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In order to enable users to work with mvMAPIT without having to
install all dependencies and libraries on their local, we provide a
docker build with all dependencies and an installed R package
mvMAPIT
. Learn how to build your own version of the docker
image.
Follow the official guide to learn how to Get Docker. This is required for being able to follow this tutorial.
The github repository of mvMAPIT already comes
with a Dockerfile
. To build the image, clone the github
repository and run the following commands.
This will produce an image named mvmapit
that contains
Rstudio
, mvMAPIT
, and all dependencies.
With a local copy of the docker image mvmapit
available,
run the following code.
This will start the docker container that serves an RStudio
application at localhost:8787
. In this container,
mvMAPIT
is already installed and can be imported and run in
the R console via the following code.
library(mvMAPIT)
mvmapit(t(simulated_data$genotype[1:100,1:10]),
t(simulated_data$trait[1:100,]),
cores = 2, logLevel = "DEBUG")
## 2023-09-25 13:10:45.66269 DEBUG:mvmapit:Running in normal test mode.
## 2023-09-25 13:10:45.665861 DEBUG:mvmapit:Genotype matrix: 10 x 100
## 2023-09-25 13:10:45.668582 DEBUG:mvmapit:Phenotype matrix: 2 x 100
## 2023-09-25 13:10:45.668886 DEBUG:mvmapit:Number of zero variance variants: 0
## 2023-09-25 13:10:45.669202 DEBUG:mvmapit:Genotype matrix after removing zero variance variants: 10 x 100
## 2023-09-25 13:10:45.669406 DEBUG:mvmapit:Scale X matrix appropriately.
## 2023-09-25 13:10:45.669805 INFO:mvmapit:Running normal C++ routine.
## 2023-09-25 13:10:45.6863 DEBUG:mvmapit:Calculated mean time of execution. Return list.
## $pvalues
## # A tibble: 30 × 3
## id trait p
## <chr> <chr> <dbl>
## 1 snp_00001 p_01*p_01 0.435
## 2 snp_00001 p_02*p_01 0.476
## 3 snp_00001 p_02*p_02 0.909
## 4 snp_00002 p_01*p_01 0.510
## 5 snp_00002 p_02*p_01 0.606
## 6 snp_00002 p_02*p_02 0.326
## 7 snp_00003 p_01*p_01 0.452
## 8 snp_00003 p_02*p_01 0.536
## 9 snp_00003 p_02*p_02 0.140
## 10 snp_00004 p_01*p_01 0.688
## # ℹ 20 more rows
##
## $pves
## # A tibble: 30 × 3
## id trait PVE
## <chr> <chr> <dbl>
## 1 snp_00001 p_01*p_01 0.0615
## 2 snp_00001 p_02*p_01 0.0663
## 3 snp_00001 p_02*p_02 -0.00451
## 4 snp_00002 p_01*p_01 -0.0215
## 5 snp_00002 p_02*p_01 -0.0239
## 6 snp_00002 p_02*p_02 -0.0287
## 7 snp_00003 p_01*p_01 0.0579
## 8 snp_00003 p_02*p_01 0.0493
## 9 snp_00003 p_02*p_02 -0.0400
## 10 snp_00004 p_01*p_01 -0.0146
## # ℹ 20 more rows
##
## $duration
## process duration_ms
## 1 cov NaN
## 2 projections NaN
## 3 vectorize NaN
## 4 q NaN
## 5 S NaN
## 6 vc NaN
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.