R: tximport包使用注意事项

Import transcript-level abundances and counts for transcript- and gene-level analysis packages

Description

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 (see txOut argument).

Usage

tximport(
  files,
  type = c("none", "salmon", "sailfish", "alevin", "piscem", "oarfish", "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
)

Arguments

Details

Inferential replicates: 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 to the files. The inferential replicates, stored in infReps in the output list, are on estimated counts, and therefore follow counts in the output list. By setting varReduce=TRUE, the inferential replicate matrices will be replaced by a single matrix with the sample variance per transcript/gene and per sample.

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 with txOut=FALSE (the default).

Solutions on summarization: regarding "tximport failed at summarizing to the gene-level":

1. provide a tx2gene data.frame linking transcripts to genes (more below)
2. avoid gene-level summarization by specifying txOut=TRUE

See vignette('tximport') for example code for generating a tx2gene data.frame from a TxDb object. The 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).

Tximeta: One automated solution for Salmon/alevin/piscem/oarfish 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 vignette.

On tx2gene construction: Note that the keys and 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 see vignette('GenomicFeatures') from the GenomicFeatures package.

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 in whitelist.txt; tierImport (FALSE) import the tier information in addition to counts; forceSlow (FALSE) force the use of the slower import R code even if eds is installed; dropMeanVar (FALSE) don’t import inferential mean and variance matrices 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 first installing the eds package, which contains a C++ importer for 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 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.

Value

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. If varReduce=TRUE the inferential replicates will be summarized according to the sample variance, and stored as a matrix variance. alevin already computes the variance of the bootstrap inferential replicates and so this is imported without needing to specify varReduce=TRUE.

References

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

Examples

Run examples

# 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)