comment out QTLPlot module and update project.toml

Project.toml

@@ -10,23 +10,19 @@ Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LossFunctions = "30fc2ffe-d236-52d8-8643-a9d8f7c094a7"
-PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
-Conda = "1"
-DelimitedFiles = "1"
 Distributions = "^0.23, 0.24, 0.25"
 LossFunctions = "~0.11"
-PyPlot = "~2.11"
 Revise = "3.5"
-StaticArrays = "1.7"
+StaticArrays = "1.9"
+StatsBase = "0.33, 0.34"
 Statistics = "1"
-StatsBase = "0.33"
 julia = "~1.9"
 
 [extras]

docs/src/functions.md

@@ -16,12 +16,6 @@ Modules = [FlxQTL, flxMLMM, EcmNestrv]
 Modules = [GRM]
 ```
 
-## Visualization (QTLplot)
-
-```@autodocs
-Modules = [QTLplot]
-```
-
 ## Multivariate Linear Models (MLM)
 
 ```@autodocs
@@ -35,3 +29,4 @@ Modules = [MLM]
 ```@autodocs
 Modules = [Util]
 ```
+

docs/src/guide/analysis.md

@@ -143,7 +143,9 @@ third marker, type `B[:,:,3]`.
 
 ## Generating plots
 
-To produce a plot (or plots) for LOD scores or effects, you need first a struct of arrays, `layers` consisting of chromosomes, marker positions, 
+The `QTLplot` module is currently unavailable but will replaced with [BigRiverQTLPlots.jl](https://github.com/senresearch/BigRiverQTLPlots.jl) soon.
+
+<!-- To produce a plot (or plots) for LOD scores or effects, you need first a struct of arrays, `layers` consisting of chromosomes, marker positions, 
 LOD scores (or effects), which should be `Array{Float64,2}`.  You can then generate one genome scan result or multiple genenome scan results on one plot.  Note that the color is randomly selected to generate a plot.   
 The function `plot1d` has more keyword argument options: `yint=[]` for a vector of y-intercept(s), `yint_color=["red"]` for a vector of y-intercept 
 color(s), `Legend=[]` for multiple graphs, `loc="upper right"` for the location of `Legend`, etc.
@@ -155,7 +157,7 @@ plot1d(Arab_lod;title= "LOD for Arabidopsis thaliana : Fitness (2 site by 3 year
 ![arabidopsis](arab-lod.png)
 
 
-
+ -->
 
 ## Performing a permutation test
 

docs/src/guide/tutorial.md

@@ -15,7 +15,7 @@ Or, equivalently,
 ```julia
 julia> using Pkg; Pkg.add("FlxQTL")
 ```
-Currently Julia `1.5` supports for the package.
+<!-- Currently Julia `1.5` supports for the package. -->
 
 
 To remove the package from the Julia REPL,

docs/src/index.md

@@ -7,9 +7,9 @@ structured multivariate traits.
 
 - Genome scan (1D, 2D) for univariate, multivariate trait(s), and genotype (probability) data
 - LOCO (Leave One Chromosome Out) support for genome scan 
-- Visualization (1D, 2D plots)
 - Computation for Genetic (or Climatic) Relatedness matrix (or kinship) 
 - CPU parallelization
+<!-- - Visualization (1D, 2D plots) -->
 
 ## Guide
 

src/geneScan1D.jl

@@ -70,36 +70,36 @@
 end
 
 ## upgraded version: 
 function marker1Scan(m,q,kmin,cross,λg,Y1,Xnul_t,X1,Z1,Nullpar::Result,Σ1,τ2_0;ρ=0.001,tol0=1e-3,tol1=1e-4,nchr=0)
 
         nmar=size(X1,1);
     if (cross==1) ## scanning genotypes
         B0=hcat(Nullpar.B,zeros(Float64,q));
 
         lod=@distributed (vcat) for j=1:nmar
             XX=vcat(Xnul_t,@view X1[[j],:])
         B0,τ2,Σ,loglik0 =ecmLMM(Y1,XX,Z1,B0,τ2_0,Σ1,λg,ones(m);tol=tol0)
             lod0= (loglik0-Nullpar.loglik)/log(10)
         est1=ecmNestrvAG(lod0,kmin,Y1,XX,Z1,B0,τ2,Σ,λg,ones(m);ρ=ρ,tol=tol1,numChr=nchr,nuMarker=j)
             [(est1.loglik-Nullpar.loglik)/log(10) est1]
                  end
 
     else # cross>1
         ## scanning genotype probabilities
 
             B0=hcat(Nullpar.B,zeros(Float64,q,cross-1))
 
           lod=@distributed (vcat) for j=1:nmar
                 XX= vcat(Xnul_t, @view X1[j,2:end,:])
                 B0,τ2,Σ,loglik0 =ecmLMM(Y1,XX,Z1,B0,τ2_0,Σ1,λg,ones(m);tol=tol0)
                   lod0= (loglik0-Nullpar.loglik)/log(10)
                 est1=ecmNestrvAG(lod0,kmin,Y1,XX,Z1,B0,τ2,Σ,λg,ones(m);ρ=ρ,tol=tol1)
             [(est1.loglik-Nullpar.loglik)/log(10) est1]
                                   end
 
      end
 
     return lod[:,1],lod[:,2]
 end
 
 
@@ -221,11 +221,11 @@
            for i=1:nChr
                 maridx=findall(XX.chr.==Chr[i])
 #                 Xnul_t=Xnul*Tg[:,:,i]';
-   @fastmath @inbounds Xnul_t=BLAS.gemm('N','T',Xnul,@view Tg[:,:,i])
+   @fastmath @inbounds Xnul_t=BLAS.gemm('N','T',Xnul, Tg[:,:,i])
                  if (cross!=1)
-   @fastmath @inbounds @views Y2,X1=transForm(Tg[:,:,i],Y1,X0[maridx,:,:],cross)
+   @fastmath @inbounds Y2,X1=transForm(Tg[:,:,i],Y1,X0[maridx,:,:],cross)
                    else
-  @fastmath @inbounds @views  Y2,X1=transForm(Tg[:,:,i],Y1,XX.X[maridx,:],cross)
+  @fastmath @inbounds Y2,X1=transForm(Tg[:,:,i],Y1,XX.X[maridx,:],cross)
                  end
                 #parameter estimation under the null
                   est00=nulScan(init,1,Λg[:,i],λc,Y2,Xnul_t,Z1,Σ1;ρ=ρ,itol=itol,tol=tol)
@@ -297,11 +297,11 @@
            for i=1:nChr
                 maridx=findall(XX.chr.==Chr[i])
 #                 Xnul_t=Xnul*Tg[:,:,i]';
-   @fastmath @inbounds Xnul_t=BLAS.gemm('N','T',Xnul,@view Tg[:,:,i])
+   @fastmath @inbounds Xnul_t=BLAS.gemm('N','T',Xnul,Tg[:,:,i])
                  if (cross!=1)
-      @fastmath @inbounds @views Y2,X1=transForm(Tg[:,:,i],Y1,X0[maridx,:,:],cross)
+      @fastmath @inbounds Y2,X1=transForm(Tg[:,:,i],Y1,X0[maridx,:,:],cross)
                    else
-      @fastmath @inbounds @views Y2,X1=transForm(Tg[:,:,i],Y1,XX.X[maridx,:],cross)
+      @fastmath @inbounds Y2,X1=transForm(Tg[:,:,i],Y1,XX.X[maridx,:],cross)
                  end
                 #parameter estimation under the null
                 est00=nulScan(init,1,Λg[:,i],λc,Y2,Xnul_t,Σ1;ρ=ρ,itol=itol,tol=tol)
@@ -363,9 +363,9 @@
 #                 Xnul_t=Xnul*Tg[:,:,i]';
               @fastmath @inbounds Xnul_t=BLAS.gemm('N','T',Xnul,@view Tg[:,:,i])
                 if (cross!=1)
-          @fastmath @inbounds @views Y,X=transForm(Tg[:,:,i],Y0,X0[maridx,:,:],cross)
+          @fastmath @inbounds Y,X=transForm(Tg[:,:,i],Y0,X0[maridx,:,:],cross)
                    else
-           @fastmath @inbounds @views Y,X=transForm(Tg[:,:,i],Y0,XX.X[maridx,:],cross)
+           @fastmath @inbounds Y,X=transForm(Tg[:,:,i],Y0,XX.X[maridx,:],cross)
                  end
                 #parameter estimation under the null
                     est00=nulScan(init,1,Λg[:,i],Y,Xnul_t;itol=itol,tol=tol,ρ=ρ)
@@ -414,14 +414,14 @@
 # export geneScan,marker1Scan
 ## upgraded version: H0:MLMM, H1:FlxQTL
 
 function obtainKc(T::Array{Float64,2},λ::Array{Float64,1},Y0::Array{Float64,2},Z0::Array{Float64,2};
         Xnul::Array{Float64,2}=ones(1,size(Y0,2)),itol=1e-2,tol::Float64=1e-3,ρ=0.001)
 
 #       T,λ = K2eig(Kg)
       init=initial(Xnul,Y0,Z0,false)
       Y, Xnul_t=transForm(T,Y0,Xnul,1)
       est0=nulScan1(init,1,λ,Y,Xnul_t,Z0;itol=itol,tol=tol,ρ=ρ)
       return est0.Vc, Y, Xnul_t
 end
 
 include("geneScan1.jl")

src/transformation.jl

@@ -158,7 +158,7 @@
         else #cross>1 size(X0)= (p,cross,n)
           p=size(X0,1); n=size(X0,3)
             X=zeros(p,cross,n)
-       @fastmath @inbounds @views  for j=1:cross
+       @fastmath @inbounds @views for j=1:cross
 #             X[:,j,:] = X0[:,j,:]*Tg'
              X[:,j,:]= BLAS.gemm('N','T',X0[:,j,:],Tg)
                      end
@@ -208,8 +208,8 @@
             return Init(init_val.B',τ2,init_val.Σ)
         else #H0 for MLMM
 
         lmul!(sqrt(1/m),init_val.Σ)
         return Init0(init_val.B',0.5*init_val.Σ,0.5*init_val.Σ)
         end
 
 end
@@ -242,10 +242,10 @@
     return nulpar
 end
 
 function nulScan(init::Init1,kmin,λg,λc,Y1,Xnul_t,Z1;ρ=0.001,itol=1e-3,tol=1e-4)
 
             B0,τ2_0,Σ1,loglik0 =ecmLMM(Y1,Xnul_t,Z1,init.B,init.τ2,init.Σ1,λg,λc;tol=itol)
             nulpar=NestrvAG(kmin,Y1,Xnul_t,Z1,B0,τ2_0,Σ1,λg,λc;ρ=ρ,tol=tol)
 
     return nulpar
 
@@ -261,16 +261,16 @@
 end
 
 #new version to estimate Kc
 function nulScan1(init::Union{Init1,Init0},kmin,λg,Y1,Xnul_t,Z;ρ=0.001,itol=1e-3,tol=1e-4)
 
        if (typeof(init)==Init1)   
            B0,Kc_0,Σ1,loglik0 =ecmLMM(Y1,Xnul_t,Z,init.B,init.Kc,init.Σ1,λg;tol=itol)
            nulpar=NestrvAG(kmin,Y1,Xnul_t,Z,B0,Kc_0,Σ1,λg;tol=tol,ρ=ρ)
         else #typeof(Init)==Init0)
            B0,Kc_0,Σ1,loglik0 =ecmLMM(Y1,Xnul_t,Z,init.B,init.Vc,init.Σ,λg;tol=itol)
            nulpar=NestrvAG(kmin,Y1,Xnul_t,Z,B0,Kc_0,Σ1,λg;tol=tol,ρ=ρ)
         end
        return nulpar
 end