Import and summarize transcript-level abundance estimates for transcript- and gene-level analysis with Bioconductor packages, such as edgeR, DESeq2, and limma-voom. The motivation and methods for the functions provided by the tximport package are described in the following article (Soneson, Love, and Robinson 2015):
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
In particular, the tximport pipeline offers the following benefits: (i) this approach corrects for potential changes in gene length across samples (e.g. from differential isoform usage) (Trapnell et al. 2013), (ii) some of the upstream quantification methods (Salmon, Sailfish, kallisto) are substantially faster and require less memory and disk usage compared to alignment-based methods that require creation and storage of BAM files, and (iii) it is possible to avoid discarding those fragments that can align to multiple genes with homologous sequence, thus increasing sensitivity (Robert and Watson 2015).
Note: another Bioconductor package, tximeta (Love et al. 2020), extends tximport,
offering the same functionality, plus the additional benefit of
automatic addition of annotation metadata for commonly used
transcriptomes (GENCODE, Ensembl, RefSeq for human and mouse). See the
tximeta package
vignette for more details. Whereas tximport outputs a
simple list of matrices, tximeta will output a
SummarizedExperiment object with appropriate GRanges
added if the transcriptome is from one of the sources above for human
and mouse. tximeta also offers easy conversion to data objects
used by edgeR and limma with the
makeDGEList function.
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 Salmon software, and later show the use of
tximport with any of:
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".)
## [1] "alevin" "cufflinks"
## [3] "kallisto" "kallisto_boot"
## [5] "refseq" "rsem"
## [7] "sailfish" "salmon"
## [9] "salmon_dm" "salmon_ec"
## [11] "salmon_gibbs" "samples.txt"
## [13] "samples_extended.txt" "tx2gene.csv"
## [15] "tx2gene.ensembl.v87.csv" "tx2gene.gencode.v27.csv"
## [17] "tx2gene_alevin.tsv"Next, we create a named vector pointing to the quantification 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 "quant.sf.gz". (We gzipped the quantification files to
make the data package smaller, this is not a problem for R functions
that we use to import the files.)
## 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 ERR188356files <- file.path(dir, "salmon", samples$run, "quant.sf.gz")
names(files) <- paste0("sample", 1:6)
all(file.exists(files))## [1] TRUETranscripts need to be associated with gene IDs for gene-level
summarization. If that information is present in the files, we can skip
this step. For Salmon, Sailfish, and kallisto 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 = "TXNAME")
tx2gene <- select(txdb, k, "GENEID", "TXNAME")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="DataFrame", in order to obtain something like
the df object constructed in the code chunk above. See the
ensembldb package vignette for more details.
In this case, we’ve used the Gencode v27 CHR transcripts to build our
index, and we used makeTxDbFromGFF and code similar to the
chunk above to build the tx2gene table. We then read in a
pre-constructed tx2gene table:
## # A tibble: 6 × 2
## TXNAME GENEID
## <chr> <chr>
## 1 ENST00000456328.2 ENSG00000223972.5
## 2 ENST00000450305.2 ENSG00000223972.5
## 3 ENST00000473358.1 ENSG00000243485.5
## 4 ENST00000469289.1 ENSG00000243485.5
## 5 ENST00000607096.1 ENSG00000284332.1
## 6 ENST00000606857.1 ENSG00000268020.3The tximport package has a single function for importing
transcript-level estimates. The type argument is used to
specify what software was used for estimation. A simple list with
matrices, "abundance", "counts", and
"length", is returned, where the transcript level
information is summarized to the gene-level. Typically, abundance is
provided by the quantification tools as TPM (transcripts-per-million),
while the counts are estimated counts (possibly fractional), and the
"length" matrix contains the effective gene lengths. The
"length" matrix can be used to generate an offset matrix
for downstream gene-level differential analysis of count matrices, as
shown below.
Note: While tximport works without any
dependencies, it is significantly faster to read in files using the
readr package. If tximport detects that readr
is installed, then it will use the readr::read_tsv function
by default. A change from version 1.2 to 1.4 is that the reader is not
specified by the user anymore, but chosen automatically based on the
availability of the readr package. Advanced users can still
customize the import of files using the importer
argument.
## [1] "abundance" "counts" "length"
## [4] "countsFromAbundance"## sample1 sample2 sample3 sample4 sample5
## ENSG00000000003.14 2.58012 2.0000 27.09648 8.48076 5.11217
## ENSG00000000005.5 0.00000 0.0000 0.00000 0.00000 0.00000
## ENSG00000000419.12 1056.99960 1337.9970 1452.99497 1289.00390 920.99960
## ENSG00000000457.13 462.88490 498.8622 560.75380 386.28665 531.57867
## ENSG00000000460.16 633.52972 418.4429 1170.33387 610.58867 915.70085
## ENSG00000000938.12 2616.00250 3697.9989 3110.00380 2690.00120 1897.00030
## sample6
## ENSG00000000003.14 5.80674
## ENSG00000000005.5 0.00000
## ENSG00000000419.12 1331.99780
## ENSG00000000457.13 542.45410
## ENSG00000000460.16 637.30845
## ENSG00000000938.12 1911.00020We 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. As of
tximport version 1.10, we have added a new
countsFromAbundance option "dtuScaledTPM".
This scaling option is designed for use with txOut=TRUE for
differential transcript usage analyses. See ?tximport for
details on the various countsFromAbundance options.
We can avoid gene-level summarization by setting
txOut=TRUE, giving the original transcript level estimates
as a list of matrices.
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.
## [1] TRUESalmon or Sailfish quant.sf files can be imported by
setting type to "salmon" or "sailfish".
files <- file.path(dir, "salmon", samples$run, "quant.sf.gz")
names(files) <- paste0("sample", 1:6)
txi.salmon <- tximport(files, type = "salmon", tx2gene = tx2gene)
head(txi.salmon$counts)## sample1 sample2 sample3 sample4 sample5
## ENSG00000000003.14 2.58012 2.0000 27.09648 8.48076 5.11217
## ENSG00000000005.5 0.00000 0.0000 0.00000 0.00000 0.00000
## ENSG00000000419.12 1056.99960 1337.9970 1452.99497 1289.00390 920.99960
## ENSG00000000457.13 462.88490 498.8622 560.75380 386.28665 531.57867
## ENSG00000000460.16 633.52972 418.4429 1170.33387 610.58867 915.70085
## ENSG00000000938.12 2616.00250 3697.9989 3110.00380 2690.00120 1897.00030
## sample6
## ENSG00000000003.14 5.80674
## ENSG00000000005.5 0.00000
## ENSG00000000419.12 1331.99780
## ENSG00000000457.13 542.45410
## ENSG00000000460.16 637.30845
## ENSG00000000938.12 1911.00020We quantified with Sailfish against a different transcriptome, so we
need to read in a different tx2gene for this next code
chunk.
tx2knownGene <- read_csv(file.path(dir, "tx2gene.csv"))
files <- file.path(dir, "sailfish", samples$run, "quant.sf")
names(files) <- paste0("sample", 1:6)
txi.sailfish <- tximport(files, type = "sailfish", tx2gene = tx2knownGene)
head(txi.sailfish$counts)## sample1 sample2 sample3 sample4 sample5 sample6
## A1BG 109.165000 316.13800 110.525000 116.00000 86.26030 76.75400
## A1BG-AS1 85.582100 141.15800 120.811000 153.49000 127.03400 105.32600
## A1CF 9.034322 10.02056 5.020884 13.02060 25.23152 25.07919
## A2M 24.000000 2.00000 21.000000 6.00000 38.00000 8.00000
## A2M-AS1 1.000000 1.00000 1.000000 1.00000 0.00000 0.00000
## A2ML1 3.046160 1.02936 4.074320 1.04871 3.07782 5.11940Note: for previous version of Salmon or Sailfish, in which
the quant.sf files start with comment lines, it is
recommended to specify the importer argument as a function
which reads in the lines beginning with the header. For example, using
the following code chunk (un-evaluated):
If inferential replicates (Gibbs or bootstrap samples) are present in
expected locations relative to the quant.sf file,
tximport will import these as well. Here we demonstrate using
Salmon, run with only 5 Gibbs replicates (usually more Gibbs samples,
such as 20 or 30, would be useful for capturing inferential
uncertainty).
files <- file.path(dir, "salmon_gibbs", samples$run, "quant.sf.gz")
names(files) <- paste0("sample", 1:6)
txi.inf.rep <- tximport(files, type = "salmon", txOut = TRUE)
names(txi.inf.rep)## [1] "abundance" "counts" "infReps"
## [4] "length" "countsFromAbundance"## [1] "sample1" "sample2" "sample3" "sample4" "sample5" "sample6"## [1] 178136 5The tximport arguments varReduce and
dropInfReps can be used to summarize the inferential
replicates into a single variance per transcript/gene and per sample, or
to not import inferential replicates, respectively.
kallisto abundance.h5 files can be imported by setting
type to "kallisto". Note that this requires that you have
the Bioconductor package rhdf5 installed. (Here
we only demonstrate reading in transcript-level information.)
files <- file.path(dir, "kallisto_boot", samples$run, "abundance.h5")
names(files) <- paste0("sample", 1:6)
txi.kallisto <- tximport(files, type = "kallisto", txOut = TRUE)
head(txi.kallisto$counts)## sample1 sample2 sample3 sample4 sample5 sample6
## ENST00000448914.1 0 0 0 0 0 0
## ENST00000631435.1 0 0 0 0 0 0
## ENST00000632684.1 0 0 0 0 0 0
## ENST00000434970.2 0 0 0 0 0 0
## ENST00000415118.1 0 0 0 0 0 0
## ENST00000633010.1 0 0 0 0 0 0Because the kallisto_boot directory also has inferential
replicate information, it was imported as well.
## [1] "abundance" "counts" "infReps"
## [4] "length" "countsFromAbundance"## [1] "sample1" "sample2" "sample3" "sample4" "sample5" "sample6"## [1] 178136 5kallisto abundance.tsv files can be imported as well,
but this is typically slower than the approach above. Note that we add
an additional argument in this code chunk,
ignoreAfterBar=TRUE. This is because the Gencode
transcripts have names like “ENST00000456328.2|ENSG00000223972.5|…”,
though our tx2gene table only includes the first “ENST”
identifier. We therefore want to split the incoming quantification
matrix rownames at the first bar “|”, and only use this as an
identifier. We didn’t use this option earlier with Salmon, because we
used the argument --gencode when running Salmon, which
itself does the splitting upstream of tximport. Note that
ignoreTxVersion and ignoreAfterBar are only to
facilitating the summarization to gene level.
files <- file.path(dir, "kallisto", samples$run, "abundance.tsv.gz")
names(files) <- paste0("sample", 1:6)
txi.kallisto.tsv <- tximport(files, type = "kallisto", tx2gene = tx2gene, ignoreAfterBar = TRUE)
head(txi.kallisto.tsv$counts)## sample1 sample2 sample3 sample4 sample5
## ENSG00000000003.14 2.59745 2.0000 27.15883 8.40623 5.06463
## ENSG00000000005.5 0.00000 0.0000 0.00000 0.00000 0.00000
## ENSG00000000419.12 1057.00040 1338.0006 1453.00134 1289.00080 921.00030
## ENSG00000000457.13 462.52870 495.4173 564.18460 385.98791 532.84843
## ENSG00000000460.16 630.39723 418.5453 1166.26643 611.51433 915.49327
## ENSG00000000938.12 2618.00130 3697.9998 3110.00650 2691.99670 1896.99980
## sample6
## ENSG00000000003.14 5.74125
## ENSG00000000005.5 0.00000
## ENSG00000000419.12 1332.00240
## ENSG00000000457.13 543.53370
## ENSG00000000460.16 636.25649
## ENSG00000000938.12 1909.99870RSEM sample.genes.results files can be imported by
setting type to "rsem", and txIn and
txOut to FALSE.
files <- file.path(dir, "rsem", samples$run, paste0(samples$run, ".genes.results.gz"))
names(files) <- paste0("sample", 1:6)
txi.rsem <- tximport(files, type = "rsem", txIn = FALSE, txOut = FALSE)
head(txi.rsem$counts)## sample1 sample2 sample3 sample4 sample5 sample6
## ENSG00000000003.14 0.00 2.00 21.00 3.00 0 1.00
## ENSG00000000005.5 0.00 0.00 0.00 0.00 0 0.00
## ENSG00000000419.12 1000.00 1250.00 1377.00 1197.00 0 1254.00
## ENSG00000000457.13 401.48 457.55 511.17 337.52 1 474.38
## ENSG00000000460.16 613.72 407.84 1119.94 556.23 2 603.25
## ENSG00000000938.12 2387.00 3466.00 2904.00 2431.00 3 1720.00RSEM sample.isoforms.results files can be imported by
setting type to "rsem", and txIn and
txOut to TRUE.
files <- file.path(dir, "rsem", samples$run, paste0(samples$run, ".isoforms.results.gz"))
names(files) <- paste0("sample", 1:6)
txi.rsem <- tximport(files, type = "rsem", txIn = TRUE, txOut = TRUE)
head(txi.rsem$counts)## sample1 sample2 sample3 sample4 sample5 sample6
## ENST00000373020.8 0 0 19.29 2.4 0 1
## ENST00000494424.1 0 0 0.00 0.0 0 0
## ENST00000496771.5 0 0 0.00 0.0 0 0
## ENST00000612152.4 0 0 1.71 0.6 0 0
## ENST00000614008.4 0 2 0.00 0.0 0 0
## ENST00000373031.4 0 0 0.00 0.0 0 0StringTie t_data.ctab files giving the coverage and
abundances for transcripts can be imported by setting type to
stringtie. These files can be generated with the following
command line call:
stringtie -eB -G transcripts.gff <source_file.bam> tximport will compute counts from the coverage information,
by reversing the formula that StringTie uses to calculate coverage (see
?tximport). The read length is used in this formula, and so
if you’ve set a different read length when using StringTie, you can
provide this information with the readLength argument. The
tx2gene table should connect transcripts to genes, and can
be pulled out of one of the t_data.ctab files. The tximport
call would look like the following (here not evaluated):
scRNA-seq data quantified with Alevin can be easily imported
using tximport. The following unevaluated example shows import
of the quants matrix (for a live example, see the unit test file
test_alevin.R). A single file should be specified which
will import a gene-by-cell matrix of data.
Note: there are two suggested ways of importing
estimates for use with differential gene expression (DGE) methods. The
first method, which we show below for edgeR and for
DESeq2, is to use the gene-level estimated counts from the
quantification tools, and additionally to use the transcript-level
abundance estimates to calculate a gene-level offset that corrects for
changes to the average transcript length across samples. The code
examples below accomplish these steps for you, keeping track of
appropriate matrices and calculating these offsets. For edgeR
you need to assign a matrix to y$offset, but the function
DESeqDataSetFromTximport takes care of creation of the offset
for you. Let’s call this method “original counts and
offset”.
The second method is to use the tximport argument
countsFromAbundance="lengthScaledTPM" or
"scaledTPM", and then to use the gene-level count matrix
txi$counts directly as you would a regular count matrix
with these software. Let’s call this method “bias corrected counts
without an offset”
Note: Do not manually pass the original gene-level
counts to downstream methods without an offset. The only case
where this would make sense is if there is no length bias to the counts,
as happens in 3’ tagged RNA-seq data (see section below). The original
gene-level counts are in txi$counts when
tximport was run with
countsFromAbundance="no". This is simply passing the summed
estimated transcript counts, and does not correct for potential
differential isoform usage (the offset), which is the point of the
tximport methods (Soneson, Love, and
Robinson 2015) for gene-level analysis. Passing uncorrected
gene-level counts without an offset is not recommended by the
tximport package authors. The two methods we provide here are:
“original counts and offset” or “bias corrected counts
without an offset”. Passing txi to
DESeqDataSetFromTximport as outlined below is correct: the
function creates the appropriate offset for you to perform gene-level
differential expression.
If you have 3’ tagged RNA-seq data, then correcting the counts for
gene length will induce a bias in your analysis, because the counts do
not have length bias. Instead of using the default
full-transcript-length pipeline, we recommend to use the original
counts, e.g. txi$counts as a counts matrix, e.g. providing
to DESeqDataSetFromMatrix or to the edgeR or
limma functions without calculating an offset and without using
countsFromAbundance.
An example of creating a DESeqDataSet for use with
DESeq2 (Love, Huber, and Anders
2014):
The user should make sure the rownames of sampleTable
align with the colnames of txi$counts, if there are
colnames. The best practice is to read sampleTable from a
CSV file, and to construct files from a column of
sampleTable, as was shown in the tximport examples
above.
An example of creating a DGEList for use with
edgeR (Robinson, McCarthy, and Smyth
2010) follows. Note that the alternate package, tximeta,
described above has a convenience function makeDGEList for
creating a data object for use with edgeR and
limma.
cts <- txi$counts
normMat <- txi$length
# Obtaining per-observation scaling factors for length, adjusted to avoid
# changing the magnitude of the counts.
normMat <- normMat/exp(rowMeans(log(normMat)))
normCts <- cts/normMat
# Computing effective library sizes from scaled counts, to account for
# composition biases between samples.
library(edgeR)
eff.lib <- calcNormFactors(normCts) * colSums(normCts)
# Combining effective library sizes with the length factors, and calculating
# offsets for a log-link GLM.
normMat <- sweep(normMat, 2, eff.lib, "*")
normMat <- log(normMat)
# Creating a DGEList object for use in edgeR.
y <- DGEList(cts)
y <- scaleOffset(y, normMat)
# filtering using the design information
design <- model.matrix(~condition, data = sampleTable)
keep <- filterByExpr(y, design)
y <- y[keep, ]
# y is now ready for estimate dispersion functions see edgeR User's GuideFor creating a matrix of CPMs within edgeR, the following code chunk can be used:
An example of creating a data object for use with limma-voom
(Law et al. 2014). Because limma-voom does
not use the offset matrix stored in y$offset, we recommend
using the scaled counts generated from abundances, either
"scaledTPM" or "lengthScaledTPM":
files <- file.path(dir, "salmon", samples$run, "quant.sf.gz")
names(files) <- paste0("sample", 1:6)
txi <- tximport(files, type = "salmon", tx2gene = tx2gene, countsFromAbundance = "lengthScaledTPM")
library(limma)
y <- DGEList(txi$counts)
# filtering using the design information:
design <- model.matrix(~condition, data = sampleTable)
keep <- filterByExpr(y, design)
y <- y[keep, ]
# normalize and run voom transformation
y <- calcNormFactors(y)
v <- voom(y, design)
# v is now ready for lmFit() see limma User's GuideThe development of tximport has benefited from contributions and suggestions from:
Rob Patro (inferential
replicates import), Andrew Parker Morgan
(RHDF5 support), Ryan C.
Thompson (RHDF5 support), Matt
Shirley (ignoreTxVersion), Avi
Srivastava (alevin import), Scott Van Buren (infReps
testing), Stephen
Turner, Richard
Smith-Unna, Rory
Kirchner, Martin Morgan,
Jenny Drnevich, Patrick Kimes,
Leon Fodoulian, Koen Van den Berge, Aaron Lun, Alexander Toenges
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
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## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] edgeR_4.5.1
## [2] limma_3.63.2
## [3] DESeq2_1.47.1
## [4] SummarizedExperiment_1.37.0
## [5] MatrixGenerics_1.19.0
## [6] matrixStats_1.4.1
## [7] tximport_1.35.0
## [8] readr_2.1.5
## [9] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [10] GenomicFeatures_1.59.1
## [11] AnnotationDbi_1.69.0
## [12] Biobase_2.67.0
## [13] GenomicRanges_1.59.1
## [14] GenomeInfoDb_1.43.2
## [15] IRanges_2.41.2
## [16] S4Vectors_0.45.2
## [17] BiocGenerics_0.53.3
## [18] generics_0.1.3
## [19] tximportData_1.34.0
## [20] knitr_1.49
## [21] rmarkdown_2.29
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## [4] bitops_1.0-9 fastmap_1.2.0 RCurl_1.98-1.16
## [7] GenomicAlignments_1.43.0 XML_3.99-0.17 digest_0.6.37
## [10] lifecycle_1.0.4 statmod_1.5.0 KEGGREST_1.47.0
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## [49] Matrix_1.7-1 jsonlite_1.8.9 hms_1.1.3
## [52] bit64_4.5.2 maketools_1.3.1 locfit_1.5-9.10
## [55] jquerylib_0.1.4 glue_1.8.0 codetools_0.2-20
## [58] gtable_0.3.6 BiocIO_1.17.1 UCSC.utils_1.3.0
## [61] munsell_0.5.1 tibble_3.2.1 pillar_1.9.0
## [64] rhdf5filters_1.19.0 htmltools_0.5.8.1 GenomeInfoDbData_1.2.13
## [67] R6_2.5.1 vroom_1.6.5 evaluate_1.0.1
## [70] lattice_0.22-6 png_0.1-8 Rsamtools_2.23.1
## [73] memoise_2.0.1 bslib_0.8.0 Rcpp_1.0.13-1
## [76] SparseArray_1.7.2 xfun_0.49 buildtools_1.0.0
## [79] pkgconfig_2.0.3