: Built-in methods for data quality control, including image() for spatial artifacts, hist() for intensity distributions, and MAplot() for log-ratio assessment.
The package is designed to handle large datasets efficiently, supporting parallel execution for intensive tasks. Its core offerings include:
For users on Linux environments or those who prefer package managers, is also available via Bioconda . You can install it using: conda install bioconductor-oligo Key Features and Capabilities oligo package r download
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") Use code with caution. : BiocManager::install("oligo") Use code with caution. Verify Installation : library(oligo) Use code with caution.
# Load raw CEL files from your working directory celFiles <- list.celfiles() rawData <- read.celfiles(celFiles) # Perform RMA preprocessing eset <- rma(rawData) Use code with caution. : Built-in methods for data quality control, including
: Implements widely used routines such as RMA (Robust Multi-array Average) for background correction, quantile normalization, and summarization.
For exon and gene arrays, you can further customize summarization to the "core," "extended," or "full" levels by adjusting the target argument in the rma() function. You can install it using: conda install bioconductor-oligo
: Active development and bug reporting take place on the oligo GitHub repository . oligo - Bioconductor