FARMS statistics

Tool stats & trends

Looking to identify usage trends or leading experts?


FARMS specifications


Unique identifier OMICS_01995
Alternative name Factor Analysis for Robust Microarray Summarization
Software type Package/Module
Interface Command line interface
Restrictions to use None
Biological technology Affymetrix
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU Lesser General Public License version 3.0
Computer skills Advanced
Stability Stable
Maintained Yes


No version available

Publication for Factor Analysis for Robust Microarray Summarization

FARMS citations


Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

Front Genet
PMCID: 5870427
PMID: 29616076
DOI: 10.3389/fgene.2018.00074

[…] The microarray data from three rat TGx systems was processing using Factor Analysis for Robust Microarray Summarization (FARMS) () with a custom CDF file from BRIANARRAY. The details were as described previously (; ). Replicate measurements were collapsed to one measu […]


Discovering Condition Specific Gene Co Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study

Sci Rep
PMCID: 5561081
PMID: 28819158
DOI: 10.1038/s41598-017-09094-4

[…] om the instrument into gene expression levels often attempt to correct for systematic bias. For microarrays, some methods include Robust Multichip Average (RMA), Affymetrix Microarray Suite MAS5, and Factor Analysis for Robust Microarray Summarization (FARMS) to name a few. For RNA-seq, some examples include Reads Per Kilobase per Million mapped reads (RPKM), Remove Unwanted Variation (RUV), and a […]


Network based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease

PMCID: 5404332
PMID: 28438154
DOI: 10.1186/s12931-017-0558-1

[…] s were used to perform quality control on the microarray data. Background correction, normalization and summarization of the data and filtering out non-informative probe sets was undertaken using the Factor Analysis for Robust Microarray Summarization (FARMS Bioconductor package) []. The gene expression data are available on the NCBI Gene Expression Omnibus (GEO) under […]


Transcriptomic responses of the liver and adipose tissues to altered carbohydrate fat ratio in diet: an isoenergetic study in young rats

PMCID: 5385083
PMID: 28405243
DOI: 10.1186/s12263-017-0558-2
call_split See protocol

[…] The CEL files derived from the liver, WAT, and BAT were quantified using robust multi-array average (RMA), factor analysis for robust microarray summarization (quantile normalization, qFARMS), and GCRMA, respectively [, , ], using the statistical language R (2.7.1) ( (R []), and B […]


Differentiating heart failure phenotypes using sex‐specific transcriptomic and proteomic biomarker panels

PMCID: 5542716
PMID: 28772032
DOI: 10.1002/ehf2.12136

[…] Transcriptomic and proteomic data were analysed using R and Bioconductor.For transcriptomic data, Factor Analysis for Robust Microarray Summarization was applied to filter out transcripts with inconsistent relative probe intensity levels and summarize probe intensities into transcript expression d […]


SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations

BMC Bioinformatics
PMCID: 5109843
PMID: 27842512
DOI: 10.1186/s12859-016-1319-8

[…] ss software [] (v1.1.0). All microarrays that passed quality control were background corrected and normalized using quantile normalization (as in RMA) [] and summarized using a factor analysis model (factor analysis for robust microarray summarization [FARMS]) [], via the ‘farms’ R package. FARMS includes an objective feature filtering technique that uses the multiple probes measuring the same tar […]


Looking to check out a full list of citations?

FARMS institution(s)
Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany

FARMS reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review FARMS