Matti Pirinen's Software
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The use of the attached source codes on this page is allowed under the
terms of the GNU
general public license (version 3).
FINEMAP and metaCCA have their own licenses.
More recent software including linemodels package can be found from GitHub.
biMM: Efficient estimation of genetic variances and
covariances for cohorts with high-dimensional
phenotype measurements.
Publication
in Bioinformatics (2017) and
the R-package biMM_1.0.0.tar.gz (updated 3 Mar 2017) with a vignette.
FINEMAP: Efficient variable selection using summary data from genome-wide association studies.
Publication in Bioinformatics (2016)
and the software.
metaCCA: Multivariate meta-analysis of genome-wide association studies using canonical correlation analysis
Publication in Bioinformatics (2016)
and the software.
Allele specific expression from RNA-seq read count data for multiple tissues
These source codes implement the models described in paper
Assessing allele specific expression across multiple tissues from RNA-seq read count data Bioinformatics 2015.
R-codes plus data sets (61 MB); or just the R-codes for functions and examples.
For a user-friendly interface for the same models see MAMBA by Manuel Rivas.
Linear mixed model software MMM
MMM is a software package for analysing a linear mixed model with one random effect
whose covariance structure can be freely specified by the user.
It is written with large data sets in mind: applied to real data sets where
hundreds of thousands of predictors on over 20,000 individuals
are tested one-by-one. Motivation for MMM came from genome-wide association studies,
but it can be used with other data as well. Written in C (GNU-C) and uses GSL and, preferably, LAPACK libraries.
Current version 1.01 (updated 10-Feb-2014): Software. Manual.
Pirinen M, Donnelly P and Spencer CCA (2012):
Efficient Computation with a Linear Mixed Model on Large-scale Data Sets with Applications to Genetic Studies.
Ann Appl Stat 7(1): 369-390.
Text and Supplementary text available.
Non-confounding covariates in logistic regression
R-functions to assess the effect of a single binary or continuous covariate
on power for detecting genetic variants (of small effects) in GWAS.
Includes as examples the codes for plotting Supplementary Figures of the publication.
R-functions. Examples.
Pirinen M, Donnelly P and Spencer CCA (2012):
Including known covariates can reduce power to detect genetic effects in case-control studies.
Nat Genet 44: 848-851.
Hippo and AEML
Haplotype estimation using incomplete prior information from pooled observations (Hippo), and
Approximate EM-algorithm with list of known haplotypes (AEML) are in a
Tar-package.
These algorithms estimate population haplotype frequencies from pooled SNP data and
can make use of a list of the haplotypes that are known to exist in the population. Written in
ANSI-C using Gnu Scientific Library (GSL v.1.0.0).
For description see:
Pirinen M (2009):
Estimating population haplotype frequencies from pooled SNP data using incomplete prior information.
Bioinformatics 25(24):3296-3302.
APE
Allelic Path Explorer (APE)
As a Tar-package.
APE is a program for extending partially known genotype data on a given
pedigree consistently (i.e. in accordance with the Mendelian rules)
to the whole pedigree. Written in ANSI-C. For details, see:
Pirinen M and Gasbarra D (2006):
Finding Consistent Gene Transmission Patterns on Large and Complex Pedigrees.
IEEE/ACM Trans. on Computational Biology and
Bioinformatics 3(3):252-262.