tximport vignette

Import and summarize transcript-level abundance estimates for gene-level analysis. The motivation and methods for the functions provided by the tximport package are described in the following article:

Charlotte Soneson, Michael I. Love, Mark D. Robinson (2015): Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research http://dx.doi.org/10.12688/f1000research.7563.1

Import transcript-level estimates

We begin by locating some prepared files that contain transcript abundance estimates for six samples, from the tximportData package. The tximport pipeline will be nearly identical for various quantification tools, usually only requiring one change the type argument. We begin with quantification files generated by the kallisto software, and later show the use of tximport with Salmon, Sailfish, and RSEM.

kallisto

First, we locate the directory containing the files. (Here we use system.file to locate the package directory, but for a typical use, we would just provide a path, e.g. "/path/to/dir".)

library(tximportData)
dir <- system.file("extdata", package = "tximportData")
list.files(dir)
## [1] "cufflinks"            "kallisto"             "rsem"                
## [4] "sailfish"             "salmon"               "samples.txt"         
## [7] "samples_extended.txt" "tx2gene.csv"

Next, we create a named vector pointing to the kallisto files. We will create a vector of filenames first by reading in a table that contains the sample IDs, and then combining this with dir and "abundance.tsv".

samples <- read.table(file.path(dir, "samples.txt"), header = TRUE)
samples
##   pop center                assay    sample experiment       run
## 1 TSI  UNIGE NA20503.1.M_111124_5 ERS185497  ERX163094 ERR188297
## 2 TSI  UNIGE NA20504.1.M_111124_7 ERS185242  ERX162972 ERR188088
## 3 TSI  UNIGE NA20505.1.M_111124_6 ERS185048  ERX163009 ERR188329
## 4 TSI  UNIGE NA20507.1.M_111124_7 ERS185412  ERX163158 ERR188288
## 5 TSI  UNIGE NA20508.1.M_111124_2 ERS185362  ERX163159 ERR188021
## 6 TSI  UNIGE NA20514.1.M_111124_4 ERS185217  ERX163062 ERR188356
files <- file.path(dir, "kallisto", samples$run, "abundance.tsv")
names(files) <- paste0("sample", 1:6)
all(file.exists(files))
## [1] TRUE

Transcripts need to be associated with gene IDs for gene-level summarization. If that information is present in the files, we can skip this step. But for kallisto, Salmon and Sailfish, the files only provide the transcript ID. We first make a data.frame called tx2gene with two columns: 1) transcript ID and 2) gene ID. The column names do not matter but this column order must be used. The transcript ID must be the same one used in the abundance files.

Creating this tx2gene data.frame can be accomplished from a TxDb object and the select function from the AnnotationDbi package. The following code could be used to construct such a table:

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
k <- keys(txdb, keytype = "GENEID")
df <- select(txdb, keys = k, keytype = "GENEID", columns = "TXNAME")
tx2gene <- df[, 2:1]  # tx ID, then gene ID

Note: if you are using an Ensembl transcriptome, the easiest way to create the tx2gene data.frame is to use the ensembldb packages. The annotation packages can be found by version number, and use the pattern EnsDb.Hsapiens.vXX. The transcripts function can be used with return.type="Data.Frame", in order to obtain something like the df object constructed in the code chunk above. See the ensembldb package vignette for more details.

Here we read in a pre-constructed tx2gene table:

tx2gene <- read.csv(file.path(dir, "tx2gene.csv"))
head(tx2gene)
##         TXNAME   GENEID
## 1    NM_130786     A1BG
## 2    NR_015380 A1BG-AS1
## 3 NM_001198818     A1CF
## 4 NM_001198819     A1CF
## 5 NM_001198820     A1CF
## 6    NM_014576     A1CF

The tximport package has a single function for importing transcript-level estimates. The type argument is used to specify what software was used for estimation (“kallisto”, “salmon”, “sailfish”, and “rsem” are implemented). A simple list with matrices, “abundance”, “counts”, and “length”, is returned, where the transcript level information is summarized to the gene-level. The “length” matrix can be used to generate an offset matrix for downstream gene-level differential analysis of count matrices, as shown below.

While tximport works without any dependencies, it is much faster to read in files using the readr package (version >= 0.2.2). To use this, we pass the read_tsv function to tximport.

library(tximport)
library(readr)
txi <- tximport(files, type = "kallisto", tx2gene = tx2gene, reader = read_tsv)
names(txi)
## [1] "abundance"           "counts"              "length"             
## [4] "countsFromAbundance"
head(txi$counts)
##             sample1   sample2    sample3   sample4   sample5  sample6
## A1BG     108.581000 314.42400 110.450000 116.00000  85.80300 75.91360
## A1BG-AS1  86.163600 140.10700 129.994000 146.40800 136.92800 97.29540
## A1CF       9.003863  12.01096   3.005232  15.01082  24.01285 22.01611
## A2M       24.000000   2.00000  21.000000   6.00000  38.00000  8.00000
## A2M-AS1    1.000000   1.00000   1.000000   1.00000   0.00000  0.00000
## A2ML1      3.012760   1.01650   3.049480   2.04965   2.02477  3.04483

We could alternatively generate counts from abundances, using the argument countsFromAbundance, scaled to library size, "scaledTPM", or additionally scaled using the average transcript length, averaged over samples and to library size, "lengthScaledTPM". Using either of these approaches, the counts are not correlated with length, and so the length matrix should not be provided as an offset for downstream analysis packages. For more details on these approaches, see the article listed under citation("tximport").

We can avoid gene-level summarization by setting txOut=TRUE, giving the original transcript level estimates as a list of matrices.

txi.tx <- tximport(files, type = "kallisto", txOut = TRUE, tx2gene = tx2gene, 
    reader = read_tsv)

These matrices can then be summarized afterwards using the function summarizeToGene. This then gives the identical list of matrices as using txOut=FALSE (default) in the first tximport call.

txi.sum <- summarizeToGene(txi.tx, tx2gene)
all.equal(txi$counts, txi.sum$counts)
## [1] TRUE

Salmon / Sailfish

Salmon or Sailfish quant.sf files can be imported by setting type to "salmon" or "sailfish".

files <- file.path(dir, "salmon", samples$run, "quant.sf")
names(files) <- paste0("sample", 1:6)
txi.salmon <- tximport(files, type = "salmon", tx2gene = tx2gene, reader = read_tsv)
head(txi.salmon$counts)
##             sample1   sample2    sample3   sample4   sample5   sample6
## A1BG     109.232000 316.22400 110.638000 116.00000  86.38430  76.91630
## A1BG-AS1  83.969700 138.44900 119.274000 151.08300 123.98500 103.25100
## A1CF       9.030691  10.01847   5.019242  13.01820  25.21914  25.07356
## A2M       24.000000   2.00000  21.000000   6.00000  38.00000   8.00000
## A2M-AS1    1.000000   1.00000   1.000000   1.00000   0.00000   0.00000
## A2ML1      3.047950   1.02987   4.076160   1.04945   3.07761   5.12409

RSEM

Likewise, RSEM sample.genes.results files can be imported by setting type to "rsem".

files <- file.path(dir, "rsem", samples$run, paste0(samples$run, ".genes.results"))
names(files) <- paste0("sample", 1:6)
txi.rsem <- tximport(files, type = "rsem", reader = read_tsv)
head(txi.rsem$counts)
##          sample1 sample2 sample3 sample4 sample5 sample6
## A1BG       94.64  278.03   94.07   96.00   55.00   64.03
## A1BG-AS1   64.28  114.08   98.88  109.05   95.32   73.11
## A1CF        0.00    2.00    1.00    1.00    0.00    1.00
## A2M        24.00    2.00   18.00    4.00   35.00    8.00
## A2M-AS1     1.00    1.00    1.00    0.00    0.00    0.00
## A2ML1       0.84    2.89    0.00    1.00    2.00    3.11

Import with edgeR, DESeq2, limma-voom

Note: there are two suggested ways of importing estimates for use with gene-level differential expression methods. The first method, which we show below for edgeR and for DESeq2, is to use the estimated counts from the quantification tools, and additionally to use the transcript-level abundance estimates to calculate an offset that corrects for changes to the average transcript length across samples. The code examples below accomplish these steps for you. The second method is to use the tximport argument countsFromAbundance="lengthScaledTPM" or "scaledTPM", and then to use the count matrix txi$counts directly as you would a regular count matrix with these software.

An example of creating a DGEList for use with edgeR:

library(edgeR)
cts <- txi$counts
normMat <- txi$length
normMat <- normMat/exp(rowMeans(log(normMat)))
library(edgeR)
o <- log(calcNormFactors(cts/normMat)) + log(colSums(cts/normMat))
y <- DGEList(cts)
y$offset <- t(t(log(normMat)) + o)
# y is now ready for estimate dispersion functions see edgeR User's Guide

An example of creating a DESeqDataSet for use with DESeq2:

library(DESeq2)

The user should make sure the rownames of sampleTable align with the colnames of txi$counts, if there are colnames. The best practice is to read sampleTable from a CSV file, and to construct files from a column of sampleTable, as was shown in the tximport examples above.

sampleTable <- data.frame(condition = factor(rep(c("A", "B"), each = 3)))
rownames(sampleTable) <- colnames(txi$counts)
dds <- DESeqDataSetFromTximport(txi, sampleTable, ~condition)
# dds is now ready for DESeq() see DESeq2 vignette

An example for use with limma-voom. limma-voom does not use the offset matrix stored in y$offset, so we recommend using the scaled counts generated from abundances, either "scaledTPM" or "lengthScaledTPM":

files <- file.path(dir, "kallisto", samples$run, "abundance.tsv")
names(files) <- paste0("sample", 1:6)
txi <- tximport(files, type = "kallisto", tx2gene = tx2gene, reader = read_tsv, 
    countsFromAbundance = "lengthScaledTPM")
library(limma)
y <- DGEList(txi$counts)
y <- calcNormFactors(y)
design <- model.matrix(~condition, data = sampleTable)
v <- voom(y, design)
# v is now ready for lmFit() see limma User's Guide

Acknowledgments

The development of tximport has benefited from contributions and suggestions from:

Matt Shirley, Stephen Turner, Rob Patro, Richard Smith-Unna, Rory Kirchner, Martin Morgan

Session info

sessionInfo()
## R version 3.3.0 (2016-05-03)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.4 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] DESeq2_1.12.3                          
##  [2] SummarizedExperiment_1.2.3             
##  [3] edgeR_3.14.0                           
##  [4] limma_3.28.10                          
##  [5] readr_0.2.2                            
##  [6] tximport_1.0.3                         
##  [7] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [8] GenomicFeatures_1.24.2                 
##  [9] AnnotationDbi_1.34.3                   
## [10] Biobase_2.32.0                         
## [11] GenomicRanges_1.24.2                   
## [12] GenomeInfoDb_1.8.2                     
## [13] IRanges_2.6.1                          
## [14] S4Vectors_0.10.1                       
## [15] BiocGenerics_0.18.0                    
## [16] tximportData_1.0.2                     
## [17] knitr_1.13                             
## 
## loaded via a namespace (and not attached):
##  [1] genefilter_1.54.2       locfit_1.5-9.1         
##  [3] splines_3.3.0           lattice_0.20-33        
##  [5] colorspace_1.2-6        rtracklayer_1.32.1     
##  [7] chron_2.3-47            survival_2.39-4        
##  [9] XML_3.98-1.4            foreign_0.8-66         
## [11] DBI_0.4-1               BiocParallel_1.6.2     
## [13] RColorBrewer_1.1-2      plyr_1.8.4             
## [15] stringr_1.0.0           zlibbioc_1.18.0        
## [17] Biostrings_2.40.2       munsell_0.4.3          
## [19] gtable_0.2.0            evaluate_0.9           
## [21] latticeExtra_0.6-28     geneplotter_1.50.0     
## [23] biomaRt_2.28.0          Rcpp_0.12.5            
## [25] acepack_1.3-3.3         xtable_1.8-2           
## [27] scales_0.4.0            formatR_1.4            
## [29] Hmisc_3.17-4            annotate_1.50.0        
## [31] XVector_0.12.0          Rsamtools_1.24.0       
## [33] gridExtra_2.2.1         ggplot2_2.1.0          
## [35] stringi_1.1.1           grid_3.3.0             
## [37] tools_3.3.0             bitops_1.0-6           
## [39] magrittr_1.5            RCurl_1.95-4.8         
## [41] RSQLite_1.0.0           Formula_1.2-1          
## [43] cluster_2.0.4           Matrix_1.2-6           
## [45] data.table_1.9.6        rpart_4.1-10           
## [47] GenomicAlignments_1.8.3 nnet_7.3-12