Key features
- Designed for eQTL analysis of large datasets.
- Performs testing for all or only local transcript-SNP pair.s
- Ultra-fast, no loss of precision.
- Equally fast for models with covariates.
- Supports
- Linear additive and ANOVA models. Supports testing for the effect of genotype-covariate interaction.
- Covariates to account for sex, population structure, surrogate variables, etc.
- Correlated and heteroskedastic errors.
- Correction for multiple testing using FDR
 .
- Separate p-value thresholds and FDR control for local and distant eQTLs (more info).
- Convenient R package at CRAN Repository .
Performance comparison:
| Method |
No covar. |
10 covar. |
|
| Matrix eQTL, Matlab |
11.8 |
11.8 |
minutes |
| Matrix eQTL, Rev R |
14.6 |
14.6 |
minutes |
| Matrix eQTL, R+GOTO |
19.4 |
19.4 |
minutes |
| Plink |
9.4 |
583.3 |
days |
| Merlin |
19.6 |
20.0 |
days |
| R/qtl |
1.0 |
4.7 |
days |
| snpMatrix |
3.2 |
5.1 |
days |
| eMap |
17.8 |
N/A |
days |
| FastMap |
10.3 |
N/A |
hours |
Info: Details of the testing procedure.
Fact: Matrix eQTL results match those by other software.
Comparison conducted analyzing CF dataset with 573,337 SNPs and 22,011 transcripts over 840 samples.
Tested on a quad-core PC, using additive linear models with zero and with 10 covariates.
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Manuscript in Bioinformatics (2012)
Getting started with Matrix eQTL
Features of Matrix eQTL
Reference manual
Frequently Asked Questions
Questions, comments, concerns?
Contact me: Andrey A. Shabalin
Support Matrix eQTL
Update History:
- 2014, February 24 — Update. Version 2.1.0
- Added support for more than 3 ANOVA categories (see
modelANOVA).
- Added option to reduce memory consumption for large gene data sets.
options(MatrixEQTL.dont.preserve.gene.object)
- Faster cis-only analysis. Much faster for small
cisDist and small slice sizes.
- File names for the output can now also be connections or
NULL.
- Redesign of QQ-plots.
- The
plot function now follows the R convention and uses ylim instead of ymin parameter.
- Fixed a bug occurring for genes collinear with covariates.
- Removed extra space in the output header (for linear models with beta estimates)
- Sets
pvbins are now fixed for QQ-plots. This makes aggregation of results easier.
- Previous updates
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