limma specifications


Unique identifier OMICS_00769
Name Linear Models for Microarray Data
Alternative name r-bioc-limma
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 3.24.15
Stability Stable
Requirements methods
Maintained Yes



Add your version



  • person_outline Gordon Smyth <>

limma article

limma citations

PMCID: 5891938

[…] channel: green; scan resolution = 3 μm; pmt: 100%, 20 bits). data were extracted with feature extraction software 10.7 (agilent technologies). raw data were normalized by the quantile algorithm of limma package in r. the raw data is available and deposited in public database (geo). the geo accession number is gse102897., hierarchical clustering was performed in mev_4_9_0 to identify […]

PMCID: 5880952

[…] accession gse 103191)., background correction, averaging of the expression values of duplicated probes, and between-sample normalization of raw data were performed using limma (limma, 2005) and r 3.2.1 ( normalization was performed using the quantile method. the expression value for each gene was calculated by averaging the expression […]

PMCID: 5955442

[…] normalization and summarization were performed on the .cel files by robust multiarray averaging (rma) [46–48]. from this preprocessed data, differentially expressed genes were identified using the limma (linear models for microarray data) package in r language [49]. genes of low median intensity (< 10th percentile) were filtered and adjusted p-values were calculated using the benjamini […]

PMCID: 5825098

[…] agilent technologies, waldbronn, germany) based on the “dynamic range expander” option in the scanner software. images were processed by agilent feature extraction software version 9.5., the limma (linear models for microarray data) package (version 3.20.1) [32] and r version 3.0.3 were used for statistical analysis and the identification of significant differentially expressed genes. single […]

PMCID: 5818071

[…] was used for further analyses. the mean expression value for each gene was calculated, and the data were log2-transformed. data were processed and normalized by quantile normalization using the limma package in r (smyth, 2005). the de genes were identified using the significance analysis of microarrays algorithm implemented in tmev (saeed et al., 2003), with a false discovery rate of 10% […]

limma institution(s)
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
limma funding source(s)
National Health and Medical Research Council Project Grant [1050661 and 1023454]; NHMRC Program Grant [1054618]; Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS

limma review

star_border star_border star_border star_border star_border
star star star star star


star_border star_border star_border star_border star_border
star star star star star
limma is a tool for reading, pre-processing, manipulate, quality control and analyse microarray data from a variety of different technologies. In my opinion, it could easily be considered the state-of-the-art tool for analysis of these kind of data. Linear modelling extended by limma provides a powerful way to analyse simple or complex experiments, allowing full control over covariates and time-series designs. Also, the team behind the tool made great effort in creating a tool easily accessible to non bio-statisticians, which is a plus in a lot of ways. Besides, I should also point out that limma has the capability to analyse RNA-seq experiments as well. Finally, users should definitely check the comprehensive (and well-written) limma User Guide, a document that will provide all the basics to get things rolling with the package.

Related Tools