). Such prediction can be done with:

analyzeORF()

This function utilizes 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 has the highest accuracy in benchmarks 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 translation start sites are supplied to the cds argument.
4. The ‘mostUpstreamAnnoated’ method. This method identifies ORF downstream of the most upstream overlapping annotated translation start site. It requires known translation 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) pathway. 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 parameter, see ?analyzeCPAT) as well as Pfam results (see Augmenting ORF Predictions with Pfam Results)

The analyzeORF() function can be used as follows:

### 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 available from Bioconductor as BSgenome orbjects: 
# http://bioconductor.org/packages/release/BiocViews.html#___BSgenome
# Here we use Hg19 --- which can be downloaded by copy/pasting the following two lines into the R termminal:
# BiocManager::install("BSgenome.Hsapiens.UCSC.hg19")

library(BSgenome.Hsapiens.UCSC.hg19)

exampleSwitchListAnalyzed <- analyzeORF(exampleSwitchListAnalyzed, genomeObject = Hsapiens, showProgress=FALSE)
#> Step 1 of 3 : Extracting transcript sequences...
#> Step 2 of 3 : Locating ORFs of interest...
#> Step 3 of 3 : Scanning for PTCs...
#> 140 putative ORFs were identified and analyzed
#> Done

head(exampleSwitchListAnalyzed$orfAnalysis, 3)
#>       isoform_id orfTransciptStart orfTransciptEnd orfTransciptLength
#> 1 TCONS_00000007               248            1414               1167
#> 2 TCONS_00000008               225            1391               1167
#> 3 TCONS_00000009               225            1391               1167
#>   orfStarExon orfEndExon orfStartGenomic orfEndGenomic
#> 1           2          3          324343        325602
#> 2           2          3          324343        325602
#> 3           2          3          324343        325602
#>   stopDistanceToLastJunction stopIndex   PTC
#> 1                      -1164         0 FALSE
#> 2                      -1164         0 FALSE
#> 3                       1336        -1  TRUE

As seen above, genomic and transcript coordinates for ORF beginning and end, as well as PTC status, are 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 implemented in:

getCDS()

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 performing both internal and external sequence analysis which enables us to annotate the isoforms involved in isoform switches even 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 file 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).

### 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
)
#> Step 1 of 3 : Extracting transcript nucleotide sequences...
#> Step 2 of 3 : Extracting ORF AA sequences...
#> Step 3 of 3 : Preparing output...
#> Done

head(exampleSwitchListAnalyzed$ntSequence,2)
#>   A DNAStringSet instance of length 2
#>     width seq                                          names               
#> [1]  2750 GGGTCTCCCTCTGTTGTCCAA...CCCCTCCCACGCGGACAGAG TCONS_00000007
#> [2]  4369 TTACTGTTGATTGTGACGAAA...AATGAAAAATATCGCCCACG TCONS_00000008

head(exampleSwitchListAnalyzed$aaSequence,2)
#>   A AAStringSet instance of length 2
#>     width seq                                          names               
#> [1]   389 MLLPPGSLSRPRTFSSQPLQT...CQQPQQAQLLPHSGPFRPNS TCONS_00000007
#> [2]   389 MLLPPGSLSRPRTFSSQPLQT...CQQPQQAQLLPHSGPFRPNS TCONS_00000008

Back to Table of Content

Advice for Running External Sequence Analysis Tools

The two fasta files that are the output 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.
• Pfam : Prediction of protein domains, which can be run either locally (using the pfam_scan.pl script as described in the readme found here or via their webserver.
• SignalP : Prediction of Signal Peptides — a short N-terminal sequence of a peptide indicating where a protein should be membrane bound or secreted. SignalP can be run either locally or via their webserver.

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

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

• CPAT : Use default parameters and the nucleotide fasta file (_nt.fasta). If the webserver was used, download the tab-delimited result file (available at 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 is used you need to copy/paste the result part of the mail you receive into an empty plain text document (notepad, sublimetext, TextEdit or similar (not Word)) and save that to a plain text (txt) file. The path to that file should be supplied. 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 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 webserver 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 an empty plain text document (notepad, 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

Importing External Sequence 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 incooperated in the switchAnalyzeRlist via (respectively)

analyzeCPAT()
analyzePFAM()
analyzeSignalP()

The functions are used as:

### Load test data (maching the external sequence analysis results)
data("exampleSwitchListIntermediary")
exampleSwitchListIntermediary
#> This switchAnalyzeRlist list contains:
#>  162 isoforms from 40 genes
#>  1 comparison from 2 conditions
#> 
#> Switching features:
#>            Comparison switchingIsoforms switchingGenes
#> 1 hESC vs Fibroblasts                83             40
#> 
#> Feature analyzed:
#> [1] "Isoform Swich Identification, ORFs, ntSequence, aaSequence"

### Add PFAM analysis
exampleSwitchListAnalyzed <- analyzePFAM(
    switchAnalyzeRlist   = exampleSwitchListIntermediary,
    pathToPFAMresultFile = system.file("extdata/pfam_results.txt", package = "IsoformSwitchAnalyzeR"),
    showProgress=FALSE
    )
#> Converting AA coordinats to transcript and genomic coordinats...
#> Added domain information to 140 (86.42%) transcripts

### Add SignalP analysis
exampleSwitchListAnalyzed <- analyzeSignalP(
    switchAnalyzeRlist       = exampleSwitchListAnalyzed,
    pathToSignalPresultFile = system.file("extdata/signalP_results.txt", package = "IsoformSwitchAnalyzeR")
    )
#> Added signal peptide information to 17 (10.49%) transcripts
    
### 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
    )
#> Added coding potential to 162 (100%) transcripts

exampleSwitchListAnalyzed
#> This switchAnalyzeRlist list contains:
#>  162 isoforms from 40 genes
#>  1 comparison from 2 conditions
#> 
#> Switching features:
#>            Comparison switchingIsoforms switchingGenes
#> 1 hESC vs Fibroblasts                83             40
#> 
#> Feature analyzed:
#> [1] "Isoform Swich Identification, ORFs, ntSequence, aaSequence, Protein Domains, Signal Peptides, Coding Potential"

Here, the pathTo<tool_name>resultFile points to the result files constructed as suggested in Advice for Running External Sequence Analysis Tools and in the detailed documentation of each of the individual functions. The part of the examples using system.file() is not nesseary when using your own data - just supply the path as a string (e.g. “/myFiles/myCPATresults.txt”)

Of particular interest is the removeNoncodinORFs argument in analyzeCPAT() since it allows integration of the CPAT and ORF analyses by removing ORFs from isoforms not predicted to be coding by CPAT. This can be particularly 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 effect all downstream analyses and plots as both analysis of protein domains and signal peptides require that ORFs are annotated (e.g. analyzeSwitchConsequences will not consider the domains (potentially) found by Pfam if the ORFs have been removed).

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Predicting Alternative Splicing

Another type of annotation we easily can obtain, since we know the exon structure of all isoforms in a given gene (with isoform switching), is alternative splicing. Here intron retention events are of particular interest as a consequnce in isoform switches since they represent the largest changes in isoforms. Identification of alternative splicing, alternative transcription start and termination sites can be obtained via the analyzeAlternativeSplicing().

### This example relies on the example data from the 'Importing External Sequence Analysis' section above 

exampleSwitchListAnalyzed <- analyzeAlternativeSplicing(exampleSwitchListAnalyzed, quiet=TRUE)

### overview of number of intron retentions (IR)
table(exampleSwitchListAnalyzed$isoformFeatures$IR)
#> 
#>   0   1   2 
#> 135  25   2

Meaning 25 isoforms contain one intron retention (IR) which two isoforms each contain two intron retentions.

IsoformSwitchAnalyzeR also supports many types of genone wide analysis of alternative splicing - for a more thorough walkthrough of the analysis of alternative splicing see the Analyzing Alternative Splicing workflow.

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Predicting Switch Consequences

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

Specifically, IsoformSwitchAnalyzeR contains a function analyzeSwitchConsequences() which extracts the isoforms with significant changes in their isoform usage (defined by the alpha and dIFcutoff parameters, see Identifying Isoform Switches for details) and the isoform, with a large 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 parameter); but, isoforms are then selected purely based on their changes in dIF values.

These isoforms are then divided into the isoforms that increase 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 “Details” on the documentation page for analyzeSwitchConsequences() (by running ?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:

### This example relies on the example data from the 'Predicting Alternative Splicing' 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
)
#> Step 1 of 4: Extracting genes with isoform switches...
#> Step 2 of 4: Analyzing 5 pairwise isoforms comparisons...
#> Step 3 of 4: Massaging isoforms comparisons results...
#> Step 4 of 4: Preparing output...
#> Identified  genes with containing isoforms switching with functional consequences...

extractSwitchSummary(exampleSwitchListAnalyzed, dIFcutoff = 0.4, filterForConsequences = FALSE)
#>            Comparison nrIsoforms nrGenes
#> 1 hESC vs Fibroblasts         12       7
extractSwitchSummary(exampleSwitchListAnalyzed, dIFcutoff = 0.4, filterForConsequences = TRUE)
#>            Comparison nrIsoforms nrGenes
#> 1 hESC vs Fibroblasts         10       5

Meaning that ~5/7 genes with switches (with dIF > 0.4) have isoform switches with functional consequences. For larger dataset with default parameters this is typicall 1/3.

Please note that the analyzeSwitchConsequences() function contains many parameters and cutoffs for deciding when a difference in the annotation is sufficiently different (e.g. supplying ‘ntCutoff=50’ ensures that differences in nucleotide lengths of two ORF are larger than 50 nucleotides — not just 3 nucleotides (one codon)).

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Removal of annotation data not needed anymore

Now that we have predicted isoform switches and their functional consequnces the switchAnalyzeRlist contains a lot of information no longer nessesary thereby significantly reducing the memmory usage. To remove the excess annotation - specifically replicate quantification and the biological sequnces - the removeAnnoationData() function have been implemented.

Please note that this function:

• is mainly intended to be used when analyzing a large number of samples
• t is highly recomended to save a version of the switchAnalyzeRlist with all the annotation before removeing it - typically done with base::saveRDS or base::save.

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Post Analysis of Isoform Switches with Consequences

Now that we have a genome-wide characterization of isoform switches with potential consequences there are a lot of possibilities for analyzing these. IsoformSwitchAnalyzeR currently directly supports two different types of post-analysis (of switches - see Genome-Wide Analysis of Alternative Splicing below for analysis of alternative splicing):

1. Analysis of Individual Isoform Switching which aims to identify the top genes and provide a visual overview summarizing all the relevant information about an isoform switch single gene.
2. Genome-Wide Analysis of Isoform Switching which summarizes and analyzes larger patterns of isoform switches with predicted functional consequences

### Load already analyzed data
data("exampleSwitchListAnalyzed")

### Let's reduce the switchAnalyzeRlist to only one condition
exampleSwitchListAnalyzedSubset <- subsetSwitchAnalyzeRlist(
    exampleSwitchListAnalyzed, 
    exampleSwitchListAnalyzed$isoformFeatures$condition_1 == 'COAD_ctrl'
)
exampleSwitchListAnalyzedSubset
#> This switchAnalyzeRlist list contains:
#>  1000 isoforms from 369 genes
#>  1 comparison from 2 conditions
#> 
#> Switching features:
#>                 Comparison switchingIsoforms switchingGenes
#> 1 COAD_ctrl vs COAD_cancer               692            369
#> 
#> Feature analyzed:
#> [1] "Isoform Swich Identification, ORFs, Protein Domains, Signal Peptides, Alternative splicing, Switch Consequences, Coding Potential"

### Extract top switching genes (by q-value)
extractTopSwitches(
    exampleSwitchListAnalyzedSubset, 
    filterForConsequences = TRUE, 
    n = 2, 
    sortByQvals = TRUE
)
#>                               gene_ref                     gene_id
#> 1 chr1:244998639..245008357,+COAD_ctrl chr1:244998639..245008357,+
#> 2      chr12:569543..671058,+COAD_ctrl      chr12:569543..671058,+
#>   gene_name condition_1 condition_2 gene_switch_q_value
#> 1    FAM36A   COAD_ctrl COAD_cancer        5.344466e-06
#> 2  B4GALNT3   COAD_ctrl COAD_cancer        5.344466e-06
#>   switchConsequencesGene Rank
#> 1                   TRUE    1
#> 2                   TRUE    2

### Extract top 2 switching genes (by dIF values)
extractTopSwitches(
    exampleSwitchListAnalyzedSubset, 
    filterForConsequences = TRUE, 
    n = 2, 
    sortByQvals = FALSE
)
#>                      gene_id gene_name condition_1 condition_2
#> 1 chr10:70980059..71027314,+     HKDC1   COAD_ctrl COAD_cancer
#> 2  chr3:13573824..13679921,+     FBLN2   COAD_ctrl COAD_cancer
#>   gene_switch_q_value combinedDIF switchConsequencesGene Rank
#> 1        7.463277e-06       1.216                   TRUE    1
#> 2        5.344466e-06       1.167                   TRUE    2

### 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 == 'ZAK')
#>                 iso_ref                             gene_ref isoform_id
#> 9   uc002uhz.2COAD_ctrl chr2:173940565..174132736,+COAD_ctrl uc002uhz.2
#> 237 uc002uhy.2COAD_ctrl chr2:173940565..174132736,+COAD_ctrl uc002uhy.2
#>                         gene_id gene_name condition_1 condition_2   IF1
#> 9   chr2:173940565..174132736,+       ZAK   COAD_ctrl COAD_cancer 0.176
#> 237 chr2:173940565..174132736,+       ZAK   COAD_ctrl COAD_cancer 0.181
#>       IF2    dIF isoform_switch_q_value switchConsequencesGene
#> 9   0.408  0.232           5.344466e-06                   TRUE
#> 237 0.024 -0.157           5.387221e-04                   TRUE
#>                   comparison Rank
#> 9   COAD_ctrl_vs_COAD_cancer    9
#> 237 COAD_ctrl_vs_COAD_cancer  237

switchPlot(exampleSwitchListAnalyzedSubset, gene = 'ZAK')

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

Other Tools in IsoformSwitchAnalyzeR

Apart from the analysis presented above, IsoformSwitchAnalyzeR also contains a few other useful tools which will be presented in this section.

The central visualization is the switch plot, created with switchPlot() function as shown above. This plot is made from four subplots, which each can be created individualy using:

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

As illustrated here:

### Load already analyzed data
data("exampleSwitchListAnalyzed")

switchPlotTranscript(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B')

switchPlotGeneExp (exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B', condition1='iPS', condition2='hESC')

switchPlotIsoExp(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B', condition1='iPS', condition2='hESC')

switchPlotIsoUsage(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B', condition1='iPS', condition2='hESC')

Another useful 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 useful 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 incorporation of the additional features, such as inter-library normalization, implemented in importIsoformExpression().

If the end goal is not to calculate gene expression (from the isoform expression) but instead to calculate isoform fractions we have implemented the isoformToIsoformFraction() function.

The last tool currently built into IsoformSwitchAnalyzeR is the extractExpressionMatrix() function. The expression information stored in the switchAnalyzeRlist’s isoformFeatures is ideal for comparing multiple annotations 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:

data("exampleSwitchListIntermediary")
ifMatrix <- extractExpressionMatrix(exampleSwitchListAnalyzed)

head(ifMatrix)
#>            COAD_ctrl LUAD_ctrl COAD_cancer LUAD_cancer
#> uc001aky.2     0.533     0.579       0.414       0.431
#> uc001ciu.2     0.546     0.535       0.354       0.393
#> uc001czi.3     0.665     0.214       0.385       0.418
#> uc001czm.3     0.199     0.697       0.464       0.408
#> uc001dck.2     0.659     0.874       0.380       0.538
#> uc001ebg.3     0.271     0.333       0.393       0.448

dim(ifMatrix)
#> [1] 139   4

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

# correlation plot
ggplot(reshape2::melt(cor(ifMatrix)), aes(x=Var1, y=Var2, fill=value)) + 
    geom_tile() +
    scale_fill_continuous('Correlation') +
    labs(x='Condition', y='Condition') +
    theme_minimal() +
    theme(axis.text.x=element_text(angle=-90, hjust = 0, vjust=0.5)) # turn x-axis 90 degrees

Or expression (via a heatmap):

library(pheatmap)
pheatmap(t(ifMatrix), show_colnames=FALSE)

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Overview of Alternative Splicing Workflow

Analysis of alternative splicing, here made possible by a liftover of functionalities from the now deprecated R package spliceR, is an important area of research. IsoformSwitchAnalyzeR is well suited for performing alternative splicing analysis since alterntive splicing litteraly will result in isoform switches. Please note that alternative splicing here is used as a term which covers both Alternative Splicing (AS), Alternative Transcription Start Sites (ATSS, sometimes called alternative first exon) as well as Alternative Transcription Start Sites (ATTS, sometimes called alternative last exon): although this is techncaiily incorrect it is here used for convenience.

The workflow implemented in IsoformSwitchAnalyzeR is mostly focused on identifying global changes in alternative splicing but also allows for analysis of individual isoforms/switches. A typical workflow consists of five steps. Please note that step 1-4 are included in the standard switch analysis workflow, meaning if you have already analyzed isoform switches with consequences you can skip 1-4. The five steps corresponds to using the following functions in succession:

### Step (1) Import data and create SwitchAnalyzeRlist
mySwitchList <- importCufflinksData()      # OR
mySwitchList <- importIsoformExpression()  # OR
mySwitchList <- importRdata()

### Step (2) Prefilter
mySwitchList <- preFilter( mySwitchList )

### Step (3) Identify differentially used isoforms
mySwitchList <- isoformSwitchTestDEXSeq(  mySwitchList, reduceToSwitchingGenes=FALSE ) # OR
mySwitchList <- isoformSwitchTestDRIMSeq( mySwitchList, reduceToSwitchingGenes=FALSE ) 

### Step (4) Analyze alternative splicing
mySwitchList <- analyzeAlternativeSplicing( mySwitchList )

### Step (5) Global splicing analysis
extractSplicingSummary( mySwitchList )
extractSplicingEnrichment( mySwitchList )
extractSplicingEnrichmentComparison( mySwitchList )
extractSplicingGenomeWide( mySwitchList )

Step 1-4 are shared with the standard switch analysis workflow which is describe above in the Detailed Workflow (specifically each step is described in Importing Data Into R, Filtering, Identifying Isoform Switches and Predicting Alternative Splicing) and will therefore not be repeated here. The only difference is that in some cases one might not want reduce the switchAnalyzeRList to only contain the genes which contain differentially used isoforms — as discussed below. This is done by setting reduceToSwitchingGenes=FALSE in the switch test.

Genome-Wide Analysis of Alternative Splicing

The genome-wide analysis of splicing patterns is facilitated by the following four functions:

• extractSplicingSummary()
• extractSplicingEnrichment()
• extractSplicingEnrichmentComparison()
• extractSplicingGenomeWide()

The extractSplicingSummary function does exactly what it says on the tin - it summarizes the number of events found in each comparison. To showcase it, let’s look at some already analyzed example data (meaning step 1-4 have already been done).

data("exampleSwitchListAnalyzed")
exampleSwitchListAnalyzed
#> This switchAnalyzeRlist list contains:
#>  1469 isoforms from 530 genes
#>  2 comparison from 4 conditions
#> 
#> Switching features:
#>                 Comparison switchingIsoforms switchingGenes
#> 1 COAD_ctrl vs COAD_cancer               692            369
#> 2 LUAD_ctrl vs LUAD_cancer               422            218
#> 3                 combined              1010            530
#> 
#> Feature analyzed:
#> [1] "Isoform Swich Identification, ORFs, Protein Domains, Signal Peptides, Alternative splicing, Switch Consequences, Coding Potential"

Note that via the in “Feature analyzed” part of the summary we can see that the analysis of alternative splicing have been done and added to the switchAnalyzeRlist.

extractSplicingSummary(
    exampleSwitchListAnalyzed,
    asFractionTotal = FALSE,
    plotGenes=FALSE
)

Note that it is possible to both summarize per gene or per isoform via the plotGenes argument and that the asFractionTotal argument enables analysis the fraction (if TRUE) or Number (if FALSE) of significant isoforms/genes which have a particular splice type.

From the summary above, it is quite clear that some of the alternative splicing events are not equally used - most prominently illustrated by the use of ATSS in COAD, where there is (a lot) more gain than losses. To formally analyze this type of uneven alternative splicing usage we have implemented extractSplicingEnrichment(). This function summarizes the uneven usage within each comparison by for each alternative splicing type calculate the fraction of events being gains (as opposed to loss) and perform a statistical analysis of this fraction.

splicingEnrichment <- extractSplicingEnrichment(
    exampleSwitchListAnalyzed,
    splicingToAnalyze='all',
    returnResult=TRUE,
    returnSummary=TRUE
)

From this plot it is clear to see that some alternative splicing types are preferentially used in the two cancer types, and from the statistical summary also created by extractSplicingEnrichment() (due to the returnSummary=TRUE argument):

subset(splicingEnrichment, splicingEnrichment$AStype == 'ATSS')
#>    condition_1 condition_2 AStype nUp nDown    propUp propUpCiLo
#> 3    COAD_ctrl COAD_cancer   ATSS 234    39 0.8571429  0.8086495
#> 11   LUAD_ctrl LUAD_cancer   ATSS  85    54 0.6115108  0.5248958
#>    propUpCiHi   propUpPval   propUpQval Significant
#> 3   0.8952936 7.816835e-32 1.172525e-30        TRUE
#> 11  0.6918656 1.094134e-02 2.735335e-02        TRUE
#>                    Comparison
#> 3  COAD_ctrl\nvs\nCOAD_cancer
#> 11 LUAD_ctrl\nvs\nLUAD_cancer

We can indeed see that ATSS has a very significant skew in COAD. Note that the “splicingToAnalyze” argument implemented in all these summary functions enables analysis of only a subset of the splice types.

Another observation that can be made from the enrichment analysis above is that although the overall trend in usage of alternative splicing is the same the two cancer types there seem to be some differences. These differences can be analyzed using extractSplicingEnrichmentComparison() function which for each splice type, in a pairwise manner contrast the individual condition comparison, to assess whether the ratio of gains are indeed different acorss comparisons.

extractSplicingEnrichmentComparison(
    exampleSwitchListAnalyzed,
    splicingToAnalyze=c('A3','MES','ATSS'), # Those significant in COAD in the previous analysis
    returnResult=FALSE # Preventing the summary statistics to be returned as a data.frame
)

And indeed only ATSS usage is significantly different between the two cancer types.

All of the above splicing analysis relies on the identification of pairs of isoforms involved in a switch, but the analysis of alternative splicing can also be done from the perspective of the individual splice types. This can be done by asking the question: How does the isoform usage of all isoforms utilizing a particular splicing type change - in other words is all isoforms or only only a subset of isoforms that are affected. This can be analysed with the extractSplicingGenomeWide() function.

extractSplicingGenomeWide(
    exampleSwitchListAnalyzed,
    featureToExtract = 'all',                 # all isoforms stored in the switchAnalyzeRlist
    splicingToAnalyze = c('A3','MES','ATSS'), # Splice types significantly enriched in COAD
    plot=TRUE,
    returnResult=FALSE  # Preventing the summary statistics to be returned as a data.frame
)

From which it can be seen that all isoforms using ATSS is generally used more, meaning it is a global phenomenon, whereas none of the other splice types are general (i.e. they seem to only happen in a specific subset of the data).

Please note that:

• The the dots in the violin plots above indicate 25th, 50th (median) and 75th percentiles (just like the box in a boxplot would).
• The analysis provided by extractSplicingGenomeWide() should only be expected to give a result when splicing is (severely) affected.
• When using the featureToExtract=‘all’ one should not use the reduceToSwitchingGenes=TRUE during the testing of isoform switches (as then it is not all isoforms but only those in genes containing at least one differentially used isoform) - then it will not be ‘all’ isoforms that can be analyzed.

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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 removes 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 approach to setting “removeNoncodinORFs=TRUE” and should be used instead of that option.

Note that this is only recommended if ORFs were predicted (i.e. 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))

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Analyze Small Upstream ORFs

Recent research suggests that small upstream ORFs are far more frequent that previously assumed. It is therefore of particular interest 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)
        ]
)

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Remove Sequences Stored in SwitchAnalyzeRlist

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

summary(exampleSwitchListIntermediary)

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

summary(exampleSwitchListIntermediary)

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Adding Uncertain Category to Coding Potential Predictions

There has been considerable debate about whether the default parameters for the codingPotential calculated for CPAT are too lenient (too low). This will always be a problem when having a single cutoff. One possible solution is to introduce an “unknown” class with medium size coding potential which we can then disregard.

Let’s 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)

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Quality control of ORF of known annotation

As we have shown there are many problems with known CDS annotations (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 that contain un-annotated stop codons, defined as * when the ORF nucleotide sequences are translated to the amino acid sequence. If enabled, the ORFs will both be removed from the switchAnalyzeRList and from the sequences outputted by extractSequence().

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 an isoform switch.

This is simply done by, in addition to running analyzeSwitchConsequences with the consequences you find interesting, making 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 give rise to the isoform switches with consequence you are interested in. One suggestion for such an analysis is illustrated here:

### Load example data
data("exampleSwitchListAnalyzed")

### Reduce datasize for fast runtime
selectedGenes <- unique(exampleSwitchListAnalyzed$isoformFeatures$gene_id)[50:100]
exampleSwitchListAnalyzedSubset <- subsetSwitchAnalyzeRlist(
    exampleSwitchListAnalyzed,
    exampleSwitchListAnalyzed$isoformFeatures$gene_id %in% selectedGenes
)

### analyze the biological mechanisms
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 already 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:

### 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)
#> Loading required package: grid
#> Loading required package: futile.logger
myVenn <- venn.diagram(mechList, col='transparent', alpha=0.4, fill=RColorBrewer::brewer.pal(n=3,name='Dark2'), filename=NULL)

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

From this, we can see the relative importance of each of the three mechanisms, as well as the combination of these.

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Analysing experiments without replicates

Generally we do not recommend analysis of experiments without replicates as it is impossible to assess technical and biological variation. On the other hand, single-replicate pilot studies can be useful for planning larger experiments: hence this small guide. For running IsoformSwitchAnalyzeR without replicates, we have to solely rely on the dIF values to call isoform switches. This can be done by following the steps outlined in Detailed Workflow with a few modifications:

• Omit the isoform switch test step
• Manuallyt set the switch q-value to 1, which can be done as follows:

aSwitchList$isoformFeatures$gene_switch_q_value <- 1

• In all subsequent steps of the Detailed Workflow, make sure IsoformSwitchAnalyzeR ignores the q-value by setting the alpha argument to something larger than 1 (e.g. 1.1). You will have to ignore the resulting warnings IsoformSwitchAnalyzeR produces as it test the validity of the arguments.

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FAQ Table of Content

• What Quantification Tool(s) Should I Use?
• How to handle cofounding effects (including batches)
• What constitute an independent biological replicate?
• Adding differential gene expression
• The error “The annotation does not fit the expression data”
• The error “The annotation (count matrix and isoform annotation) contain differences in which isoforms are analyzed…”
• The error “The supplied design matrix will result in a model matrix that is not full rank”

What Quantification Tool(s) Should I Use?

Please note that the following is a reflection of my personal convictions and should not be read as “the truth”.

Generally speaking there are two different ways to obtain transcript quantification which boils down to whether you trust the available transcript annotation or not (or think you RNA-seq data contains important additional isoforms not included in the available annotations).

If you generally trust the transcript annotation, state-of-the-art quantifications can be obtained directly from the sequence files (FASTQ) via Salmon or Kallisto. These methods give much more accurate quantifications (and are a lot faster) than for example first mapping to the genome and afterwards quantifying with Cufflinks. You can find more information in the Kallisto paper. For comparison you can find the Salmon paper here but please not the comparison in figure 1 is a special case. Under normal circumstances Kallisto and Salmon performs quite similarly and and choice is up to personal preference. IsoformSwitchAnalyzeR directly supports the quantifications of both tools (and more) via the importIsoformExpression() function - see Importing Data Into R. Kallisto can be downloaded from here and the manual for running Kallisto can be found here. Salmon can be downloaded from here and a manual for running Salmon can be found here.

If you on the other hand do not have any annotation, or want to augment it, you need to do a transcript deconvolution. A transcript deconvolution can be described as a 4-step process: - First you map reads to the genome (with tools such as STAR, HISAT2 etc.). - Then you assemble transcripts (typically guided) with tools such as Cufflinks or StringTie. Please note that many other tools for solving this problem exists and new methods are still published regularly. Since the transcript assembly is the central step in the analysis, you probably want to read up on these tools yourself to make sure you use the better tools. - The third step is to merge the assembled transcripts from all individual Cufflinks/StringTie/other runs into one combined reference transcripome with tools such as Cuffmerge, StringTie or TACO. - In step 4 this combined transcriptome can be used to quantify all the original FASTQ files with Cufflinks/CuffDiff, StringTie, Salmon or Kallisto.

As an alternative, tools such as Bayesembler performs step 2-4 jointly.

You will need to figure out which combination of tools you choose on your own - but IsoformSwitchAnalyzeR can analyze the data from all of them. For more information see Importing Data Into R.

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How to handle cofounding effects (including batches)

“Cofounding effect” is a common description of all sources of variation not of interest for the the analysis you are doing. This can be anything from a batch effects (data/sample produced/analyzed at different locations, timepoints, personel etc) to an effect you are not interested in but which migth influence the result (e.g. sex or age). Taking all this into account to avoide false positive of false negative results can easily be done when testing for isoform switches (or anything else really) when using tools based on linear models.

Naturally taking cofounding effects into account is also supported in IsoformSwitchAnalyzeR via the isoformSwitchTestDRIMSeq() and isoformSwitchTestDEXSeq() functions. Both these functions automatically takes additional counfounding effects into account if they have been annotated in the switchAnalyzeRlist. isoformSwitchTestDEXSeq() furthermroe also supports extract of batch corrected effect sizes (IF and dIFs).

Counfounding effects should be annotated in ‘designMatrix’ used when creating the switchAnalyzeRList (via createSwitchAnalyzeRlist() or importRdata()). This is simply done by adding extra columns to the design matrix before creating the switchAnalyzeRlist:

So if the design matrix looked like:

myDesign <- data.frame(
    sampleID=1:6,
    condition=c('wt','wt','wt','ko','ko','ko') # WT = wildtype, KO = Knock out
)

myDesign
#>   sampleID condition
#> 1        1        wt
#> 2        2        wt
#> 3        3        wt
#> 4        4        ko
#> 5        5        ko
#> 6        6        ko

Adding a co-founder is simply done as follows:

myDesign$batch <- factor(c('a','a','b','a','b','b'))
myDesign
#>   sampleID condition batch
#> 1        1        wt     a
#> 2        2        wt     a
#> 3        3        wt     b
#> 4        4        ko     a
#> 5        5        ko     b
#> 6        6        ko     b

Please note that cofounders can both be categorical (e.g. sex) (indicated by type character or factor) and continous variables (e.g. age) (indicated by numeric or integers) - both will be taken into account. Due to the differenti interpertation it is recomended to use strings/factors for categorical variables (aka not use 1 and 2 to indicate batch in the example above (instead of “a” and “b”)). The the conditions analyzed (as indicated by the “condition” column in the design matrix) must however always be categorical (character or factor).

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What constitute an independent biological replicate?

A very nice discussion, which I highly reccomend, can be found in Vaux et al.

As a TL;DR here are a couple of guidelines:

• Science can under NO circumstances be made from sample sizes of 1 (n=1). To call anything science proof that a result is reproducible is needed.
• If all replicates were made at the same time no extrapolations can be made! Running an experiment in parallel does not result in independent samples - it is still only n=1. The experiment will need to be done multiple times.
• If all experiments were all done on the same cell line, extrapolation to anything other than that cell line cannot be made. The same actually goes for mouse strains.
• If a knock-down is performed multiple different siRNA/shRNAs are needed (both to be independent and due to off target effects).
• If a knock-out is created is not enough to only investigate a single cell line originating from single clone. Multiple independent clones are needed.
• If you want to investigate a disease, analysing a single mouse or human is not enough - you would need data from multiple patients/mice to extrapolate to other humans/mice.

All these replicates which does not allow for extrapolation would be termed independent replicates - and if you ask me the work replicate should never be allowed to be used without specifying whether it is independent or not.

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Adding differential gene expression

Gene differential expression is currently not build into IsoformSwitchAnalyzeR since there are so many other good tools to do this. Some frequently used tools are edgeR, DESeq2, limma and Sleuth (for Kallisto users). This list is not exhaustive but clearly shows the variety of well maintained tools out there. For a recent review of these tool we refere you to Love et al 2018. No matter which tool you choose we support integration of the result by showing the result in the plot created by the switchPlot() function.

To add the result of a differential expression analysis all you need to do is overwrite the following columns:

exampleSwitchList$isoformFeatures$gene_q_value
exampleSwitchList$isoformFeatures$iso_q_value

With the corresponding FDR corrected P-values of a differential expression analysis. We reccomend the base function match() to do this - and do remember to consider conditions (indicated by the “condition_1” and “condition_2” columns) if you have more than one comparison.

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The error “The annotation does not fit the expression data”

This problem can arrise from multiple sources but essentially means that we determined that not all isoforms quantified are found in the annotation or vice versa. Ensuring that it is the exact same isoforms that are pressent in the annotation and quantification is essential because it is the foundation for all downstream analysis with IsoformSwitchAnalyzeR - therefore we need to be quite strict with this.

Currently we have documented this problem from three different sources:

Source 1: The GTF and Fasta files were obtained from Ensemble - Where files unfortunatly don’t match. This most often occure because Ensembl’s file structure is a bit complicated (the fasta and GTF files available do not match default). This also mean that to get a combination suitable for IsoformSwitchAnalyzeR a particular combination of files have to be used. Specifically you need to download 3 files:

• The cDNA fasta file (called “Homo_sapiens.GRCh38.cdna.all.fa.gz” for human)
• The ncRNA fasta file (called “Homo_sapiens.GRCh38.ncrna.fa.gz” for human)
• The largest GTF which include haplotype information (called “Homo_sapiens.GRCh38.93.chr_patch_hapl_scaff.gtf.gz” for human)

The two fasta files (cdna + ncrna) then needs to be concatenated into one fasta file. This concatenated file (almost perfectly) match the large GTF file. This also mean that you will have to use the concatenated fasta file as a basis for building the Salmon/Kallisto/RSEM index.

Please note that since GENCODE genes is a parsing of Ensemble data their files might be easier to use. Simply use the “Comprehensive gene annotation” GTF file along with the “Transcript sequences” fasta file - these match.

Source 2: The GTF and FASTQ file used to quantify with Kallisto/Salmon/RSEM/StringTie contain non-overlapping isoforms which in some cases are due to version numbering being pressent or not. We have found this when using data downloaded from Ensembl. Three possible solutions are:

• Update to the lastest IsoformSwitchAnalyzeR version which better handles this.
• Use files from GENCODE genes instead. Simply use the “Comprehensive gene annotation” GTF file along with the “Transcript sequences” fasta file - these match.
• Use a tool to generate the fastq file from the GTF file of interest. This can be done with tool such as the gffread tool included in both Cufflinks/Cuffdiff and Stringtie

Source 3: When using RSEM for the quantificaitons some older versions of tximport could cause this problm. To fix this problem you need to update the underlying package tximport. This can be done by copy/pasting this command into R:

devtools::install_github("mikelove/tximport")

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The error “The annotation (count matrix and isoform annotation) contain differences in which isoforms are analyzed…”

This error occurs because there is differences in which isoforms are found in the annotation and which isoforms have been quantified. This problem have (so far) only been observed in data quantified with Salmon. It typically occres because during the index building Salmon collapses identical transcripts. A discussion with the Salmon authors of pros and cons of this behaviour can be found here.

We ask the user to make sure why this problem occurs in the user-specific setting, and that removing the non-quantified isoforms is the desired solution, before continuing.

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The error “The supplied design matrix will result in a model matrix that is not full rank”

If a desing matrix is not of full rank it simply means that one or more columns (or combinations of columns) in the design matrix describe the same thing. Imaging a case when you had two paramters describing the same person - say fingerprint and social security number. If you then try to estimate the effect of these factors you can have an infinite number of solutions by having them being opposites (-1 and 1, -2 and 2 etc) which means you cannot estimate a linear model. If you have problems with this the easies thing is to write a post about it on http://support.bioconductor.org.