limma statistics

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Citations per year

Number of citations per year for the bioinformatics software tool limma

Tool usage distribution map

This map represents all the scientific publications referring to limma per scientific context
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Associated diseases

This word cloud represents limma usage per disease context

Popular tool citations

chevron_left Gene set enrichment analysis Normalization Differential expression detection Differential expression Differential expression detection Batch effect correction chevron_right
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limma specifications


Unique identifier OMICS_00769
Name limma
Alternative names Linear Models for Microarray Data, 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 3.36.5
Stability Stable
AnnotationDbi, methods, stats, gplots, graphics, Biobase, splines, utils, grDevices, locfit, affy, MASS, illuminaio, ellipse, R(>=2.3.0), BiasedUrn, GO.db,, statmod(>=1.2.2), vsn
Maintained Yes






No version available



  • person_outline Gordon Smyth

Publications for Linear Models for Microarray Data

limma citations


Estrogen related receptor gamma functions as a tumor suppressor in gastric cancer

Nat Commun
PMCID: 5954140
PMID: 29765046
DOI: 10.1038/s41467-018-04244-2

[…] n accordance with the manufacturer’s protocols. After the bead chips were scanned with an Illumina BeadArray Reader, the microarray data were normalized using the quantile normalization method in the Linear Models for Microarray Data package in the R language environment. The expression level of each gene was then log2 transformed before further analysis. The microarray data are available from the […]


Aurora kinase A (AURKA) interaction with Wnt and Ras MAPK signalling pathways in colorectal cancer

Sci Rep
PMCID: 5951826
PMID: 29760449
DOI: 10.1038/s41598-018-24982-z

[…] Two biological replicates were performed for each siRNA experiment (both AURKA and non-targeting). Pre-processing and differential expression analysis (DEA) was done using the R-Bioconductor packages Limma and SVA-combat. In short, conventional background correction was applied, followed by within-array normalization using Loess. Subsequently, batch-effect removal was applied for the replicate exp […]


Inhibition of 2 AG hydrolysis differentially regulates blood brain barrier permeability after injury

PMCID: 5952841
PMID: 29759062
DOI: 10.1186/s12974-018-1166-9

[…] trimmed mean of the M-values method [] using the calcNormFactors() function from the edgeR package []. The mean-variance relationship of the counts was estimated using the voom() function [] from the limma package []. To identify differentially expressed genes, the log2 fold differences and p values were estimated by fitting a linear model for each gene and applying empirical Bayes to moderate res […]


Expressional analysis of disease relevant signalling pathways in primary tumours and metastasis of head and neck cancers

Sci Rep
PMCID: 5943339
PMID: 29743718
DOI: 10.1038/s41598-018-25512-7

[…] Analysis of the data was performed using R 2.15.2 with the limma package 3.14.4. Raw fluorescence intensities from all hybridizations were normalized, applying robust multichip average (RMA) normalization for the. CEL data, followed by a quantile normalizatio […]


Leukaemic alterations of IKZF1 prime stemness and malignancy programs in human lymphocytes

PMCID: 5943605
PMID: 29743561
DOI: 10.1038/s41419-018-0600-3

[…] Differential expression analysis was performed using R_3.3 and Bioconductor_3.4. RNA-seq data were analysed using DESeq2_1.18.0 package. Microarray data were analysed using limma_3.34.0 package. A model formula of ~B + A was used for performing the analysis, . The results of the effect of factor A can be adjusted by the condition of B. A stands for the major factor (IK6 […]


Transcriptomic integration of D4R and MOR signaling in the rat caudate putamen

Sci Rep
PMCID: 5943359
PMID: 29743514
DOI: 10.1038/s41598-018-25604-4

[…] The differential expression analysis was done using the limma package in R. The multiple linear regressions were performed by fitting the following model:1Y=Y0+β1X1+β2X2+β3X3+β4X1X2+εwhere y is the normalized gene expression in log2 scale, y0 is the averag […]

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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

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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.