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tximport imports transcript-level estimates from variousexternal software and optionally summarizes abundances, counts,and transcript lengthsto the gene-level (default) or outputs transcript-level matrices(see txOut argument).

a two-column data.frame linking transcript id (column 1)to gene id (column 2).the column names are not relevant, but this column order must be used. this argument is required for gene-level summarization, and the tximportvignette describes how to construct this data.frame (see Details below).An automated solution to avoid having to create tx2gene ifone has quantified with Salmon or alevin with human or mouse transcriptomesis to use the tximeta function from the tximeta Bioconductor package.

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a function to re-compute counts and abundances from theinferential replicates, e.g. matrixStats::rowMedians to re-compute counts as the median of the inferential replicates. The order of operations is:first counts are re-computed, then abundances are re-computed.Following this, if countsFromAbundance is not "no",tximport will again re-compute counts from the re-computed abundances.infRepStat should operate on rows of a matrix. (default is NULL)

summarizeToGene:While tximport summarizes to the gene-level by default, the user can also perform the import and summarization steps manually,by specifing txOut=TRUE and then using the function summarizeToGene.Note however that this is equivalent to tximport withtxOut=FALSE (the default).

See vignette('tximport') for example code for generating atx2gene data.frame from a TxDb object.The tx2gene data.frame should exactly match and be derived fromthe same set of transcripts used for quantifying (the set of transcriptused to create the transcriptome index).

Tximeta:One automated solution for Salmon or alevin quantification data is to use thetximeta function in the tximeta Bioconductor packagewhich builds upon and extends tximport; this solution shouldwork out-of-the-box for human and mouse transcriptomes downloadedfrom GENCODE, Ensembl, or RefSeq. For other cases, the usershould create the tx2gene manually as shown in the tximportvignette.

alevin:The alevinArgs argument includes some alevin-specific arguments.This optional argument is a list with any or all of the following named logical variables:filterBarcodes, tierImport, and forceSlow.The variables are described as follows (with default values in parens):filterBarcodes (FALSE) import only cell barcodes listed inwhitelist.txt;tierImport (FALSE) import the tier information in addition to counts;forceSlow (FALSE) force the use of the slower import R codeeven if fishpond is installed;dropMeanVar (FALSE) don't import inferential mean and variancematrices even if they exist (also skips inferential replicates)For type="alevin" all arguments other than files,dropInfReps, and alevinArgs are ignored.Note that files should point to a single quants_mat.gz file,in the directory structure created by the alevin software(e.g. do not move the file or delete the other important files).Note that importing alevin quantifications will be much faster by firstinstalling the fishpond package, which contains a C++ importerfor alevin's EDS format.For alevin, tximport is importing the gene-by-cell matrix of counts,as txi$counts, and effective lengths are not estimated.txi$mean and txi$variance may also be imported ifinferential replicates were used, as well as inferential replicatesif these were output by alevin.Length correction should not be applied to datasets where thereis not an expected correlation of counts and feature length.

A simple list containing matrices: abundance, counts, length.Another list element 'countsFromAbundance' carries throughthe character argument used in the tximport call.The length matrix contains the average transcript length for eachgene which can be used as an offset for gene-level analysis.If detected, and txOut=TRUE, inferential replicates foreach sample will be imported and stored as a list of matrices,itself an element infReps in the returned list.An exception is alevin, in which the infReps are a listof bootstrap replicate matrices, where each matrix hasgenes as rows and cells as columns.If varReduce=TRUE the inferential replicates will be summarizedaccording to the sample variance, and stored as a matrix variance.alevin already computes the variance of the bootstrap inferential replicatesand so this is imported without needing to specify varReduce=TRUE.

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):

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).

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:

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.

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").

If inferential replicates (Gibbs or bootstrap samples) are present in expected locations relative to the quant.sf file, tximport will import these as well, if txOut=TRUE. tximport will not summarize inferential replicate information to the gene-level. Here we demonstrate using Salmon, run with only 5 Gibbs replicates (usually more Gibbs samples would be useful for estimating variability).

The tximport arguments varReduce and dropInfReps can be used to summarize the inferential replicates into a single variance per transcript and per sample, or to not import inferential replicates, respectively.

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.

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.

However, I do have a question/idea. Are you working on a local copy of Dataiku say installed on a Macintosh computer rather than say a production Linux Server? If so, are you able to install Bioconductor tximport on your computer outside of Dataiku DSS in an R environment directly or through say RStudio?

High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Various quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that the presence of differential isoform usage can lead to inflated false discovery rates in differential gene expression analyses on simple count matrices but that this can be addressed by incorporating offsets derived from transcript-level abundance estimates. We also show that the problem is relatively minor in several real data sets. Finally, we provide an R package ( tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.

All samples have one of two technologies, they can either be Microarray or Rna-Seq samples. To process the Rna-seq samples, we first apply Salmon and then tximport to process all the samples. This is explained in detail in the refinebio docs.

The first theory we had is that some samples could be associated with multiple experiments,and this somehow could be causing the bug because tximport could be picking up the wrong files.This is not the first data integrity issue that we have had. 006ab0faaa