R语言 Julia以及全基因组选择

小编著：
最近在学Julia语言，想测试一下和R的区别，发现前辈的博客，翻译时不禁感慨，这是2018年了，博客是2010年的，8年已过，我才听说Julia。但……不晚！

文章来源： https://www.r-bloggers.com/r-julia-and-genome-wide-selection/

我想起一些琐事的事情，以及一些断裂的代码，在2010年我参加了基因组选择的summer school（http://taurus.ansci.iastate.edu/wiki/pages/E4o0S0C7/Course_materials.html），共有2000个个体，20000个SNP（0,1,2），然后使用MCMC 计算育种值，使用的是R语言。

nmarkers = 2000;    # number of markers
startMarker = 1981; # set to 1 to use all
numiter  = 2000;    # number of iterations
vara     = 1.0/20.0; 

# input data
data     = matrix(scan("trainData.out0"),ncol=nmarkers+2,byrow=TRUE);
nrecords = dim(data)[1];

beg = Sys.time()

# x has the mean followed by the markers
x = cbind(1,data[,startMarker:nmarkers]);
y = data[,nmarkers+1];
a =  data[,nmarkers+2];
# inital values

nmarkers = nmarkers - startMarker + 1;
mean2pq = 0.5;                          # just an approximation
scalea  = 0.5*vara/(nmarkers*mean2pq);  # 0.5 = (v-2)/v for v=4

size = dim(x)[2];
b = array(0.0,size);
meanb = b;
b[1] = mean(y);
var  = array(0.0,size);

# adjust y
 ycorr = y - x%*%b;                  

# MCMC sampling
for (iter in 1:numiter){
  # sample vare
  vare = ( t(ycorr)%*%ycorr )/rchisq(1,nrecords + 3);

  # sample intercept
  ycorr = ycorr + x[,1]*b[1];
  rhs = sum(ycorr)/vare;
  invLhs = 1.0/(nrecords/vare);
  mean = rhs*invLhs;
  b[1] = rnorm(1,mean,sqrt(invLhs));
  ycorr = ycorr - x[,1]*b[1];
  meanb[1] = meanb[1] + b[1];

  # sample variance for each locus
  for (locus in 2:size){
    var[locus] = (scalea*4+b[locus]*b[locus])/rchisq(1,4.0+1)
  }

# sample effect for each locus
  for (locus in 2:size){
    # unadjust y for this locus
    ycorr = ycorr + x[,locus]*b[locus];
    rhs = t(x[,locus])%*%ycorr/vare;
    lhs = t(x[,locus])%*%x[,locus]/vare + 1.0/var[locus];
    invLhs = 1.0/lhs;
    mean = invLhs*rhs;
    b[locus]= rnorm(1,mean,sqrt(invLhs));
    #adjust y for the new value of this locus
    ycorr = ycorr - x[,locus]*b[locus];
    meanb[locus] = meanb[locus] + b[locus];
  }
}

Sys.time() - beg

meanb = meanb/numiter;
aHat  = x %*% meanb;

然后，我们需要定义几个新的变量，将基因组数据，表型数据以及育种值数据读进矩阵里面，简历几个循环，进行向量的运算。

我使用Julia去做类似的事情：

nmarkers = 2000    # Number of markers
startmarker = 1981 # Set to 1 to use all
numiter = 2000     # Number of iterations

data = dlmread("markers.csv", ',')
(nrecords, ncols) = size(data)

tic()

#this is the mean and markers matrix
X = hcat(ones(Float64, nrecords), data[:, startmarker:nmarkers])
y = data[:, nmarkers + 1]
a = data[:, nmarkers + 2]

nmarkers = nmarkers - startmarker + 1
vara = 1.0/nmarkers
mean2pq = 0.5

scalea  = 0.5*vara/(nmarkers*mean2pq) # 0.5 = (v-2)/v for v=4

ndesign = size(X, 2)
b = zeros(Float64, ndesign)
meanb = zeros(Float64, ndesign)
b[1] = mean(y)
varian  = zeros(Float64, ndesign)

# adjust y
ycorr = y - X * b                  

# MCMC sampling
for i = 1:numiter
  # sample vare
  vare = dot(ycorr, ycorr )/randchi2(nrecords + 3)

  # sample intercept
  ycorr = ycorr + X[:, 1] * b[1];
  rhs = sum(ycorr)/vare;
  invlhs = 1.0/(nrecords/vare);
  mn = rhs*invlhs;
  b[1] = randn() * sqrt(invlhs) + mn;
  ycorr = ycorr - X[:, 1] * b[1];
  meanb[1] = meanb[1] + b[1];

  # sample variance for each locus
  for locus = 2:ndesign
      varian[locus] = (scalea*4 + b[locus]*b[locus])/randchi2(4.0 + 1);
  end

  # sample effect for each locus
  for locus = 2:ndesign
      # unadjust y for this locus
      ycorr = ycorr + X[:, locus] * b[locus];
      rhs = dot(X[:, locus], ycorr)/vare;
      lhs = dot(X[:, locus], X[:, locus])/vare + 1.0/varian[locus];
      invlhs = 1.0/lhs;
      mn = invlhs * rhs;
      b[locus] = randn() * sqrt(invlhs) + mn;
      #adjust y for the new value of this locus
      ycorr = ycorr - X[:, locus] * b[locus];
      meanb[locus] = meanb[locus] + b[locus];
  end
end

toc()

meanb = meanb/numiter;
aHat  = X * meanb;

这两个代码比较相似，但是也有一些不同：

第一个读入的数据是二进制的，我不知道Julia如何操作，所以我就将其转为csv，然后读取。
为了防止名称重复，R中可以随意命名，但是Julia不行，所以我在Julia程序中进行了修改。
R中可以赋值，a=b，你改变a和b都没问题，但是Julia中你动了b，a也变了。语法不太一样。
Julia中向量和数组不太一样，太令人困惑了。

比较有意思的是，Julia的代码在速度上不是很突出，因为我的代码太粗糙了。我变化了marker的数目，发现Julia的运算速度大约是R的2.8倍。Julia在官网上称进行数值运算时，速度是R的100倍，但是我的没有达到这么高。

在1996年或者1997年是，我由SAS转到了ASReml进行基因组数据分析，它大约提高了1~2倍的速度，而且支持了更多的模型。

现在，又到了更换软件的时候了，由R转到Julia，特别是基因组选择方面，Julia的速度是R的3倍，Julia性能非常优秀。

R语言 Julia以及全基因组选择

猜你喜欢