Import transcript isoform quantification into EZbakRData object

A convenient wrapper to tximport() for importing isoform quantification data into an EZbakRData object. You need to run this before running EstimateIsoformFractions.

Usage

ImportIsoformQuant(
  obj,
  files,
  quant_tool = c("none", "salmon", "sailfish", "alevin", "piscem", "kallisto", "rsem",
    "stringtie"),
  txIn = TRUE,
  ...
)

Arguments

obj

An EZbakRData object.

files

A named vector of paths to all transcript quantification files that you would like to import. This will be passed as the first argument of tximport::tximport() (also named files). The names of this vector should be the same as the sample names as they appear in the metadf of the EZbakRData object.

quant_tool

String denoting the type of software used to generate the abundances. Will get passed to the type argument of tximport::tximport(). As described in the documentation for tximport 'Options are "salmon", "sailfish", "alevin", "piscem", "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. Be aware that specifying type other than "none" will ignore the arguments below (geneIdCol, etc.)'. Referenced 'arguments below' can be specified as part of ....

txIn

Whether or now you are providing isoform level quantification files. Alternative (txIn = FALSE) is gene-level quantification. In ImportIsoformQuant, txIn gets passed to BOTH the txIn and txOut parameters in tximport().

...

Additional arguments to be passed to tximport::tximport(). Especially relevant if you set quant_tool to "none".

Value

An EZbakRData object with an additional element in the readcounts list named "isform_quant_<quant_tool>". It contains TPM, expected_count, and effective length information for each transcript_id and each sample.

Examples

# Dependencies for example
library(dplyr)
library(data.table)
#> 
#> Attaching package: ‘data.table’
#> The following objects are masked from ‘package:dplyr’:
#> 
#>     between, first, last

# Simulate and analyze data
simdata <- EZSimulate(30)
ezbdo <- EZbakRData(simdata$cB, simdata$metadf)
ezbdo <- EstimateFractions(ezbdo)
#> Estimating mutation rates
#> Summarizing data for feature(s) of interest
#> Averaging out the nucleotide counts for improved efficiency
#> Estimating fractions
#> Processing output

# Hack to generate example quantification files
savedir <- tempdir()
rsem_data <- tibble(
  transcript_id = paste0("tscript_feature", 1:30),
  gene_id = paste0("feature", 1:30),
  length = 1000,
  effective_length = 1000,
  expected_count = 1000,
  TPM = 10,
  FPKM = 10,
  IsoPct = 1
)

fwrite(rsem_data, file.path(savedir, "Sample_1.isoforms.results"), sep = '\t')

files <- file.path(savedir,"Sample_1.isoforms.results")
names(files) <- "Sample_1"

# Read in file
ezbdo <- ImportIsoformQuant(ezbdo, files, quant_tool = "rsem")
#> reading in files with read.delim (install 'readr' package for speed up)
#> 1 
#>