Author: Michael I. Love, Charlotte Soneson, Mark D. Robinson

# Importing transcript abundance with tximport

## Introduction

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 et al. 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.

## 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 salmon software, and later show the use of tximport with any of:

- salmon (Patro et al. 2017)
- alevin (Srivastava et al. 2019)
- oarfish (Jousheghani and Patro 2024)
- sailfish (Patro et al. 2014)
- kallisto (Bray et al. 2016)
- RSEM (Li and Dewey 2011)
- StringTie (Pertea et al. 2015)

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] "alevin"                  "cufflinks"
    ##  [3] "gencode"                 "kallisto"
    ##  [5] "kallisto_boot"           "oarfish"
    ##  [7] "refseq"                  "rsem"
    ##  [9] "sailfish"                "salmon"
    ## [11] "salmon_dm"               "salmon_ec"
    ## [13] "salmon_gibbs"            "samples_extended.txt"
    ## [15] "samples.txt"             "tx2gene_alevin.tsv"
    ## [17] "tx2gene.csv"             "tx2gene.ensembl.v87.csv"
    ## [19] "tx2gene.gencode.v27.csv"

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.)

    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, "salmon", samples$run, "quant.sf.gz")
    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. 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:

    library(readr)
    tx2gene <- read_csv(file.path(dir, "tx2gene.gencode.v27.csv"))
    head(tx2gene)

    ## # 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 ENSG00000268020.3

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. 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.

    library(tximport)
    txi <- tximport(files, type = "salmon", tx2gene = tx2gene)
    names(txi)

    ## [1] "abundance"           "counts"              "length"
    ## [4] "countsFromAbundance"

    head(txi$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 1911.00020

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. 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.

    txi.tx <- tximport(files, type = "salmon", txOut = TRUE)

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

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

    files <- file.path(dir, "salmon", samples$run, "quant.sf.gz")
    names(files) <- paste0("sample", 1:6)
    txi.salmon <- tximport(files, type = "salmon", tx2gene = tx2gene)
    head(txi.salmon$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 1911.00020

We 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   5.11940

Note: 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):

    txi <- tximport("quant.sf", type = "none", txOut = TRUE, txIdCol = "Name", abundanceCol = "TPM",
        countsCol = "NumReads", lengthCol = "Length", importer = function(x) read_tsv(x,
            skip = 8))

## salmon with inferential replicates

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"

    names(txi.inf.rep$infReps)

    ## [1] "sample1" "sample2" "sample3" "sample4" "sample5" "sample6"

    dim(txi.inf.rep$infReps$sample1)

    ## [1] 178136      5

The 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

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       0

## kallisto with inferential replicates

Because the `kallisto_boot` directory also has inferential replicate information, it was imported as well.

    names(txi.kallisto)

    ## [1] "abundance"           "counts"              "infReps"
    ## [4] "length"              "countsFromAbundance"

    names(txi.kallisto$infReps)

    ## [1] "sample1" "sample2" "sample3" "sample4" "sample5" "sample6"

    dim(txi.kallisto$infReps$sample1)

    ## [1] 178136      5

## kallisto with TSV files

kallisto `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 1909.99870

## RSEM

RSEM `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 1720.00

RSEM `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       0

## StringTie

StringTie `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):

    tmp <- read_tsv(files[1])
    tx2gene <- tmp[, c("t_name", "gene_name")]
    txi <- tximport(files, type = "stringtie", tx2gene = tx2gene)

## alevin

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.

    files <- "path/to/alevin/quants_mat.gz"
    txi <- tximport(files, type = "alevin")

## oarfish

Long read data quantified with oarfish can be imported using tximport. The following example shows import of three samples from SG-Nex. See vignette of tximportData for more details. Note this quantification includes ~8k additional transcripts of length 1,000 bp drawn from chr1-22.

    sgnex_file <- "sgnex_h9"
    files <- file.path(dir, "oarfish", paste0(sgnex_file, "_rep", 2:4, ".quant.gz"))
    names(files) <- paste0("sgnex-rep", 2:4)
    txi.oar <- tximport(files, type = "oarfish", txOut = TRUE)

## Downstream DGE in Bioconductor

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, limma 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. The functions `edgeR::DGEListFromTximport`, for edgeR and limma, and `DESeq2::DESeqDataSetFromTximport`, for DESeq2, take 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 may happen in 3’ tagged RNA-seq data (see section below), or if there is little to no fragmentation before sequencing. The original gene-level counts are in `txi$counts` when `tximport` was run with `countsFromAbundance="no"`. Using only `txi$counts` 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 et al. 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 `DESeq2::DESeqDataSetFromTximport` or `edgeR::DGEListFromTximport` as outlined below is correct: the functions create the appropriate offset for you to perform gene-level differential expression.

## 3’ tagged RNA-seq

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.

## DESeq2

An example of creating a `DESeqDataSet` for use with DESeq2 (Love et al. 2014):

    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

## edgeR

The following code will create a `DGEList` object containing gene-level counts for analysis with the edgeR package (Chen et al. 2025):

    library(edgeR)
    dge <- DGEListFromTximport(txi)

The `DGEListFromTximport` function is available in edgeR as of version 4.10.0 (Bioconductor release 3.23, Spring 2026).

The object `dge` is ready for any of the edgeR analysis pipelines, starting usually with `filterByExpr` and `normLibSizes` followed by `glmQLFit`. See the edgeR User’s Guide.

If the `tximport` output contains inferential replicates, then `DGEListFromTximport` will also estimate the count overdispersion that arises from the fact that reads have to assigned to transcripts probabilistically (Baldoni et al. 2024). The following code will compute “divided counts” (Baldoni et al. 2024) suitable for differential expression analysis at the transcript level:

    library(edgeR)
    dge.tx <- DGEListFromTximport(txi.tx, divide = TRUE)

See the edgeR User’s Guide for more details.

## limma-voom

The DGEList objects created by `DGEListFromTximport` are also suitable for analysis with limma-voom. In the limma-voom pipeline, `dge` is preprocessed by `filterByExpr` and `normLibSizes`, as for edgeR, but then `glmQLFit` is replaced by either `voom` and `lmFit` (Law et al. 2014) or by the newer function `voomLmFit` (Baldoni et al. 2025). See the limma User’s Guide for more details.

## Acknowledgments

The 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), Gordon Smyth and Pedro Baldoni (edgeR and limma functions and docs), Stephen Turner, Richard Smith-Unna, Rory Kirchner, Martin Morgan, Jenny Drnevich, Patrick Kimes, Leon Fodoulian, Koen Van den Berge, Aaron Lun, Alexander Toenges

## Session info

    sessionInfo()

    ## R version 4.6.0 (2026-04-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## 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
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [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
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] edgeR_4.10.0
    ##  [2] limma_3.68.0
    ##  [3] DESeq2_1.52.0
    ##  [4] SummarizedExperiment_1.42.0
    ##  [5] MatrixGenerics_1.24.0
    ##  [6] matrixStats_1.5.0
    ##  [7] tximport_1.40.0
    ##  [8] readr_2.2.0
    ##  [9] TxDb.Hsapiens.UCSC.hg19.knownGene_3.22.1
    ## [10] GenomicFeatures_1.64.0
    ## [11] AnnotationDbi_1.74.0
    ## [12] Biobase_2.72.0
    ## [13] GenomicRanges_1.64.0
    ## [14] Seqinfo_1.2.0
    ## [15] IRanges_2.46.0
    ## [16] S4Vectors_0.50.0
    ## [17] BiocGenerics_0.58.0
    ## [18] generics_0.1.4
    ## [19] tximportData_1.38.0
    ## [20] knitr_1.51
    ## [21] rmarkdown_2.31
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tidyselect_1.2.1         farver_2.1.2             blob_1.3.0
    ##  [4] S7_0.2.2                 Biostrings_2.80.0        bitops_1.0-9
    ##  [7] fastmap_1.2.0            RCurl_1.98-1.18          GenomicAlignments_1.48.0
    ## [10] XML_3.99-0.23            digest_0.6.39            lifecycle_1.0.5
    ## [13] statmod_1.5.1            KEGGREST_1.52.0          RSQLite_2.4.6
    ## [16] magrittr_2.0.5           compiler_4.6.0           rlang_1.2.0
    ## [19] sass_0.4.10              tools_4.6.0              utf8_1.2.6
    ## [22] yaml_2.3.12              rtracklayer_1.72.0       S4Arrays_1.12.0
    ## [25] bit_4.6.0                curl_7.1.0               DelayedArray_0.38.0
    ## [28] RColorBrewer_1.1-3       abind_1.4-8              BiocParallel_1.46.0
    ## [31] sys_3.4.3                grid_4.6.0               Rhdf5lib_1.32.0
    ## [34] ggplot2_4.0.3            scales_1.4.0             cli_3.6.6
    ## [37] crayon_1.5.3             httr_1.4.8               tzdb_0.5.0
    ## [40] rjson_0.2.23             DBI_1.3.0                cachem_1.1.0
    ## [43] rhdf5_2.54.1             parallel_4.6.0           formatR_1.14
    ## [46] XVector_0.52.0           restfulr_0.0.16          vctrs_0.7.3
    ## [49] Matrix_1.7-5             jsonlite_2.0.0           hms_1.1.4
    ## [52] bit64_4.8.0              maketools_1.3.2          locfit_1.5-9.12
    ## [55] jquerylib_0.1.4          glue_1.8.1               codetools_0.2-20
    ## [58] gtable_0.3.6             BiocIO_1.22.0            tibble_3.3.1
    ## [61] pillar_1.11.1            htmltools_0.5.9          rhdf5filters_1.22.0
    ## [64] R6_2.6.1                 vroom_1.7.1              evaluate_1.0.5
    ## [67] lattice_0.22-9           png_0.1-9                Rsamtools_2.28.0
    ## [70] cigarillo_1.2.0          memoise_2.0.1            bslib_0.10.0
    ## [73] Rcpp_1.1.1-1.1           SparseArray_1.12.0       xfun_0.57
    ## [76] buildtools_1.0.0         pkgconfig_2.0.3

## References

Baldoni, Pedro L., Lizhong Chen, Mengbo Li, Yunshun Chen, and Gordon K. Smyth. 2025. “Dividing Out Quantification Uncertainty Enables Assessment of Differential Transcript Usage with limma and edgeR.” Nucleic Acids Research 53 (22): gkaf1305. https://doi.org/10.1093/nar/gkaf1305.
Baldoni, Pedro L., Yunshun Chen, Soroor Hediyeh-zadeh, et al. 2024. “Dividing Out Quantification Uncertainty Allows Efficient Assessment of Differential Transcript Expression with edgeR.” Nucleic Acids Research 52 (3): e13. https://doi.org/10.1093/nar/gkad1167.
Bray, Nicolas, Harold Pimentel, Pall Melsted, and Lior Pachter. 2016. “Near-Optimal Probabilistic RNA-Seq Quantification.” Nature Biotechnology 34: 525--527. http://dx.doi.org/10.1038/nbt.3519.
Chen, Yunshun, Lizhong Chen, Aaron T L Lun, Pedro L Baldoni, and Gordon K Smyth. 2025. “edgeR V4: Powerful Differential Analysis of Sequencing Data with Expanded Functionality and Improved Support for Small Counts and Larger Datasets.” Nucleic Acids Research 53 (2): gkaf018. https://doi.org/10.1093/nar/gkaf018.
Jousheghani, Zahra Zare, and Rob Patro. 2024. “Oarfish: Enhanced Probabilistic Modeling Leads to Improved Accuracy in Long Read Transcriptome Quantification.” bioRxiv, ahead of print. https://doi.org/10.1101/2024.02.28.582591.
Law, Charity W., Yunshun Chen, Wei Shi, and Gordon K. Smyth. 2014. “voom: precision weights unlock linear model analysis tools for RNA-seq read counts.” Genome Biology 15: 29. https://doi.org/10.1186/gb-2014-15-2-r29.
Li, Bo, and Colin N. Dewey. 2011. “RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.” BMC Bioinformatics 12: 323+. https://doi.org/10.1186/1471-2105-12-3231.
Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology 15: 550. http://dx.doi.org/10.1186/s13059-014-0550-8.
Love, Michael I., Charlotte Soneson, Peter F. Hickey, et al. 2020. “Tximeta: Reference sequence checksums for provenance identification in RNA-seq.” PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1007664.
Patro, Rob, Geet Duggal, Michael I. Love, Rafael A. Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” Nature Methods. http://dx.doi.org/10.1038/nmeth.4197.
Patro, Rob, Stephen M. Mount, and Carl Kingsford. 2014. “Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms.” Nature Biotechnology 32: 462–64. http://dx.doi.org/10.1038/nbt.2862.
Pertea, Mihaela, Geo M Pertea, Corina M Antonescu, Tsung-Cheng Chang, Joshua T Mendell, and Steven L Salzberg. 2015. “StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.” Nature Biotechnology 33: 290–95. https://dx.doi.org/10.1038%2Fnbt.3122.
Robert, Christelle, and Mick Watson. 2015. “Errors in RNA-Seq quantification affect genes of relevance to human disease.” Genome Biology, ahead of print. https://doi.org/10.1186/s13059-015-0734-x.
Soneson, Charlotte, Michael I. Love, and Mark Robinson. 2015. “Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.” F1000Research 4. http://dx.doi.org/10.12688/f1000research.7563.1.
Srivastava, Avi, Laraib Malik, Tom Sean Smith, Ian Sudbery, and Rob Patro. 2019. “Alevin efficiently estimates accurate gene abundances from dscRNA-seq data.” Genome Biology 20 (65). https://doi.org/10.1186/s13059-019-1670-y.
Trapnell, Cole, David G Hendrickson, Martin Sauvageau, Loyal Goff, John L Rinn, and Lior Pachter. 2013. “Differential analysis of gene regulation at transcript resolution with RNA-seq.” Nature Biotechnology, ahead of print. https://doi.org/10.1038/nbt.2450.