GWIS configuration

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.10.4"
 manifest_format = "2.0"
-project_hash = "2b58dcc0ffd21f9ddd37c869a6c32c55af516134"
+project_hash = "b1a916e4f68d2fb953c6eba21c0fc7546e7dc5c6"
 
 [[deps.ARFFFiles]]
 deps = ["CategoricalArrays", "Dates", "Parsers", "Tables"]

src/inputs_from_config.jl

@@ -60,13 +60,22 @@ function estimands_from_variants(
     estimand_constructor, 
     outcomes, 
     confounders;
+    extra_treatments=[],
     outcome_extra_covariates=[],
     positivity_constraint=0.,
     verbosity=1
     )
     estimands = []
     for variant in variants
-        treatments = treatments_from_variant(variant, dataset)
+
+        if isempty(extra_treatments)
+            treatments = treatments_from_variant(variant, dataset)
+        elseif length(extra_treatments) == 1
+            treatments = Dict(treatments_from_variant(variant, dataset)..., treatments_from_variant(string(extra_treatments[1]), dataset)...)
+        else
+            error("GWIS mode only supports pairwise interaction with one extra treatment.")
+        end
+
         local Ψ
         try
             Ψ = factorialEstimands(
@@ -171,13 +180,15 @@ function treatments_from_variant(variant::String, dataset::DataFrame)
 end
 
 function estimands_from_gwas(dataset, variants, outcomes, confounders; 
+    extra_treatments=extra_treatments,
     outcome_extra_covariates = [],
     positivity_constraint=0.,
     verbosity=0
     )
     variants_groups = Iterators.partition(variants, length(variants) ÷ Threads.nthreads())
     estimands_tasks = map(variants_groups) do variants
         Threads.@spawn estimands_from_variants(variants, dataset, ATE, outcomes, confounders;
+            extra_treatments=extra_treatments,
             outcome_extra_covariates=outcome_extra_covariates,
             positivity_constraint=positivity_constraint,
             verbosity=verbosity
@@ -187,6 +198,24 @@ function estimands_from_gwas(dataset, variants, outcomes, confounders;
     return vcat(estimands_partitions...)
 end
 
+function estimands_from_gwis(dataset, variants, outcomes, confounders;
+    extra_treatments=extra_treatments,
+    outcome_extra_covariates = [],
+    positivity_constraint=0.,
+    verbosity=0
+    )
+    variants_groups = Iterators.partition(variants, length(variants) ÷ Threads.nthreads())
+    estimands_tasks = map(variants_groups) do variants
+        Threads.@spawn estimands_from_variants(variants, dataset, AIE, outcomes, confounders;
+            extra_treatments=extra_treatments,
+            outcome_extra_covariates=outcome_extra_covariates,
+            positivity_constraint=positivity_constraint,
+            verbosity=verbosity
+        )
+    end
+    estimands_partitions = fetch.(estimands_tasks)
+    return vcat(estimands_partitions...)
+end
 
 get_only_file_with_suffix(files, suffix) = files[only(findall(x -> endswith(x, suffix), files))]
 
@@ -210,7 +239,7 @@ function get_genotypes_from_beds(bedprefix)
 end
 
 function make_genotypes(genotype_prefix, config, call_threshold)
-    genotypes = if config["type"] == "gwas"
+    genotypes = if config["type"] == "gwas"|| config["type"] == "gwis"
         get_genotypes_from_beds(genotype_prefix)
     else
         variants_set = Set(retrieve_variants_list(config["variants"]))
@@ -263,10 +292,19 @@ function inputs_from_config(config_file, genotypes_prefix, traits_file, pcs_file
     elseif config_type == "gwas"
         variants = filter(!=("SAMPLE_ID"), names(genotypes))
         estimands_from_gwas(dataset, variants, outcomes, confounders;
+            extra_treatments=extra_treatments,
             outcome_extra_covariates=outcome_extra_covariates,
             positivity_constraint=positivity_constraint,
             verbosity=verbosity
         )
+    elseif config_type == "gwis"
+        variants = filter(!=("SAMPLE_ID"), names(genotypes))
+        estimands_from_gwis(dataset, variants, outcomes, confounders;
+            extra_treatments=extra_treatments,
+            outcome_extra_covariates=outcome_extra_covariates,
+            positivity_constraint=positivity_constraint,
+            verbosity=verbosity
+        )  
     else
         throw(ArgumentError(string("Unknown extraction type: ", config_type, ", use any of: (flat, groups, gwas)")))
     end

test/data/config_gwis.yaml

@@ -0,0 +1,10 @@
+type: gwis
+
+extra_treatments:
+  - 22001
+
+outcome_extra_covariates:
+  - COV_1
+
+extra_confounders:
+  - 21003

test/inputs_from_gwis_config.jl

@@ -0,0 +1,113 @@
+module TestGwisEstimands
+
+using Test
+using SnpArrays
+using TargeneCore
+using Arrow
+using DataFrames
+using Serialization
+using TMLE
+using CSV
+
+TESTDIR = joinpath(pkgdir(TargeneCore), "test")
+
+include(joinpath(TESTDIR, "testutils.jl"))
+
+function get_summary_stats(estimands)
+    outcomes = [TargeneCore.get_outcome(Ψ) for Ψ in estimands]
+    results = DataFrame(ESTIMAND = estimands, OUTCOME = outcomes)
+    return sort(combine(groupby(results, :OUTCOME), nrow), :OUTCOME)
+end
+
+function check_estimands_levels_order(estimands)
+    for Ψ in estimands
+        # If the two components are present, the first is the 0 -> 1 and the second is the 1 -> 2
+        variant = collect(keys(Ψ.args[1].treatment_values))[2]
+        if length(Ψ.args) == 2
+            @test Ψ.args[1].treatment_values[variant] == (control = 0x00, case = 0x01)
+            @test Ψ.args[2].treatment_values[variant] == (control = 0x01, case = 0x02)
+        else
+            # Otherwise we check they are one or the other
+            arg = only(Ψ.args)
+            @test arg.treatment_values[variant]==(control = 0x00, case = 0x01) ||
+            arg.treatment_values[variant]==( control = 0x01, case = 0x02)
+        end
+   end
+end
+
+@testset "Test inputs_from_config gwis: no positivity constraint" begin
+    tmpdir = mktempdir()
+    copy!(ARGS, [
+        "estimation-inputs",
+        joinpath(TESTDIR, "data", "config_gwis.yaml"),
+        string("--traits-file=", joinpath(TESTDIR, "data", "ukbb_traits.csv")),
+        string("--pcs-file=", joinpath(TESTDIR, "data", "ukbb_pcs.csv")),
+        string("--genotypes-prefix=", joinpath(TESTDIR, "data", "ukbb", "genotypes" , "ukbb_1.")),
+        string("--outprefix=", joinpath(tmpdir, "final")), 
+        "--batchsize=5",
+        "--verbosity=0",
+        "--positivity-constraint=0"
+    ])
+    TargeneCore.julia_main()
+    # Check dataset
+    dataset = DataFrame(Arrow.Table(joinpath(tmpdir, "final.data.arrow")))
+    @test size(dataset) == (1940, 886)
+
+    # Check estimands
+    estimands = []
+    for file in readdir(tmpdir, join=true)
+        if endswith(file, "jls")
+            append!(estimands, deserialize(file).estimands)
+        end
+    end
+    @test all(e isa JointEstimand for e in estimands)
+
+    # There are 875 variants in the dataset
+    summary_stats = get_summary_stats(estimands)
+    @test summary_stats == DataFrame(
+        OUTCOME = [:BINARY_1, :BINARY_2, :CONTINUOUS_1, :CONTINUOUS_2, :TREAT_1], 
+        nrow = repeat([875], 5)
+    )
+
+    check_estimands_levels_order(estimands)
+end
+
+
+@testset "Test inputs_from_config gwis: positivity constraint" begin
+    tmpdir = mktempdir()
+    copy!(ARGS, [
+        "estimation-inputs",
+        joinpath(TESTDIR, "data", "config_gwis.yaml"),
+        string("--traits-file=", joinpath(TESTDIR, "data", "ukbb_traits.csv")),
+        string("--pcs-file=", joinpath(TESTDIR, "data", "ukbb_pcs.csv")),
+        string("--genotypes-prefix=", joinpath(TESTDIR, "data", "ukbb", "genotypes" , "ukbb_1.")),
+        string("--outprefix=", joinpath(tmpdir, "final")), 
+        "--batchsize=5",
+        "--verbosity=0",
+        "--positivity-constraint=0.2"
+    ])
+    TargeneCore.julia_main()
+    # Check dataset
+    dataset = DataFrame(Arrow.Table(joinpath(tmpdir, "final.data.arrow")))
+    @test size(dataset) == (1940, 886)
+    # Check estimands
+    estimands = []
+    for file in readdir(tmpdir, join=true)
+        if endswith(file, "jls")
+            append!(estimands, deserialize(file).estimands)
+        end
+    end
+    # The positivity constraint reduces the number of variants
+    @test all(e isa JointEstimand for e in estimands)
+    summary_stats = get_summary_stats(estimands)
+    @test summary_stats == DataFrame(
+        OUTCOME = [:BINARY_1, :BINARY_2, :CONTINUOUS_1, :CONTINUOUS_2, :TREAT_1], 
+        nrow = repeat([142], 5)
+    )
+
+    check_estimands_levels_order(estimands)
+end
+
+
+end
+true

test/runtests.jl

@@ -10,5 +10,6 @@ TESTDIR = joinpath(pkgdir(TargeneCore), "test")
     @test include(joinpath(TESTDIR, "inputs_from_estimands.jl"))
     @test include(joinpath(TESTDIR, "inputs_from_config.jl"))
     @test include(joinpath(TESTDIR, "inputs_from_gwas_config.jl"))
+    @test include(joinpath(TESTDIR, "inputs_from_gwis_config.jl"))
     @test include(joinpath(TESTDIR, "sieve_variance.jl"))
 end