Abstract
NBSplice is an R package that uses isoform expression counts to evaluate differential splicing by means of generalized linear models at the gene level. The package allows the estimation of differences between isoforms’ relative expression to infer changes in alternative splicing patterns. This vignette explains the use of the package in a typical analysis workflow. NBSplice package version: 1.14.0NBSplice
is an R package sitting on Bioconductor. You could download the source file from the Bioconductor site (http://bioconductor.org/) and then use R CMD install
. Another easy way to install it is by doing:
Here we show the most basic steps for a differential splicing analysis pipeline using the NBSplice
R package.
To start the analysis, an expression matrix at the isoform/transcript level called isoCounts
, previously generated by upstream processing tools like Kallisto (Bray et al. 2016) or RSEM (Li and Dewey 2011), is required. You also need a table describing isoform-gene relationships called geneIso
and a table of experiment information called designMatrix
. In particular, this table must contain a column storing the experiment factor which will be evaluated. Here, we assume that this column is called condition
.
As input, the NBSplice
package expects isoform expression counts obtained from RNA-seq in the form of a matrix of numerical values. In this matrix, the count value found in the i-th row and the j-th column represents the expression counts assigned to isoform i in experimental sample j. The values in the matrix should be un-normalized. We suggest the use of the effective counts typically reported by the mentioned quantification tools. It is not necessary to round those because NBSplice
will do it internally. The expression matrix could be easily obtained by the combination of quantification files for all samples. For instance, if Kallisto was used to transcript quantification, the expression matrix should be built combining the est_counts
columns of those files as is shown in the code below.
fileNames<-c(paste(rep("C1R",4), 1:4,"/abundance.tsv", sep=""),
paste(rep("C2R",4), 1:4,"/abundance.tsv", sep=""))
myInfo<-lapply(seq_along(fileNames), function(x){
quant<-read.delim(fileNames[x], header = TRUE)
expression<-quant[,"est_counts"]
return(expression)
})
# Adding the isoform IDS as row names
isoform_id<-read.delim(fileNames[x], header = TRUE)[,"target_id"]
expressionMatrix<-(do.call(cbind, myInfo))
rownames(expressionMatrix)<-isoform_id
# Adding the samples names as column names
colnames(expressionMatrix)<-c(paste(rep("C1R",4), 1:4, sep=""),
paste(rep("C2R",4), 1:4, sep=""))
If another quantification tool was used, you only need to change the name of the quantification files and the name of the columns having estimated counts and isoforms IDs. For instance, for RSEM quantification, isoforms counts and IDS are in the expected_count
and transcript_id
columns of the Sample.isoforms.results
file, respectively.
As an example, we have included an isoCounts
matrix which is a subset of one of the simulated matrix used to demonstrate NBSplice
utility.
## C1R1 C1R2 C1R3 C1R4 C2R1 C2R2 C2R3 C2R4
## ENST00000228345 79 0 106 99 76 0 68 23
## ENST00000358495 567 212 162 715 70 176 255 243
## ENST00000397230 15 0 0 28 0 0 22 3
## ENST00000430095 0 6 0 77 0 0 27 59
## ENST00000461568 11 0 0 0 0 2 0 0
## ENST00000463750 10 31 126 60 88 0 0 31
## [1] 3144 8
As can be noted, the isoCounts
matrix represents an experiment with 8
samples were 3122
isoforms were quantified.
The geneIso
matrix must have two columns called isoform_id
and gene_id
and as many rows as the isoCounts
matrix. The i-th row of the geneIso
matrix specifies from which gene was originated the i-th isoform. This information will be used by NBSplice
methods for model fitting and hypothesis tests. This matrix could be easily obtained from the annotation file used for isoform quantification. For instance, if RSEM was used, the matrix can be built taking the first two columns (transcript_id
and gene_id
) of the Sample.isoforms.results
file. In the case of Kallisto, this information is not stored in the quantification file. You could use the biomaRt
R package to obtain gene IDs for the quantified isoforms or the refGenome
package to extract those from the annotation file.
As an example, we provided a geneIso
matrix associated with the isoCounts
object previously loaded.
## gene_id isoform_id
## ENST00000228345 ENSG00000002016 ENST00000228345
## ENST00000358495 ENSG00000002016 ENST00000358495
## ENST00000397230 ENSG00000002016 ENST00000397230
## ENST00000430095 ENSG00000002016 ENST00000430095
## ENST00000461568 ENSG00000002016 ENST00000461568
## ENST00000463750 ENSG00000002016 ENST00000463750
## [1] 3144 2
This matrix contains annotation information, so, you need to build it everytime that you change your reference transcriptome, downloading the respective information from the database site.
The designMatrix
table must contain experiment information relevant for the differential splicing analysis. Its number of columns is variable but it should have as rows as columns the isoCounts
matrix has. Thus, in the designMatrix
the i-th row specifies experimental information associated to the i-th sample. It is worth to mention that the order and names of isoCounts
columns must be the same of the designMatrix
rows. Currently, NBSplice
allows two-levels comparison of single-factor experiments (multiple-factor experiment comparisons are in development). In order to do this, it is necessary to specify the argument colName
which indicates the name of the designMatrix
column corresponding to that factor. We provided the designMatrix
object associated to our isoCounts
matrix which could be loaded doing data(designMatrix, package="NBSplice")
. Alternatively, this matrix could be created using the code below.
designMatrix<-data.frame(sample=c(paste(rep("C1R", 4), 1:4, sep=""),
paste(rep("C2R", 4), 1:4, sep="")), condition=factor(c(
rep("Normal", 4), rep("Tumor", 4)), levels=c("Normal",
"Tumor")))
rownames(designMatrix)<-designMatrix[,"sample"]
designMatrix
## sample condition
## C1R1 C1R1 Normal
## C1R2 C1R2 Normal
## C1R3 C1R3 Normal
## C1R4 C1R4 Normal
## C2R1 C2R1 Tumor
## C2R2 C2R2 Tumor
## C2R3 C2R3 Tumor
## C2R4 C2R4 Tumor
In our case, we must define our colName
argument indicating that our factor of interest is condition.
## [1] "Normal" "Tumor"
As can be noted, our experiment involved two experimental conditions: Normal and Tumor. Note that this column of the designMatrix
is a factor
with two levels where the first level, commonly taken as the reference, is Normal.
NBSplice
incorporates a new class called IsoDataSet
to store expression counts and experimental data. An object of this class has the next slots: counts
, geneCounts
, colData
, isoGeneRel
, design
and lowExpIndex
. The first slot will contain the isoCounts
matrix, whereas, the second one store the expression counts at the gene level. To do this, NBSplice
incorporates a function which takes every gene of geneIso
and, for each sample, compute its total expression as the sum of the counts of all its isoforms contained in isoCounts
. The third and fifth slots correspond to designMatrix
and geneIso
objects. The slot called design
save the formula used to model fitting and hypothesis tests, and is internally defined by NBSplice
based on the colName
argument. The last argument, lowExpIndex
, stores the indexes of low-expressed isoforms. The way to define those isoforms is explained in Identifying low-expressed isoforms.
The typical way to build an IsoDataSet
object is using its constructor which have the same name as the class. In addition to the four arguments previously defined, it is possible to define the BPPARAM
argument which must be a BiocParallelParam
instance defining the parallel back-end to be used (Morgan et al., n.d.).
NBSplice
also provides several methods for easy object and slots inspections.
## IsoDataSet
## Isoform Counts:
## C1R1 C1R2 C1R3 C1R4 C2R1 C2R2 C2R3
## ENST00000228345 111.79303 0.0000 149.7884 141.06040 111.9745 0.00 100.9089
## ENST00000358495 802.36265 296.6549 228.9219 1018.76958 103.1344 258.33 378.4085
## ENST00000397230 21.22653 0.0000 0.0000 39.89587 0.0000 0.00 32.6470
## C2R4
## ENST00000228345 36.469152
## ENST00000358495 385.304517
## ENST00000397230 4.756846
##
## Gene Counts:
## C1R1 C1R2 C1R3 C1R4 C2R1 C2R2
## ENSG00000002016 1158.9683 481.3646 854.9243 1667.0775 495.0451 408.04405
## ENSG00000005059 182.5481 158.1227 72.0680 115.4131 139.9681 58.71137
## ENSG00000005889 1982.5575 1859.6906 1575.6043 1530.2917 1461.5618 1241.74555
## C2R3 C2R4
## ENSG00000002016 651.4561 692.9139
## ENSG00000005059 136.5238 144.2910
## ENSG00000005889 1167.8724 2177.0498
##
## Isoform-Gene relationship:
## gene_id isoform_id
## ENST00000228345 ENSG00000002016 ENST00000228345
## ENST00000358495 ENSG00000002016 ENST00000358495
## ENST00000397230 ENSG00000002016 ENST00000397230
##
## Design Matrix:
## sample condition
## C1R1 C1R1 Normal
## C1R2 C1R2 Normal
## C1R3 C1R3 Normal
##
## Formula:
## counts ~ condition + iso + condition:iso
## <environment: 0x557a1268a528>
## C1R1 C1R2 C1R3 C1R4 C2R1 C2R2
## ENST00000228345 111.79303 0.000000 149.7884 141.06040 111.9745 0.000000
## ENST00000358495 802.36265 296.654936 228.9219 1018.76958 103.1344 258.330043
## ENST00000397230 21.22653 0.000000 0.0000 39.89587 0.0000 0.000000
## ENST00000430095 0.00000 8.395894 0.0000 109.71365 0.0000 0.000000
## ENST00000461568 15.56612 0.000000 0.0000 0.00000 0.0000 2.935569
## ENST00000463750 14.15102 43.378788 178.0503 85.49115 129.6547 0.000000
## C2R3 C2R4
## ENST00000228345 100.90892 36.469152
## ENST00000358495 378.40846 385.304517
## ENST00000397230 32.64700 4.756846
## ENST00000430095 40.06678 93.551303
## ENST00000461568 0.00000 0.000000
## ENST00000463750 0.00000 49.154074
Note that the isoCounts
method returns the counts
slot but it seems to be different to the IsoCounts
object previously loaded. This is because this method has an additional logical parameter, CPM
with default value TRUE
, useful to indicate if expression counts should be returned in counts per million (CPM) scale.
Low expression transcripts have been widely recognized as a source of noise in model fitting and parameter estimation of RNA-seq expression counts. Typically, count thresholds are used to decide if a gene or an isoform has low expression. However, NBSplice
assumes that an isoform has a low expression if their absolute or relative expression is lower than specifics thresholds. This assumption is due to the package model infers differential splicing by means of changes in relative expression of isoforms. Consequently, a transcript which has a very low relative expression in both conditions could exhibit a small change but enough to be erroneously detected as significant. The buildLowExpIdx
method is responsible for the identification of low-expressed isoforms. It takes an IsoDataSet
object and two expression thresholds and creates a low-expression index and stores this index in the lowExpIndex
object slot. The two thresholds are called ratioThres
and countsThres
. The first one of those is used to set a minimum of relative expression that each isoform should achieve in all experimental samples. The second one indicates a threshold in CPM for mean expression across conditions. The method also needs the specification of the colName
argument, in order to compute mean expressions per factor level, and admits parallelization using the BPPARAM
argument.
Once an IsoDataSet
was built and its low-expressed isoforms were identified, you could use the NBSplice
for differential splicing evaluation using the NBTest
method. It provides a user-friendly interface between model fitting and hypothesis testing and the user by means a single code line. The theory behind the NBSplice
model is explained in NBSplice strategy. Briefly, NBTest
iterates along genes obtaining significance p-values at gene and isoform level indicating the occurrence of differential splicing and significant changes in isoform proportion, respectively. Then, isoform and gene p-values are adjusted. Low-expressed isoforms are ignored for model fitting but their mean relative expression per condition are also computed. The method requires the specification of the colName
, as well as the statistic assumed for hypothesis test p-values distribution. As the colName
factor could have more than two levels, it is necessary the specification of an additional argument defining the two levels to be contrasted. By default, the contrast
argument takes the two first levels of colName
. NBTest
returns an NBSpliceRes
object which stores the obtained results.
NBSpliceRes
is another new class provided by NBSplice to facilitate the manipulation and exploration of the differential splicing results obtained using the NBTest
method. An object of this class has three slots called results
, lowExpIndex
and contrast
. The first slot stores differential splicing results for all isoforms, whereas the second slot keeps the indexes of low-expressed isoforms and the third one contains the evaluated contrast. Specific methods for slot accession are also provided by NBSplice
## [1] 1 3 4 5 6 7
## [1] "Normal" "Tumor"
## iso gene ratio_Normal ratio_Tumor odd
## 2 ENST00000358495 ENSG00000002016 0.54719747 0.49422479 -0.10181928
## 21 ENST00000394650 ENSG00000005059 0.69478391 0.78304282 0.11958650
## 26 ENST00000379177 ENSG00000005889 0.34888943 0.25494787 -0.31369597
## 27 ENST00000379188 ENSG00000005889 0.40700345 0.34146857 -0.17556603
## 35 ENST00000540034 ENSG00000005889 0.08976672 0.08393136 -0.06721502
## 37 ENST00000219069 ENSG00000006194 0.76318911 0.80921396 0.05855751
## stat pval genePval FDR geneFDR
## 2 0.14639753 0.7151892 0.7151892 0.9926409 0.9995640
## 21 1.05528398 0.3439129 0.3439129 0.7935389 0.8405744
## 26 0.55196653 0.4671017 0.8761851 0.9190379 0.9995640
## 27 0.17311930 0.6822731 0.8761851 0.9901351 0.9995640
## 35 0.02497649 0.8761851 0.8761851 0.9982574 0.9995640
## 37 0.06668665 0.8005993 0.7438810 0.9982574 0.9995640
The data.frame
containing differential splicing results has the next columns:
iso
: Isoform name.gene
: Gene name.ratio_X
: Relative expression of the isoform in experimental condition X, which is the first value specified with the contrast
argument.ratio_Y
: Relative expression of the isoform in experimental condition Y, which is the second value specified with the contrast
argument.odd
: Ratio between isoform’s relative expression in condition X and Y.stat
: Statistic of the hypothesis test performed to decide if the change in isoform’s relative expression between X and Y conditions was significant.pval
: P-value at the isoform level corresponding to the statistic stat
.genePval
: P-value at the gene level.FDR
: Adjusted p-value at the isoform level.genePval
: Adjusted p-value at the gene level.In particular, the pval
or its adjusted value, FDR
, is useful for deciding if an isoform experimented significant change in its relative expression due to the change between X and Y conditions, here called Normal and Tumor. Whereas, the genePval
or its adjusted value, geneFDR
, will help you to decide if a gene evidenced significant changes in its alternative splicing pattern when conditions X and Y were contrasted.
In the example illustrated above, non-reliable isoforms were discarded by means of the filter
argument of the results
methods which has TRUE
as its default value. Non-reliable refers to both low expressed isoforms and those cases where model estimations did not achieve convergence. To obtain the full results
matrix only you need specifies that filter
argument is FALSE
.
## iso gene ratio_Normal ratio_Tumor odd stat
## 1 ENST00000228345 ENSG00000002016 0.089070273 0.108429887 NA NA
## 2 ENST00000358495 ENSG00000002016 0.547197468 0.494224790 -0.1018193 0.1463975
## 3 ENST00000397230 ENSG00000002016 0.010561661 0.014244721 NA NA
## 4 ENST00000430095 ENSG00000002016 0.020813457 0.049128715 NA NA
## 5 ENST00000461568 ENSG00000002016 0.003357753 0.001798561 NA NA
## 6 ENST00000463750 ENSG00000002016 0.090468201 0.083210744 NA NA
## pval genePval FDR geneFDR
## 1 NA 0.7151892 NA 0.999564
## 2 0.7151892 0.7151892 0.9926409 0.999564
## 3 NA 0.7151892 NA 0.999564
## 4 NA 0.7151892 NA 0.999564
## 5 NA 0.7151892 NA 0.999564
## 6 NA 0.7151892 NA 0.999564
Non-reliable isoforms could be easily detected in the obtained matrix because they present NA
value in all parameters derived from model fitting and hypothesis test. It is worth to note that although non-reliable isoforms were not analyzed by statistical models, they have been used to compute the total gene counts so in order to avoid overestimation of relative expression of the well-expressed isoforms.
The NBSpliceRes
class has several methods useful for differential splicing results exploration.
NBSplice
assumes a gene is differentially spliced if its p-value (raw or adjusted) is lower than a significant threshold (default value = 0.05). The results table only for those differentially spliced genes is easily extracted using the GetDSResults
method. Whereas, the names of those genes is obtained by means of the GetDSGenes
. Both methods expect at least one argument of class NBSpliceRes
. In addition, they could receive a logical parameter, adjusted
, indicating if p-values must be adjusted (default) or not, and a numeric parameter, p.value
, establishing the significance threshold (default value = 0.05).
## iso gene ratio_Normal ratio_Tumor odd
## 378 ENST00000206514 ENSG00000092068 0.03696968 0.07501606 0.7076040
## 379 ENST00000316902 ENSG00000092068 0.51890400 0.17702007 -1.0754558
## 381 ENST00000339733 ENSG00000092068 0.03601217 0.05323293 0.3908204
## 383 ENST00000422941 ENSG00000092068 0.02884770 0.03681750 0.2439428
## 386 ENST00000524758 ENSG00000092068 0.03023622 0.04352228 0.3642326
## 388 ENST00000528186 ENSG00000092068 0.02675809 0.04615988 0.5452741
## stat pval genePval FDR geneFDR
## 378 6.0654084 0.0170055687 0.00281695 0.128238715 0.03830455
## 379 14.3400329 0.0003851829 0.00281695 0.009577521 0.03830455
## 381 1.8410376 0.1804745117 0.00281695 0.566677648 0.03830455
## 383 0.7091551 0.4034386713 0.00281695 0.854605021 0.03830455
## 386 1.5880384 0.2130252802 0.00281695 0.629128681 0.03830455
## 388 3.5529531 0.0648255060 0.00281695 0.305294101 0.03830455
## [1] 110 10
## [1] "ENSG00000092068" "ENSG00000103275" "ENSG00000103942" "ENSG00000119446"
## [5] "ENSG00000123983" "ENSG00000126883"
## [1] 33
As can be seen, in our example, NBSplice
detected 184 isoforms with a significant change of relative expression between Normal and Tumor condition whereas 40 genes were identified as differentially spliced.
The scatter plot of isoforms’ relative expression between the two contrasted conditions is explored using the plotRatiosDisp
. It returns a ggplot2
object which could be then easily modified and customized.
As can be noted, isoforms are colored according to if they came from those genes detected as differentially spliced or not. To do this, the adjusted
and p.value
parameters, previously used to call the getDSResults
method, could be specified in the plotRatiosDisp
calling.
The volcano plot is commonly used to explore the dispersion between statistical significance from a statistical test with the magnitude of the change. In the case of NBSplice
the isoform p-value and the difference between isoform’s relative expression per contrasted condition are explored using the plotVolcano
method. In addition, isoforms are colored according to if they came from those genes detected as differentially spliced or not.
NBSplice
also provides two methods to individually explore differentially spliced genes: GetGeneResults
and plotGeneResults
. The first one is useful for obtaining the differential expression results of a single gene, specified using the gene
argument. The second one is a graphical method to generate a bar-plot illustrating the isoforms’ relative expression values in the two contrasted conditions. In both cases, it is possible filtering those non-reliable isoforms by means of the filterLowExpIso
argument. In the case of the plotGeneResults
method, non-significant isoforms could be also filtered out setting the filterNotSignificant
argument to TRUE
which is its default value. As an example, the results obtained for the first analyzed gene are shown below.
## Select the first differentially spliced gene
gene<-GetDSGenes(myDSResults)[1]
GetGeneResults(myDSResults, gene)
## iso gene ratio_Normal ratio_Tumor odd
## 378 ENST00000206514 ENSG00000092068 0.03696968 0.07501606 0.7076040
## 379 ENST00000316902 ENSG00000092068 0.51890400 0.17702007 -1.0754558
## 381 ENST00000339733 ENSG00000092068 0.03601217 0.05323293 0.3908204
## 383 ENST00000422941 ENSG00000092068 0.02884770 0.03681750 0.2439428
## 386 ENST00000524758 ENSG00000092068 0.03023622 0.04352228 0.3642326
## 388 ENST00000528186 ENSG00000092068 0.02675809 0.04615988 0.5452741
## 390 ENST00000528860 ENSG00000092068 0.02884770 0.03681750 0.2439428
## 391 ENST00000529705 ENSG00000092068 0.02209550 0.06319893 1.0509135
## 392 ENST00000532568 ENSG00000092068 0.03410357 0.07346647 0.7674271
## stat pval genePval FDR geneFDR
## 378 6.0654084 0.0170055687 0.00281695 0.128238715 0.03830455
## 379 14.3400329 0.0003851829 0.00281695 0.009577521 0.03830455
## 381 1.8410376 0.1804745117 0.00281695 0.566677648 0.03830455
## 383 0.7091551 0.4034386713 0.00281695 0.854605021 0.03830455
## 386 1.5880384 0.2130252802 0.00281695 0.629128681 0.03830455
## 388 3.5529531 0.0648255060 0.00281695 0.305294101 0.03830455
## 390 0.7091551 0.4034386713 0.00281695 0.854605021 0.03830455
## 391 13.1933293 0.0006259889 0.00281695 0.014451319 0.03830455
## 392 7.1220185 0.0100334774 0.00281695 0.093240396 0.03830455
NBSplice
, is a package developed for differential splicing detection starting from isoform quantification data from RNA-seq experiments. Our tool is based on generalized linear models (GLMs) and uses them to infer significant differences in isoforms relative expression values between two experimental conditions. In addition, NBSplice
not only reveals what genes were under DS but also reveals the identity of the gene isoforms affected by it as well as