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For release 0.3 (#175)
* cleanup repo * remove rs regexp assumption * fix adapt_flashpca * remove some unused files
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.github/workflows/CompatHelper.yml

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Project.toml

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@@ -10,12 +10,8 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
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CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
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Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
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JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
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MKL = "33e6dc65-8f57-5167-99aa-e5a354878fb2"
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Mmap = "a63ad114-7e13-5084-954f-fe012c677804"
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SnpArrays = "4e780e97-f5bf-4111-9dc4-b70aaf691b06"
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TMLE = "8afdd2fb-6e73-43df-8b62-b1650cd9c8cf"
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YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
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[compat]
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CategoricalArrays = "0.10"
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Combinatorics = "1.0"
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DataFrames = "1.2"
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HypothesisTests = "0.10"
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JLD2 = "0.4"
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MKL = "0.5"
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SnpArrays = "0.3"
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TMLE = "0.8.2"
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YAML = "0.4"

README.md

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### MechanismActors strategy
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For this strategy, use the `with-actors` command. and `--asb-prefix` and `--trans-actors` options. This will generate pairwise interaction parameters between bQTLs output by the [Baal-ChIP pipeline](https://git.ecdf.ed.ac.uk/oalmelid/baal-nf) and trans-actors eQTLs from a CSV file. Additionally, if template parameters configuration files containing extra treatments are provided, nth-order interaction parameters will be generated.

bin/sieve_variance.jl

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bin/summarize.jl

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bin/tmle_inputs.jl

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arg_type = Int
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required = false
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help = "Further performance may be obtained by batching phenotypes in a"*
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" single Targeted Estimation run"
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" single Targeted Estimation run. If not specified, all phenotypes "*
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"constitute a batch."
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"--positivity-constraint"
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arg_type = Float64

build_sysimage.jl

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docker/Dockerfile

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ENV JULIA_DEPOT_PATH=/opt
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RUN apt-get update && apt-get install -y wget unzip procps libcurl4-openssl-dev libbz2-dev liblzma-dev
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RUN apt-get update && apt-get install -y wget unzip procps
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# Install gcta
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RUN chmod a+x /usr/local/bin/gcta64
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# Install R and hal9001
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RUN apt-get install -y r-base \
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r-base-core \
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r-recommended \
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r-base-dev
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RUN apt-get install -y libssl-dev \
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libxml2-dev \
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libcurl4-openssl-dev \
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libgit2-dev \
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libfontconfig1-dev \
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libharfbuzz-dev \
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libfribidi-dev \
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libpng-dev \
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libtiff5-dev \
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libjpeg-dev
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RUN R -e "install.packages('devtools', repos='http://cran.us.r-project.org'); \
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require(devtools);\
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install_version('hal9001', version = '0.4.1', repos = 'http://cran.us.r-project.org')"
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# Import project, build and precompile
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COPY . /TargeneCore.jl
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RUN julia -q --project=/TargeneCore.jl -e'using Pkg; Pkg.instantiate(); Pkg.resolve(); Pkg.precompile(); using TargeneCore'
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# Prepend a writable DEPOT_PATH ($PWD is mounted by default by singularity)
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RUN mkdir $PWD/julia_depot
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ENV JULIA_DEPOT_PATH=$PWD/julia_depot:$JULIA_DEPOT_PATH
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# RUN julia -q --project build_sysimage.jl
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# RUN echo 'alias julia="julia -J TMLEEpistasisSysimage.so"' >> ~/.bashrc

example_script.jl

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src/TargeneCore.jl

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module TargeneCore
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if occursin("Intel", Sys.cpu_info()[1].model)
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using MKL
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end
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using DataFrames
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using CSV
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using TMLE
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using BGEN
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using JLD2
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using SnpArrays
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using Mmap
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using HypothesisTests
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using YAML
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using Combinatorics
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###############################################################################
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# INCLUDES
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include("confounders.jl")
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include("sieve_plateau.jl")
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include(joinpath("tmle_inputs", "tmle_inputs.jl"))
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include(joinpath("tmle_inputs", "from_actors.jl"))
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include(joinpath("tmle_inputs", "from_param_files.jl"))
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# EXPORTS
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export filter_chromosome, merge_beds, adapt_flashpca
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export sieve_variance_plateau
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export tmle_inputs
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end

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