tximport imports transcript-level estimates from various
external software and optionally summarizes abundances, counts,
and transcript lengths
to the gene-level (default) or outputs transcript-level matrices
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tximport( files, type = c("none", "salmon", "sailfish", "alevin", "kallisto", "rsem", "stringtie"), txIn = TRUE, txOut = FALSE, countsFromAbundance = c("no", "scaledTPM", "lengthScaledTPM", "dtuScaledTPM"), tx2gene = NULL, varReduce = FALSE, dropInfReps = FALSE, infRepStat = NULL, ignoreTxVersion = FALSE, ignoreAfterBar = FALSE, geneIdCol, txIdCol, abundanceCol, countsCol, lengthCol, importer = NULL, existenceOptional = FALSE, sparse = FALSE, sparseThreshold = 1, readLength = 75, alevinArgs = NULL )
a character vector of filenames for the transcript-level abundances
character, the type of software used to generate the abundances. Options are "salmon", "sailfish", "alevin", "kallisto", "rsem", "stringtie", or "none". This argument is used to autofill the arguments below (geneIdCol, etc.) "none" means that the user will specify these columns.
logical, whether the incoming files are transcript level (default TRUE)
logical, whether the function should just output transcript-level (default FALSE)
character, either "no" (default), "scaledTPM", "lengthScaledTPM", or "dtuScaledTPM". Whether to generate estimated counts using abundance estimates:
dtuScaledTPM is designed for DTU analysis in combination with
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 tximport
vignette describes how to construct this data.frame (see Details below).
An automated solution to avoid having to create
whether to reduce per-sample inferential replicates
information into a matrix of sample variances
whether to skip reading in inferential replicates
(default FALSE). For alevin,
a function to re-compute counts and abundances from the
inferential replicates, e.g.
logical, whether to split the tx id on the '.' character
to remove version information to facilitate matching with the tx id in
logical, whether to split the tx id on the '|' character
to facilitate matching with the tx id in
name of column with gene id. if missing, the
name of column with tx id
name of column with abundances (e.g. TPM or FPKM)
name of column with estimated counts
name of column with feature length information
a function used to read in the files
logical, should tximport not check if files exist before attempting
import (default FALSE, meaning files must exist according to
logical, whether to try to import data sparsely (default is FALSE).
Initial implementation for
the minimum threshold for including a count as a non-zero count during sparse import (default is 1)
numeric, the read length used to calculate counts from
StringTie's output of coverage. Default value (from StringTie) is 75.
The formula used to calculate counts is:
named list, with logical elements
tximport will also load in information about inferential replicates –
a list of matrices of the Gibbs samples from the posterior, or bootstrap replicates,
per sample – if these data are available in the expected locations relative
The inferential replicates, stored in
infReps in the output list,
are on estimated counts, and therefore follow
counts in the output list.
varReduce=TRUE, the inferential replicate matrices
will be replaced by a single matrix with the sample variance per transcript/gene
and per sample.
tximport summarizes to the gene-level by default,
the user can also perform the import and summarization steps manually,
txOut=TRUE and then using the function
Note however that this is equivalent to
txOut=FALSE (the default).
Solutions on summarization: regarding
"tximport failed at summarizing to the gene-level":
tx2gene data.frame linking transcripts to genes (more below)
avoid gene-level summarization by specifying
vignette('tximport') for example code for generating a
tx2gene data.frame from a
tx2gene data.frame should exactly match and be derived from
the same set of transcripts used for quantifying (the set of transcript
used to create the transcriptome index).
One automated solution for Salmon or alevin quantification data is to use the
tximeta function in the tximeta Bioconductor package
which builds upon and extends
tximport; this solution should
work out-of-the-box for human and mouse transcriptomes downloaded
from GENCODE, Ensembl, or RefSeq. For other cases, the user
should create the
tx2gene manually as shown in the tximport
On tx2gene construction:
Note that the
select functions used
to create the
tx2gene object are documented
in the man page for AnnotationDb-class objects
in the AnnotationDbi package (TxDb inherits from AnnotationDb).
For further details on generating TxDb objects from various inputs
vignette('GenomicFeatures') from the GenomicFeatures package.
alevinArgs argument includes some alevin-specific arguments.
This optional argument is a list with any or all of the following named logical variables:
The variables are described as follows (with default values in parens):
filterBarcodes (FALSE) import only cell barcodes listed in
tierImport (FALSE) import the tier information in addition to counts;
forceSlow (FALSE) force the use of the slower import R code
fishpond is installed.
type="alevin" all arguments other than
alevinArgs are ignored.
files should point to a single
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 first
fishpond package, which contains a C++ importer
for alevin's EDS format.
tximport is importing the gene-by-cell matrix of counts,
txi$counts, and effective lengths are not estimated.
txi$variance may also be imported if
inferential replicates were used, as well as inferential replicates
if these were output by alevin.
Length correction should not be applied to datasets where there
is not an expected correlation of counts and feature length.
A simple list containing matrices: abundance, counts, length.
Another list element 'countsFromAbundance' carries through
the character argument used in the tximport call.
The length matrix contains the average transcript length for each
gene which can be used as an offset for gene-level analysis.
If detected, and
txOut=TRUE, inferential replicates for
each 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 list
of bootstrap replicate matrices, where each matrix has
genes as rows and cells as columns.
varReduce=TRUE the inferential replicates will be summarized
according to the sample variance, and stored as a matrix
alevin already computes the variance of the bootstrap inferential replicates
and so this is imported without needing to specify
(note that alevin uses the 1/N variance estimator, so not the same as
Charlotte Soneson, Michael I. Love, Mark D. Robinson (2015) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. http://doi.org/10.12688/f1000research.7563
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# load data for demonstrating tximport # note that the vignette shows more examples # including how to read in files quickly using the readr package library(tximportData) dir <- system.file("extdata", package="tximportData") samples <- read.table(file.path(dir,"samples.txt"), header=TRUE) files <- file.path(dir,"salmon", samples$run, "quant.sf.gz") names(files) <- paste0("sample",1:6) # tx2gene links transcript IDs to gene IDs for summarization tx2gene <- read.csv(file.path(dir, "tx2gene.gencode.v27.csv")) txi <- tximport(files, type="salmon", tx2gene=tx2gene)
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