| readTxResults {scater} | R Documentation |
Create a SingleCellExperiment object from pseudo-aligner results via tximport.
readTxResults(..., full_length = TRUE) readKallistoResults(..., full_length = TRUE) readSalmonResults(..., full_length = TRUE)
... |
Arguments to be passed to |
full_length |
Logical scalar indicating whether the sequencing data is full-length (e.g., Smart-seq2) or end-biased, e.g., UMI-based protocols. |
If full_length=TRUE, counts are computed from the length-scaled TPMs.
Otherwise, counts are not computed from the abundances.
readKallistoResults and readSalmonResults are simply wrappers around readTxResults with type pre-specified.
This function has now been deprecated in favour of tximeta.
The latter produces a SummarizedExperiment that is easily coerced into a SingleCellExperiment.
A SingleCellExperiment containing the abundance, count and feature length information from the supplied samples.
Davis McCarthy and Aaron Lun
Soneson C, Love MI, Robinson MD (2015). Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521.
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"))
sce <- readTxResults(files, type="salmon", tx2gene=tx2gene)