calc_prebs: Calculate PREBS values

In prebs: Probe region expression estimation for RNA-seq data for improved microarray comparability

Description

calc_prebs calculates PREBS values for given set of BAM files.

Usage

calc_prebs(bam_files, probe_mapping_file, cdf_name = NULL, cluster = NULL,
  output_eset = TRUE, paired_ended_reads = FALSE, ignore_strand = TRUE,
  sum.method = "rpa")

Arguments

bam_files	A vector containing .bam files.
probe_mapping_file	A file containing probe mappings in the genome.
cdf_name	A name of CDF package to use in RMA algorithm. If cdf_name=NULL, the package name is inferred from the name of probe_mapping_file ("HGU133Plus2_Hs_ENSG_mapping.txt" -> "hgu133plus2hsensgcdf")
cluster	A cluster object created using "makeCluster" function from "parellel" package. If cluster=NULL, no parallelization is used.
output_eset	If set to TRUE, the output of calc_prebs will be ExpressionSet object. Otherwise, the output will be a data frame.
paired_ended_reads	Set it to TRUE if your data contains paired-ended reads. Otherwise, the two read mates will be treated as independent units.
ignore_strand	If set to TRUE, then the strand is ignored while counting read overlaps with probe regions. If you use strand-specific RNA-seq protocol, set to FALSE, otherwise set it to TRUE.
sum.method	Microarray summarization method to be used. Can be either rpa or rma. The default mode is rpa.

Details

calc_prebs is the main function of prebs package that implements the whole pipeline. The function takes mapped reads in BAM format and probe sequence mappings as an input.

calc_prebs can run in two modes: rpa and rma. RMA is the classical microarray summarization algorithm developed by R. A. Irizarry et al. (2003), while RPA is a newer algorithm that was developed by L. Lahti et al. (2011). The default mode is rpa. NOTE: before prebs version 1.7.1 only RMA mode was available.

The output format depends on output_eset option. If output_eset=TRUE then calc_prebs returns ExpressionSet object (ExpressionSet object is defined in affy package). Otherwise, it returns a data frame containing PREBS values.

For running calc_prebs with custom CDF, the custom CDF package has to be downloaded and installed from Custom CDF website: http://brainarray.mbni.med.umich.edu/CustomCDF

For running calc_prebs with manufacturer's CDF, the manufacturer's CDF package can be installed from Bioconductor, for example: BiocManager::install("GenomicRanges"); BiocManager::install("hgu133plus2cdf")

For a detailed input specification, please refer to the prebs vignette.

Value

ExpressionSet object or a data frame containing PREBS values

Examples

if (require(prebsdata)) {
  # Get full paths to data files in \code{prebsdata} package
  bam_file1 <- system.file(file.path("sample_bam_files", "input1.bam"), package="prebsdata")
  bam_file2 <- system.file(file.path("sample_bam_files", "input2.bam"), package="prebsdata")
  bam_files <- c(bam_file1, bam_file2)
  custom_cdf_mapping1 <- system.file(file.path("custom-cdf", "HGU133Plus2_Hs_ENSG_mapping.txt"),
                                     package="prebsdata")
  custom_cdf_mapping2 <- system.file(file.path("custom-cdf", "HGU133A2_Hs_ENSG_mapping.txt"),
                                     package="prebsdata")
  manufacturer_cdf_mapping <- system.file(file.path("manufacturer-cdf", "HGU133Plus2_mapping.txt"),
                                          package="prebsdata")
  if (interactive()) {
    # Run PREBS using custom CDF without parallelization ("rpa" mode)
    prebs_values <- calc_prebs(bam_files, custom_cdf_mapping1)
    head(exprs(prebs_values))

    # Run PREBS using custom CDF without parallelization ("rma" mode)
    prebs_values <- calc_prebs(bam_files, custom_cdf_mapping1, sum.method="rma")
    head(exprs(prebs_values))

    # Run PREBS using custom CDF with parallelization
    library(parallel)
    N_CORES = 2
    CLUSTER <- makeCluster(N_CORES)
    prebs_values <- calc_prebs(bam_files, custom_cdf_mapping1, cluster=CLUSTER)
    stopCluster(CLUSTER)

    # Run PREBS using another custom CDF
    prebs_values <- calc_prebs(bam_files, custom_cdf_mapping2)

    # Run PREBS and return data frame instead of ExpressionSet object
    prebs_values <- calc_prebs(bam_files, custom_cdf_mapping1, output_eset=FALSE)
    head(prebs_values)
  }

  # Run PREBS using Manufacturer's CDF (outputs probe set expressions)
  prebs_values <- calc_prebs(bam_files, manufacturer_cdf_mapping)
  head(exprs(prebs_values))

  # Same as above, but state CDF package name explicitly
  prebs_values <- calc_prebs(bam_files, manufacturer_cdf_mapping, cdf_name="hgu133plus2cdf")
}

prebs documentation built on Nov. 8, 2020, 5:40 p.m.