IntEREst

Creating reference

The first step is to build a reference which will be used for the summarization of the sequence reads, and also downstream analysis (e.g. significant differential IR analysis). This can be carried out by referencePrepare() function. The resulted reference data frame includes coordinates of introns and exons of the genes; They can be extracted from various sources i.e. UCSC, biomaRt or a user defined file (e.g. GFF3/GTF). The exons with overlapping genomic coordinates can be collapsed (if collapseExons parameter is set as TRUE) to avoid assigning reads mapping to any alternatively skipped exons to their overlapping introns. An example of the process is shown in figure 2.

Figure 2: Running reference isoforms a, b, c and d leads to gene A with exons that are results of collapsing of exons of all isoforms.

Here we build a reference data frame from a manually built GFF3 file that includes exonic coordinates of the gene RHBDD3.

# Load library quietly
suppressMessages(library("IntEREst"))
# Selecting rows related to RHBDD3 gene
tmpGen<-u12[u12[,"gene_name"]=="RHBDD3",]
# Extracting exons
tmpEx<-tmpGen[tmpGen[,"int_ex"]=="exon",]

# Building GFF3 file
exonDat<- cbind(tmpEx[,3], ".",
    tmpEx[,c(7,4,5)], ".", tmpEx[,6], ".",paste("ID=exon",
    tmpEx[,11], "; Parent=ENST00000413811", sep="") )
trDat<- c(tmpEx[1,3], ".", "mRNA", as.numeric(min(tmpEx[,4])),
    as.numeric(max(tmpEx[,5])), ".", tmpEx[1,6], ".",
    "ID=ENST00000413811")
outDir<- file.path(tempdir(),"tmpFolder")
dir.create(outDir)
outDir<- normalizePath(outDir)
gff3File<-paste(outDir, "gffFile.gff", sep="/")
cat("##gff-version 3\n",file=gff3File, append=FALSE)
cat(paste(paste(trDat, collapse="\t"),"\n", sep=""),
    file=gff3File, append=TRUE)
write.table(exonDat, gff3File,
    row.names=FALSE, col.names=FALSE,
    sep='\t', quote=FALSE, append=TRUE)

# Extracting U12 introns info from 'u12' data
u12Int<-u12[u12$int_ex=="intron"&u12$int_type=="U12",]

# Building reference
#Since it is based on one gene only (that does not feature alternative splicing
#events) there is no difference if the collapseExons is set as TRUE or FALSE
testRef<- referencePrepare (sourceBuild="file",
    filePath=gff3File, u12IntronsChr=u12Int[,"chr"],
    u12IntronsBeg=u12Int[,"begin"],
    u12IntronsEnd=u12Int[,"end"], collapseExons=TRUE,
    fileFormat="gff3", annotateGeneIds=FALSE)

## Import genomic features from the file as a GRanges object ... OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK

head (testRef)

##     chr    begin      end strand int_ex int_ex_num collapsed_transcripts_id
## 1 chr22 29655841 29656226      -   exon          1                        1
## 2 chr22 29656227 29656314      - intron          2                        1
## 3 chr22 29656315 29656602      -   exon          3                        1
## 4 chr22 29656603 29656690      - intron          4                        1
## 5 chr22 29656691 29656853      -   exon          5                        1
## 6 chr22 29656854 29659823      - intron          6                        1
##   collapsed_transcripts int_type
## 1       ENST00000413811     <NA>
## 2       ENST00000413811       U2
## 3       ENST00000413811     <NA>
## 4       ENST00000413811       U2
## 5       ENST00000413811     <NA>
## 6       ENST00000413811       U2

Annotating U12 type introns

It is possible to annotate the U12 type introns in a reference using the annotateU12 function. U12-type introns (also known as minor type introns) are detected and spliced by the U12 splicing machinery as opposed to the majority of the introns (known as major type or U2 type) which are spliced by the U2 spliceosome. U12-type introns also feature evolutionary conserved splice sites which are distinguished from the splice sites of U2 type introns hence they can be detected by mapping a position weigh matrix (PWM) to their splice sites and measuring their match score based on the PWM. The following scripts re-annotates introns of the genes RHBDD2 and YBX2.

# Improting genome
BSgenome.Hsapiens.UCSC.hg19 <-
BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19
#Index of the subset of rows
ind<- u12$gene_name %in% c("RHBDD2", "YBX2")

# Annotate U12 introns with strong U12 donor site, branch point
# and acceptor site from the u12 data in the package
annoU12<-
    annotateU12(pwmU12U2=list(pwmU12db[[1]][,11:17],pwmU12db[[2]],
        pwmU12db[[3]][,38:40],pwmU12db[[4]][,11:17],
        pwmU12db[[5]][,38:40]),
    pwmSsIndex=list(indexDonU12=1, indexBpU12=1, indexAccU12=3,
        indexDonU2=1, indexAccU2=3),
    referenceChr=u12[ind,'chr'],
    referenceBegin=u12[ind,'begin'],
    referenceEnd=u12[ind,'end'],
    referenceIntronExon=u12[ind,"int_ex"],
    intronExon="intron",
    matchWindowRelativeUpstreamPos=c(NA,-29,NA,NA,NA),
    matchWindowRelativeDownstreamPos=c(NA,-9,NA,NA,NA),
    minMatchScore=c(rep(paste(80,"%",sep=""),2), "40%",
    paste(80,"%",sep=""), "40%"),
    refGenome=BSgenome.Hsapiens.UCSC.hg19,
    setNaAs="U2",
    annotateU12Subtype=TRUE)

## 
## U12  U2 
##   2  10

# How many U12 and U2 type introns with strong U12 donor sites,
# acceptor sites (and branch points for U12-type) are there?
    table(annoU12[,1])

## 
## U12  U2 
##   2  10

Intron retention, Inton spanning and exon-exon junction level estimation

The raw counts and normalized intron retention, intron spanning and exon-exon junction levels can be estimated using any of the two RNAseq read summarization functions, interest() and interest.sequential(). The interest() function is more robust since it distributes the reads in the .bam file over several computing cores and analyze the distributed data simultaneously. Note that regions in the genome with repetitive sequence elements may bias the mapping of the read sequences and the retention analysis. If you wish to exclude these regions from the analysis you can use the getRepeatTable() function, however We did not find repetitive DNA elements in particular biasing our results therefore we do not routinely use this function. As for instance, if you wish to exclude the coordinates in the genome housing Alu elements, you can run the reads summarization functions with the repeatsTableToFilter= getRepeatTable(repFamilyFil= "Alu") parameter setting. Also, to only consider the reads that map to intron-exon or exon-exon junctions set junctionReadsOnly= TRUE, however we recommend setting junctionReadsOnly= FLASE when measuring the intron retention levels (i.e. method=IntRet) and setting junctionReadsOnly= TRUE when measuring the exon-exon junction levels. The junctionReadsOnly' parameter does NOT apply to the intron-spanning levels estimation mode (i.e.method=IntSpan`).

The interest() and interest.sequential() read summarization functions write output text files, additionally they can return a summarizedExperiment object for every sample they analyze. As shown in the following test script we, however usually prevent the individual runs to return any objects (by setting the returnObj=FALSE); instead, after running the analysis for all samples we generate a single summarizedExperiment object that includes results of all analyzed samples. To build such object from the output text files the readInterestResults() function can be used. In the following scripts a bam file from a single MDS sample with mutated ZRSR2 is used which includes all the reads mapped to the gene RHBDD3 only. We run 3 analysis that results to the number of reads mapping to the introns of the gene RHBDD3, number of reads spanning the introns of the gene RHBDD3 and the number of junction reads mapping to the exons of gene RHBDD3. eventually a SummarizedExperiment object is built for each of the 3 analysis that includes the read counts together with the coordinates and annotations of the introns and exons. The same analysis can be run on multiple .bam files to obtain SummarizedExperiment objects that include results for all analyzed .bam files.

# Creating temp directory to store the results
outDir<- file.path(tempdir(),"interestFolder")
dir.create(outDir)
outDir<- normalizePath(outDir)
# Loading suitable bam file
bamF <- system.file("extdata", "small_test_SRR1691637_ZRSR2Mut_RHBDD3.bam",
    package="IntEREst", mustWork=TRUE)
# Choosing reference for the gene RHBDD3
ref<-u12[u12[,"gene_name"]=="RHBDD3",]

# Intron retention analysis
# Reads mapping to inner introns are considered, hence 
# junctionReadsOnly is FALSE
testInterest<- interest(
    bamFileYieldSize=10000,
    junctionReadsOnly=FALSE,
    bamFile=bamF,
    isPaired=TRUE,
    isPairedDuplicate=FALSE,
    isSingleReadDuplicate=NA,
    reference=ref,
    referenceGeneNames=ref[,"ens_gene_id"],
    referenceIntronExon=ref[,"int_ex"],
    repeatsTableToFilter=c(),
    outFile=paste(outDir,
        "intRetRes.tsv", sep="/"),
    logFile=paste(outDir,
        "log.txt", sep="/"),
    method="IntRet",
    clusterNo=1,
    returnObj=FALSE,
    scaleLength= TRUE,
    scaleFragment= TRUE
)

## InERESt: Running bamPreprocess. Detailed log info are written in:  /tmp/RtmpfBYzup/interestFolder/log.txt 
## Log info: Running interest in Parallel mode.
## InERESt: Running interestAnalyse.
## InERESt:interestAnalyse: Begins ...
## Analyzing paired reads, ID:  SRR1691633.22391522 
## Paired intRetRes:  15 
## Paired exExRes:  15 
## BETWEEN SINGLE AND PAIRED N1
## BETWEEN SINGLE AND PAIRED N2 
## BETWEEN SINGLE AND PAIRED N3
## Analyzing single reads, ID:  SRR1691633.10002641 
## Single intRetRes:  15 
##  Single exExRes:  15 
## BETWEEN SINGLE AND PAIRED N4
## InERESt:interestAnalyse: Read counting ends. Running time:  1.66455  secs
## InERESt: Running interestSummarise.
## IntERESt:interestSummarise: Begins ...
## IntERESt:interestSummarise: Normalizing intron retention read levels.
## InERESt: run ends. Full running time:  1.669218  secs

testIntRetObj<- readInterestResults(
    resultFiles= paste(outDir,
        "intRetRes.tsv", sep="/"), 
    sampleNames="small_test_SRR1691637_ZRSR2Mut_RHBDD3", 
    sampleAnnotation=data.frame( 
        type="ZRSR2mut",
        test_ctrl="test"), 
    commonColumns=1:ncol(ref), freqCol=ncol(ref)+1, 
    scaledRetentionCol=ncol(ref)+2, scaleLength=TRUE, scaleFragment=TRUE, 
    reScale=TRUE, geneIdCol="ens_gene_id")

## 
## Reading file 1 / 1 : /tmp/RtmpfBYzup/interestFolder/intRetRes.tsv

# Intron Spanning analysis
# Reads mapping to inner introns are considered, hence 
# junctionReadsOnly is FALSE
testInterest<- interest(
    bamFileYieldSize=10000,
    junctionReadsOnly=FALSE,
    bamFile=bamF,
    isPaired=TRUE,
    isPairedDuplicate=FALSE,
    isSingleReadDuplicate=NA,
    reference=ref,
    referenceGeneNames=ref[,"ens_gene_id"],
    referenceIntronExon=ref[,"int_ex"],
    repeatsTableToFilter=c(),
    outFile=paste(outDir,
        "intSpanRes.tsv", sep="/"),
    logFile=paste(outDir,
        "log.txt", sep="/"),
    method="IntSpan",
    clusterNo=1,
    returnObj=FALSE,
    scaleLength= TRUE,
    scaleFragment= TRUE
)

## InERESt: Running bamPreprocess. Detailed log info are written in:  /tmp/RtmpfBYzup/interestFolder/log.txt 
## Log info: Running interest in Parallel mode.
## InERESt: Running interestAnalyse.
## InERESt:interestAnalyse: Begins ...
## Analyzing paired reads, ID:  SRR1691633.22391522 
## Paired intRetRes:  15 
## Paired exExRes:  15 
## BETWEEN SINGLE AND PAIRED N1
## BETWEEN SINGLE AND PAIRED N2 
## BETWEEN SINGLE AND PAIRED N3
## Analyzing single reads, ID:  SRR1691633.10002641 
## Single intRetRes:  15 
##  Single exExRes:  15 
## BETWEEN SINGLE AND PAIRED N4
## InERESt:interestAnalyse: Read counting ends. Running time:  1.42964  secs
## InERESt: Running interestSummarise.
## IntERESt:interestSummarise: Begins ...
## IntERESt:interestSummarise: Normalizing intron retention read levels.
## InERESt: run ends. Full running time:  1.43241  secs

testIntSpanObj<- readInterestResults(
    resultFiles= paste(outDir,
        "intSpanRes.tsv", sep="/"), 
    sampleNames="small_test_SRR1691637_ZRSR2Mut_RHBDD3", 
    sampleAnnotation=data.frame( 
        type="ZRSR2mut",
        test_ctrl="test"), 
    commonColumns=1:ncol(ref), freqCol=ncol(ref)+1, 
    scaledRetentionCol=ncol(ref)+2, scaleLength=TRUE, scaleFragment=TRUE, 
    reScale=TRUE, geneIdCol="ens_gene_id")

## 
## Reading file 1 / 1 : /tmp/RtmpfBYzup/interestFolder/intSpanRes.tsv

# Exon-exon junction analysis
# Reads mapping to inner exons are NOT considered, hence 
# junctionReadsOnly is TRUE
testInterest<- interest(
    bamFileYieldSize=10000,
    junctionReadsOnly=TRUE,
    bamFile=bamF,
    isPaired=TRUE,
    isPairedDuplicate=FALSE,
    isSingleReadDuplicate=NA,
    reference=ref,
    referenceGeneNames=ref[,"ens_gene_id"],
    referenceIntronExon=ref[,"int_ex"],
    repeatsTableToFilter=c(),
    outFile=paste(outDir,
        "exExRes.tsv", sep="/"),
    logFile=paste(outDir,
        "log.txt", sep="/"),
    method="ExEx",
    clusterNo=1,
    returnObj=FALSE,
    scaleLength= TRUE,
    scaleFragment= TRUE
)

## InERESt: Running bamPreprocess. Detailed log info are written in:  /tmp/RtmpfBYzup/interestFolder/log.txt 
## Log info: Running interest in Parallel mode.
## InERESt: Running interestAnalyse.
## InERESt:interestAnalyse: Begins ...
## Analyzing paired reads, ID:  SRR1691633.22391522 
## Paired intRetRes:  15 
## Paired exExRes:  15 
## BETWEEN SINGLE AND PAIRED N1
## BETWEEN SINGLE AND PAIRED N2 
## BETWEEN SINGLE AND PAIRED N3
## Analyzing single reads, ID:  SRR1691633.10002641 
## Single intRetRes:  15 
##  Single exExRes:  15 
## BETWEEN SINGLE AND PAIRED N4
## InERESt:interestAnalyse: Read counting ends. Running time:  1.503736  secs
## InERESt: Running interestSummarise.
## IntERESt:interestSummarise: Begins ...
## IntERESt:interestSummarise: Normalizing exon-exon junction read levels.
## InERESt: run ends. Full running time:  1.506408  secs

testExExObj<- readInterestResults(
    resultFiles= paste(outDir,
        "exExRes.tsv", sep="/"), 
    sampleNames="small_test_SRR1691637_ZRSR2Mut_RHBDD3", 
    sampleAnnotation=data.frame( 
        type="ZRSR2mut",
        test_ctrl="test"), 
    commonColumns=1:ncol(ref), freqCol=ncol(ref)+1, 
    scaledRetentionCol=ncol(ref)+2, scaleLength=TRUE, scaleFragment=TRUE, 
    reScale=TRUE, geneIdCol="ens_gene_id")

## 
## Reading file 1 / 1 : /tmp/RtmpfBYzup/interestFolder/exExRes.tsv

# View intron retention result object
testIntRetObj

## class: SummarizedExperiment 
## dim: 15 1 
## metadata(2): scaleFragment scaleLength
## assays(2): counts scaledRetention
## rownames: NULL
## rowData names(16): id int_ex_id ... int_type int_subtype
## colnames(1): small_test_SRR1691637_ZRSR2Mut_RHBDD3
## colData names(3): resultFiles type test_ctrl

# View intron spanning  result object
testIntSpanObj

## class: SummarizedExperiment 
## dim: 15 1 
## metadata(2): scaleFragment scaleLength
## assays(2): counts scaledRetention
## rownames: NULL
## rowData names(16): id int_ex_id ... int_type int_subtype
## colnames(1): small_test_SRR1691637_ZRSR2Mut_RHBDD3
## colData names(3): resultFiles type test_ctrl

# View exon-exon junction result object
testExExObj

## class: SummarizedExperiment 
## dim: 15 1 
## metadata(2): scaleFragment scaleLength
## assays(2): counts scaledRetention
## rownames: NULL
## rowData names(16): id int_ex_id ... int_type int_subtype
## colnames(1): small_test_SRR1691637_ZRSR2Mut_RHBDD3
## colData names(3): resultFiles type test_ctrl

# View first rows of intron retention read counts table
head(counts(testIntRetObj))

##      small_test_SRR1691637_ZRSR2Mut_RHBDD3
## [1,]                                     0
## [2,]                                    11
## [3,]                                     0
## [4,]                                    11
## [5,]                                     0
## [6,]                                   180

# View first rows of intron spanning read counts table
head(counts(testIntSpanObj))

##      small_test_SRR1691637_ZRSR2Mut_RHBDD3
## [1,]                                     0
## [2,]                                    39
## [3,]                                     0
## [4,]                                    25
## [5,]                                     0
## [6,]                                     5

# View first rows of exon-exon junction read counts table
head(counts(testExExObj))

##      small_test_SRR1691637_ZRSR2Mut_RHBDD3
## [1,]                                    39
## [2,]                                     0
## [3,]                                    64
## [4,]                                     0
## [5,]                                    23
## [6,]                                     0

Using the test data mdsChr22Obj

As a demo we ran the IntEREst pipeline on 16 .bam files that each includes reads mapped to U12 genes (i.e. genes with at least one U12-type intron) located in chromosome 22. These bam files were results of mapping RNAseq data from bone-marrow samples published by Madan et al. (2015) to the Human genome (hg19). The studied samples were extracted from 16 individuals; out of which 8 were diagnosed with Myelodysplastic syndrome (MDS) and featured ZRSR2 mutation, 4 were diagnosed with MDS but lacked the mutation (referred to as ZRSR2 wild-type MDS samples) and 4 were healthy individuals.

The data is accessible through GEO with the accession number GSE63816 and the scripts that we ran to map the RNAseq data, modify the bam files, extract the reads mapped to U12 genes in chr22 and build mdsChr22Obj, mdsChr22ExObj and mdsChr22RefIntRetSpObj objects are available in the scripts folder of the IntEREst package (See the readme.txt file in the folder for more information). You can get its full path using this script in R: system.file("scripts","readme.txt", package="IntEREst").

The mdsChr22Obj object is a summarizedExperiment object that includes information regarding levels of intron-mapping reads in the U12 genes located on the Chromosome 22, across all the 16 MDS samples. The mdsChr22ExObj object contain the levels of exon-exon junction mapping reads and the mdsChr22RefIntRetSpObj include the levels of intron-spanning reads. Each object include two assays: counts and scaledRetention. Both can be accessed using functions with the same names: counts() and scaledRetention(). The former (counts) returns a data frame which includes the read counts of each intron/exon in each sample, and the latter (scaledRetention) returns a data frame with similar dimensions that includes the FPKM normalized read counts. The result objects also include intron/exon and sample annotations that can be retrieved using rowData() and colData() functions.

# Load library quietly
suppressMessages(library("IntEREst"))
#View object
mdsChr22Obj

## class: SummarizedExperiment 
## dim: 819 16 
## metadata(2): scaleFragment scaleLength
## assays(2): counts scaledRetention
## rownames: NULL
## rowData names(10): chr begin ... collapsed_gene_id intron_type
## colnames(16): SRR1691633 SRR1691634 ... SRR1691647 SRR1691648
## colData names(3): resultFiles type test_ctrl

mdsChr22ExObj

## class: SummarizedExperiment 
## dim: 494 16 
## metadata(2): scaleFragment scaleLength
## assays(2): counts scaledRetention
## rownames: NULL
## rowData names(5): chr begin end transcripts_id int_ex
## colnames(16): SRR1691633 SRR1691634 ... SRR1691647 SRR1691648
## colData names(3): resultFiles type test_ctrl

# See read counts
head(counts(mdsChr22Obj))

##      SRR1691633 SRR1691634 SRR1691635 SRR1691636 SRR1691637 SRR1691638
## [1,]          0          0          0          0          0          0
## [2,]          1          2          1          1          1          1
## [3,]          0          0          0          0          0          0
## [4,]         21         35         45         44         22         21
## [5,]          0          0          0          0          0          0
## [6,]         63         98        128         86         67         98
##      SRR1691639 SRR1691640 SRR1691641 SRR1691642 SRR1691643 SRR1691644
## [1,]          0          0          0          0          0          0
## [2,]          0          2          1          3          2          2
## [3,]          0          0          0          0          0          0
## [4,]         27         20         89         40         28         30
## [5,]          0          0          0          0          0          0
## [6,]         84         33        161         90         24         49
##      SRR1691645 SRR1691646 SRR1691647 SRR1691648
## [1,]          0          0          0          0
## [2,]          3          1          6          1
## [3,]          0          0          0          0
## [4,]         14         14         71         14
## [5,]          0          0          0          0
## [6,]         35         33        112         37

# See FPKM Normalized values
head(scaledRetention(mdsChr22Obj))

##      SRR1691633 SRR1691634 SRR1691635 SRR1691636 SRR1691637 SRR1691638
## [1,]      0.000      0.000      0.000      0.000       0.00      0.000
## [2,]   4675.344   9547.815   2617.253   2708.442    7832.08   3481.506
## [3,]      0.000      0.000      0.000      0.000       0.00      0.000
## [4,]   8125.426  13827.871   9747.011   9862.463   14259.79   6050.618
## [5,]      0.000      0.000      0.000      0.000       0.00      0.000
## [6,]  30339.572  48189.833  34507.300  23992.376   54051.44  35143.788
##      SRR1691639 SRR1691640 SRR1691641 SRR1691642 SRR1691643 SRR1691644
## [1,]      0.000       0.00      0.000       0.00       0.00       0.00
## [2,]      0.000   18404.00   3353.724   18382.35   25100.40   19113.15
## [3,]      0.000       0.00      0.000       0.00       0.00       0.00
## [4,]   8336.627   15230.90  24701.912   20283.98   29081.84   23726.67
## [5,]      0.000       0.00      0.000       0.00       0.00       0.00
## [6,]  32281.073   31278.91  55617.122   56803.84   31025.39   48234.04
##      SRR1691645 SRR1691646 SRR1691647 SRR1691648
## [1,]       0.00       0.00       0.00       0.00
## [2,]   37202.38   15318.63   36507.01   22353.36
## [3,]       0.00       0.00       0.00       0.00
## [4,]   14367.82   17748.48   35751.69   25899.07
## [5,]       0.00       0.00       0.00       0.00
## [6,]   44706.72   52070.18   70193.73   85192.21

# See intron/exon annotations
head(rowData(mdsChr22Obj))

## DataFrame with 6 rows and 10 columns
##           chr     begin       end      strand      int_ex int_ex_num
##   <character> <integer> <integer> <character> <character>  <integer>
## 1       chr22  17618410  17619247           *        exon          1
## 2       chr22  17619248  17619439           *      intron          2
## 3       chr22  17619440  17619628           *        exon          3
## 4       chr22  17619629  17621948           *      intron          4
## 5       chr22  17621949  17622123           *        exon          5
## 6       chr22  17622124  17623987           *      intron          6
##   collapsed_transcripts_id  collapsed_transcripts collapsed_gene_id intron_type
##                  <integer>            <character>       <character>    <factor>
## 1                    21037 uc002zmi.4,uc011agh...   100130717,27440          NA
## 2                    21037 uc002zmi.4,uc011agh...   100130717,27440          U2
## 3                    21037 uc002zmi.4,uc011agh...   100130717,27440          NA
## 4                    21037 uc002zmi.4,uc011agh...   100130717,27440          U2
## 5                    21037 uc002zmi.4,uc011agh...   100130717,27440          NA
## 6                    21037 uc002zmi.4,uc011agh...   100130717,27440          U2

# See sample annotations
head(colData(mdsChr22Obj))

## DataFrame with 6 rows and 3 columns
##                       resultFiles     type test_ctrl
##                       <character> <factor>  <factor>
## SRR1691633 ./SRR1691633_ZRSR2Mu.. ZRSR2mut      test
## SRR1691634 ./SRR1691634_ZRSR2Mu.. ZRSR2mut      test
## SRR1691635 ./SRR1691635_ZRSR2Mu.. ZRSR2mut      test
## SRR1691636 ./SRR1691636_ZRSR2Mu.. ZRSR2mut      test
## SRR1691637 ./SRR1691637_ZRSR2Mu.. ZRSR2mut      test
## SRR1691638 ./SRR1691638_ZRSR2Mu.. ZRSR2mut      test

It is possible to plot() the object to check the distribution of the intron retention levels. The following scripts plot the average retention of all introns across the 3 sample types: ZRSR2 mutated MDS, ZRSR2 wild type MDS and healthy. The lowerPlot=TRUE and upperPlot=TRUE parameter settings ensures that both, the upper and lower triangle of the grid are plotted.

# Retention of all introns
plot(mdsChr22Obj, logScaleBase=exp(1), pch=20, loessLwd=1.2, 
    summary="mean", col="black", sampleAnnoCol="type", 
    lowerPlot=TRUE, upperPlot=TRUE)

Figure 3: Plotting the distribution of the retention levels (\(log_e\) scaled retention) of introns of genes located on chromosome 22. The values have been averaged across the sample types ZRSR2 mutated, ZRSR2 wild type, and healthy.

The following script plots the average retention of the U12 introns across the 3 sample types: ZRSR2 mutated MDS, ZRSR2 MDS wild type and healthy. By default the upper triangle of the grid is plotted only (lowerPlot=FALSE).

#Retention of U12 introns
plot(mdsChr22Obj, logScaleBase=exp(1), pch=20, plotLoess=FALSE, 
    summary="mean", col="black", sampleAnnoCol="type", 
    subsetRows=u12Index(mdsChr22Obj, intTypeCol="intron_type"))

Figure 4: Plotting the distribution of the retention levels (\(log_e\) scaled retention) of introns of genes located on chromosome 22. The values have been averaged across the sample types ZRSR2 mutated, ZRSR2 wild type, and healthy.

Comparing intron retention levels across various samples

IntEREst also provides various tools to compare the retention levels of the introns or exon junction levels across various samples. Initially, we extract the significantly higher and lower retained introns by using exactTestInterest() function which employs the exactTest() function from the edgeR package, i.e. an exact test for differences between two groups of negative-binomial counts. Note that exactTestInterest() makes comparison between a pair of sample types only (e.g. test vs ctrl).

# Check the sample annotation table
getAnnotation(mdsChr22Obj)

## DataFrame with 16 rows and 3 columns
##                       resultFiles     type test_ctrl
##                       <character> <factor>  <factor>
## SRR1691633 ./SRR1691633_ZRSR2Mu.. ZRSR2mut      test
## SRR1691634 ./SRR1691634_ZRSR2Mu.. ZRSR2mut      test
## SRR1691635 ./SRR1691635_ZRSR2Mu.. ZRSR2mut      test
## SRR1691636 ./SRR1691636_ZRSR2Mu.. ZRSR2mut      test
## SRR1691637 ./SRR1691637_ZRSR2Mu.. ZRSR2mut      test
## ...                           ...      ...       ...
## SRR1691644 ./SRR1691644_WT/inte..  ZRSR2wt      ctrl
## SRR1691645 ./SRR1691645_Normal/..  HEALTHY      ctrl
## SRR1691646 ./SRR1691646_Normal/..  HEALTHY      ctrl
## SRR1691647 ./SRR1691647_Normal/..  HEALTHY      ctrl
## SRR1691648 ./SRR1691648_Normal/..  HEALTHY      ctrl

# Run exact test
test<- exactTestInterest(mdsChr22Obj, 
    sampleAnnoCol="test_ctrl", sampleAnnotation=c("ctrl","test"), 
    geneIdCol= "collapsed_transcripts_id", silent=TRUE, disp="common")

# Number of stabilized introns (in Chr 22)
sInt<- length(which(test$table[,"PValue"]<0.05 
    & test$table[,"logFC"]>0 & 
    rowData(mdsChr22Obj)[,"int_ex"]=="intron"))
print(sInt)

## [1] 16

# Number of stabilized (significantly retained) U12 type introns
numStU12Int<- length(which(test$table[,"PValue"]<0.05 & 
    test$table[,"logFC"]>0 & 
    rowData(mdsChr22Obj)[,"intron_type"]=="U12" & 
    !is.na(rowData(mdsChr22Obj)[,"intron_type"])))
# Number of U12 introns
numU12Int<- 
    length(which(rowData(mdsChr22Obj)[,"intron_type"]=="U12" & 
    !is.na(rowData(mdsChr22Obj)[,"intron_type"]))) 
# Fraction(%) of stabilized (significantly retained) U12 introns
perStU12Int<- numStU12Int/numU12Int*100
print(perStU12Int)

## [1] 50

# Number of stabilized U2 type introns
numStU2Int<- length(which(test$table[,"PValue"]<0.05 & 
    test$table[,"logFC"]>0 & 
    rowData(mdsChr22Obj)[,"intron_type"]=="U2" & 
    !is.na(rowData(mdsChr22Obj)[,"intron_type"])))
# Number of U2 introns
numU2Int<- 
    length(which(rowData(mdsChr22Obj)[,"intron_type"]=="U2" & 
    !is.na(rowData(mdsChr22Obj)[,"intron_type"])))
# Fraction(%) of stabilized U2 introns
perStU2Int<- numStU2Int/numU2Int*100
print(perStU2Int)

## [1] 1.827676

As shown in the previous analysis ~50% of U12-type introns (of genes on Chr22) are significantly more retained (i.e. stabilized) in the ZRSR2 mutated samples comparing to the other samples, whereas same comparison shows that only ~1% of the U2-type introns are significantly more retained. For more complex experiments such as comparing samples based on a user defined design matrix other differential expression analysis functions from edgeR package, e.g. Linear Model (GLM) functions, have also been implemented in IntEREst; glmInterest() performs GLM likelihood ratio test, qlfInterest() runs quasi likelihood F-test, and treatInterest() runs fold-change threshold test on the retention levels of the introns/exons. DESeq2 and DEXSeq based functions (deseqInterest() and DEXSeqIntEREst()) are also available in the package. Using the QLF edgeR based method the following commands can be used to extract the data for introns/exons that their retention levels vary significantly across all sample types: ZRSR2 mutation, ZRSR2 wild type, and healthy.

# Extract type of samples
group <- getAnnotation(mdsChr22Obj)[,"type"]
group

##  [1] ZRSR2mut ZRSR2mut ZRSR2mut ZRSR2mut ZRSR2mut ZRSR2mut ZRSR2mut ZRSR2mut
##  [9] ZRSR2wt  ZRSR2wt  ZRSR2wt  ZRSR2wt  HEALTHY  HEALTHY  HEALTHY  HEALTHY 
## Levels: HEALTHY ZRSR2mut ZRSR2wt

# Test retention levels' differentiation across 3 types samples
qlfRes<- qlfInterest(x=mdsChr22Obj, 
    design=model.matrix(~group), silent=TRUE, 
    disp="tagwiseInitTrended", coef=2:3, contrast=NULL, 
    poisson.bound=TRUE)

# Extract index of the introns with significant retention changes
ind= which(qlfRes$table$PValue< 0.05)
# Extract introns with significant retention level changes
variedIntrons= rowData(mdsChr22Obj)[ind,]

#Show first 5 rows and columns of the result table
print(variedIntrons[1:5,1:5])

## DataFrame with 5 rows and 5 columns
##           chr     begin       end      strand      int_ex
##   <character> <integer> <integer> <character> <character>
## 1       chr22  17626008  17629337           *      intron
## 2       chr22  17629451  17630431           *      intron
## 3       chr22  17630636  17640015           *      intron
## 4       chr22  19493005  19494908           +      intron
## 5       chr22  19495041  19495288           +      intron

Next, to better illustrate the differences in the retention levels of different types of introns across the studied samples, we first use the bopxplot() method to illustrate the retention levels of all U12-type and U2-type introns in various sample types, and then we use the u12BoxplotNb() function to compare the retention of the U12 introns to their up- and down-stream U2-type introns.

# boxplot U12 and U2-type introns 
par(mar=c(7,4,2,1))
u12Boxplot(mdsChr22Obj, sampleAnnoCol="type", 
    intExCol="int_ex",  intTypeCol="intron_type", intronExon="intron", 
    col=rep(c("orange", "yellow"),3) ,  lasNames=3, 
    outline=FALSE, ylab="FPKM", cex.axis=0.8)

Figure 5: Boxplot of the retention levels of U12 introns vs U2 introns, summed over samples with similar annotations i.e. ZRSR2 mutation, ZRSR2 wild type, or healthy.

# boxplot U12-type intron and its up/downstream U2-type introns 
par(mar=c(2,4,1,1))
u12BoxplotNb(mdsChr22Obj, sampleAnnoCol="type", lasNames=1,
    intExCol="int_ex", intTypeCol="intron_type", intronExon="intron", 
    boxplotNames=c(), outline=FALSE, plotLegend=TRUE, 
    geneIdCol="collapsed_transcripts_id", xLegend="topleft", 
    col=c("pink", "lightblue", "lightyellow"), ylim=c(0,1e+06), 
    ylab="FPKM", cex.axis=0.8)

Figure 6: Boxplot of retention levels of U12 introns vs their up- and down-stream U2 introns across all samples.

The boxplot clearly shows the increase retention of U12-type introns comparing to all the U2 introns (figure 5) and in particular comparing to the U2-type introns located on the up- or down-stream of the U12-type introns (figure 6). It is also clear that the elevated level of intron retention with U12-type introns is exacerbated in the ZRSR2 mutated samples comparing to the other studied samples. In order to better illustrate the stabilization of the transcripts with U12-type introns comparing to the ones that lack U12-type introns, we plot the density of the log fold-change of the retention (ZRSR2 mutated v.s. other samples) of U12-type introns and compare it to the log fold-change values for randomly selected U2-type introns, and U2-type introns up- or down-stream the U12-type introns.

u12DensityPlotIntron(mdsChr22Obj, 
    type= c("U12", "U2Up", "U2Dn", "U2UpDn", "U2Rand"), 
    fcType= "edgeR", sampleAnnoCol="test_ctrl", 
    sampleAnnotation=c("ctrl","test"), intExCol="int_ex", 
    intTypeCol="intron_type", strandCol= "strand", 
    geneIdCol= "collapsed_transcripts_id", naUnstrand=FALSE, col=c(2,3,4,5,6), 
    lty=c(1,2,3,4,5), lwd=1, plotLegend=TRUE, cexLegend=0.7, 
    xLegend="topright", yLegend=NULL, legend=c(), randomSeed=10,
    ylim=c(0,0.6), xlab=expression("log"[2]*" fold change FPKM"))

Figure 7: Density plot of the log fold change of U12-type introns, random U2-type introns and U2 introns (up / down / up and down)stream of U12-type introns.

# estimate log fold-change of introns 
# by comparing test samples vs ctrl 
# and don't show warnings !
lfcRes<- lfc(mdsChr22Obj, fcType= "edgeR", 
    sampleAnnoCol="test_ctrl",sampleAnnotation=c("ctrl","test"))

# Build the order vector
ord<- rep(1,length(lfcRes))
ord[u12Index(mdsChr22Obj, intTypeCol="intron_type")]=2

# Median of log fold change of U2 introns (ZRSR2 mut. vs ctrl)
median(lfcRes[ord==1])

## [1] 0

# Median of log fold change of U2 introns (ZRSR2 mut. vs ctrl)
median(lfcRes[ord==2])

## [1] 1.008306

As shown in figure 7 (and computed after the plot), when comparing the ZRSR2 mutated samples vs the other samples, for all U2-type introns the most frequent log fold-change (median) is ~0 whereas this value for the U12-type introns is noticeably higher (~1.01). It is also possible to run a statistical test to see if the log fold-changes of U12-type introns (ZRSR2 mutated samples vs other samples) are significantly higher than the log fold-changes of U2-type introns. For this purpose we use the jonckheere.test() function, i.e. Jonckheere-Terpstra ordered alternative hypothesis test, from the Clinfun package.

# Run Jockheere Terpstra's trend test
library(clinfun)
jtRes<- jonckheere.test(lfcRes, ord, alternative = "increasing", 
    nperm=1000)
jtRes

## 
##  Jonckheere-Terpstra test
## 
## data:  
## JT = 11209, p-value = 0.001
## alternative hypothesis: increasing

The result of the Jonckheere-Terpstra test with 1000 permutation runs shows that when comparing the samples that lack the ZRSR2 mutation to the the ZRSR2 mutated samples, the null hypothesis that the log fold-changes of the retentions of U12-type and U2-type introns are equally distributed was rejected with p-value 0.001, while the alternative being that the values in the U12-type introns are higher compared to the U2-type.

Our recommended pipeline for differential intron retention analysis

After building the reference as described in the test scripts above, we recommend running interest() (or interest.sequential()) twice: once on the reference with uncollapsed exons and with method="IntSpan" setting (whcih results to the intron-spanning object). and subsequently on the reference with collapsed exons and with method="IntRet" parameter setting. Next, use applyOverlap(query, subject, type="within", replaceValues=TRUE, FUN=sum) to map the reference with collpased exon to the reference with uncollapsed exon and sum the read-count levels accordingly (whcih results to the intron-mapping object). cbind the 2 objects and continue as the exmple shown below to detect the significantly differential retained introns.

mdsChr22RefIntRetSpObj<- cbind(mdsChr22Obj, mdsChr22IntSpObj)

mdsChr22RefIntRetSpObj<- addAnnotation(x=mdsChr22RefIntRetSpObj, 
    sampleAnnotationType="intronExon", 
    sampleAnnotation=c(rep("intron",16), rep("exon",16))
    )

library(BiocParallel)
mdsChr22RefIntRetSpIntFilObj<- 
    mdsChr22RefIntRetSpObj[rowData(mdsChr22RefIntRetSpObj)$int_ex=="intron",]

# Differential IR analysis run
ddsChr22Diff<- deseqInterest(mdsChr22RefIntRetSpIntFilObj,  
    design=~test_ctrl+test_ctrl:intronExon, 
    sizeFactor=rep(1,nrow(colData(mdsChr22RefIntRetSpIntFilObj))), 
    contrast=list("test_ctrltest.intronExonintron",
        "test_ctrlctrl.intronExonintron"),
    bpparam = SnowParam(workers=20))

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

# See the number of significantly more retained U12 and U2 introns
pThreshold<- 0.01
mdsChr22UpIntInd<- which(ddsChr22Diff$padj< pThreshold & ddsChr22Diff$padj>0)
table(rowData(mdsChr22RefIntRetSpIntFilObj)$intron_type[mdsChr22UpIntInd])

## 
##    U12 U12/U2     U2 
##      9      0     14

# See the fraction of significantly more retained U12 and U2 introns
100*table(rowData(mdsChr22RefIntRetSpIntFilObj)$intron_type[mdsChr22UpIntInd])/
    table(rowData(mdsChr22RefIntRetSpIntFilObj)$intron_type)

## 
##       U12    U12/U2        U2 
## 50.000000            3.655352

Note that the two SummarizedExperiment objects mentioned above (i.e. mdsChr22Obj and mdsChr22ExObj) were the results of running interest() in “IntRet” and “IntSpan” modes on the exon-collapsed reference. As the names suggest these two objects are limited to the U12 intron containing genes on chr22. The described pipeline, analyzes the variation of the intron mapping reads relative to the variation of the intron spanning reads across the studied conditions whilst excluding the reads that map to exons that overlap the studying intronic regions.

IntEREst

Ali Oghabian

2022-11-07

Introduction to IntEREst