How to Install and Uninstall eigensoft Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: December 28,2024

1. Install "eigensoft" package

Here is a brief guide to show you how to install eigensoft on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                eigensoft
            
                    
    
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2. Uninstall "eigensoft" package

Here is a brief guide to show you how to uninstall eigensoft on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                eigensoft
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the eigensoft package on Ubuntu 20.10 (Groovy Gorilla)

Package: eigensoft
Architecture: amd64
Version: 7.2.1+dfsg-2
Priority: optional
Section: multiverse/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 2791
Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgsl25 (>= 2.6), liblapack3 | liblapack.so.3, liblapacke (>= 3.9.0), perl:any
Recommends: gnuplot
Filename: pool/multiverse/e/eigensoft/eigensoft_7.2.1+dfsg-2_amd64.deb
Size: 361936
MD5sum: 89e12f3374c518634e6087bde695d6c6
SHA1: 960671f9614b878f20ae445abd981c7fd2ef09b7
SHA256: 367621511bd8a422aa2fd47cc5dfe52286f6017e2155b993d072a72f060a3fbd
SHA512: cbbb8518149d0c149dac45fbc27ca722e44acf8de2e8eb7ec820b9a742edf78212cf1390f3db7a1b405b4927c7618360c9d4240bfcbbf1ddec6b790e7d570a79
Homepage: http://www.hsph.harvard.edu/faculty/alkes-price/software/
Description-en: reduction of population bias for genetic analyses
The EIGENSOFT package combines functionality from the group's population
genetics methods (Patterson et al. 2006) and their EIGENSTRAT stratification
method (Price et al. 2006). The EIGENSTRAT method uses principal components
analysis to explicitly model ancestry differences between cases and
controls along continuous axes of variation; the resulting correction is
specific to a candidate marker's variation in frequency across ancestral
populations, minimizing spurious associations while maximizing power to
detect true associations. The EIGENSOFT package has a built-in plotting
script and supports multiple file formats and quantitative phenotypes.
Description-md5: 2245ac8a80e3acb8bcf1427468278197