R语言 fabia包 fabias()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-2-25 17:29:31

fabias(fabia)
fabias()所属R语言包：fabia

                                    Factor Analysis for Bicluster Acquisition: Sparseness Projection (FABIAS)
                                       因子分析Bicluster收购：稀疏投影（FABIAS）

                                       译者：生物统计家园网机器人LoveR

描述----------Description----------

fabias: C implementation of  fabias.
fabias：C的fabias实施。

用法----------Usage----------

fabias(X,p=5,alpha=0.6,cyc=500,spz=0.5,non_negative=0,random=1.0,center=2,norm=1,lap=1.0,nL=0,lL=0,bL=0)

参数----------Arguments----------

参数：X
the data matrix.
数据矩阵。

参数：p
number of hidden factors = number of biclusters; default = 5.
隐性因素数=的biclusters;默认值= 5。

参数：alpha
sparseness loadings (0.1 - 1.0); default = 0.1.
稀疏负荷（0.1  -  1.0）;默认值= 0.1。

参数：cyc
number of iterations; default = 500.
迭代次数，默认为500。

参数：spz
sparseness factors (0.5 - 2.0); default = 0.5 (Laplace).
稀疏的因素（0.5  -  2.0）;默认值= 0.5（拉普拉斯）。

参数：non_negative
Non-negative factors and loadings if non_negative > 0; default = 0.
如果非消极因素和负荷non_negative> 0;默认= 0。

参数：random
<=0: by SVD, >0: random initialization of loadings in [-random,random]; default = 1.0.
用SVD <= 0：0：[随机，随机];默认值= 1.0负荷的随机初始化。

参数：center
data centering: 1 (mean), 2 (median), > 2 (mode), 0 (no); default = 2.
数据定心：1（平均），2（中位数），2（模式），0（无）;默认为2。

参数：norm
data normalization:  1 (0.75-0.25 quantile), >1 (var=1), 0 (no); default = 1.
数据标准化：1（0.75-0.25位数），1（VAR = 1），0（无）;默认值= 1。

参数：lap
minimal value of the variational parameter; default = 1.0.
极小值变分参数的默认值= 1.0;

参数：nL
maximal number of biclusters at which a row element can participate; default = 0 (no limit)
最大数量时biclusters行元素可以参与;默认= 0（无限制）

参数：lL
maximal number of row elements per bicluster; default = 0 (no limit)
最大数量每bicluster行元素;默认= 0（没有限制）

参数：bL
cycle at which the nL or lL maximum starts; default = 0 (start at the beginning)
NL或LL最大的周期开始;默认值= 0（从头开始）

Details

详情----------Details----------

Biclusters are found by sparse factor analysis where both the factors and the loadings are sparse.
biclusters发现稀疏的因子分析的因素和负荷稀疏。

Essentially the model is the sum of outer products of vectors:
模型本质上是向量外产品的总和：

where the number of summands p is the number of biclusters. The matrix factorization is
加数p是的biclusters数量。矩阵分解

Here λ_i are from R^n, z_i from R^l, L from R^{n \times p}, Z from R^{p \times l}, and X, U from R^{n \times l}.
这里λ_iR^n，z_iR^l，LR^{n \times p}，ZR^{p \times l} X，UR^{n \times l}。

If the nonzero components of the sparse vectors are grouped together then the outer product results in a matrix with a nonzero block and zeros elsewhere.
如果稀疏向量的非零组件被组合在一起，然后在同一个非零块矩阵和零别处外产品的结果。

The model selection is performed by a variational approach according to Girolami 2001 and Palmer et al. 2006.
模型的选择是由变分法，根据2001年和Girolami Palmer等人。 2006年。

The prior has finite support, therefore after each update of the loadings they are projected to the finite support. The projection is done according to Hoyer, 2004: given an l_1-norm and an l_2-norm minimize the Euclidean distance to the original vector (currently the l_2-norm is fixed to 1). The projection is a convex quadratic problem which is solved iteratively where at each iteration at least one component is set to zero. Instead of the  l_1-norm a sparseness measurement is used which relates the l_1-norm to the l_2-norm.
前有限的支持，因此，在每次更新的负荷，他们预计将有限的支持。投影是根据2004年霍耶：目前l_1，规范的范数减少到原来的向量（欧氏距离l_2l_2，规范固定为1 ）。投影是一个凸二次其中至少一个组件，在每一次迭代设置为零，这是解决反复的问题。而不是l_1-规范使用1稀疏测量涉及l_1，规范l_2-范。

The code is implemented in C.
在C代码实现

值----------Value----------

参数：
object of the class Factorization. Containing LZ (estimated noise free data L  Z), L (loadings  L), Z (factors Z), U (noise X-LZ), center (centering vector), scaleData (scaling vector), X (centered and scaled data X), Psi (noise variance σ), lapla (variational parameter), avini (the information which the factor z_{ij} contains about x_j averaged over j) xavini (the information which the factor z_{j} contains about x_j) ini (for each j the information which the factor z_{ij} contains about x_j).
对象类Factorization。含有LZ（估计无噪音的数据L  Z）L（负荷L）Z（因素Z）U （噪音X-LZ）center（定心向量），scaleData（缩放向量），X（中心和缩放的数据X）<X >（噪声方差Psi）σ（变参数），lapla（因素avini约z_{ij}平均包含的信息<X >）x_j（信息因素j包含有关xavini）z_{j}（每x_j的信息因素ini包含有关j）。

作者（S）----------Author(s)----------

Sepp Hochreiter

参考文献----------References----------

‘FABIA: Factor Analysis for Bicluster Acquisition’, Bioinformatics 26(12):1520-1527, 2010. http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btq227
‘A Variational Method for Learning Sparse and Overcomplete Representations’, Neural Computation 13(11): 2517-2532, 2001.
‘Variational EM algorithms for non-Gaussian latent variable models’, Advances in Neural Information Processing Systems 18, pp. 1059-1066, 2006.
‘Non-negative Matrix Factorization with Sparseness Constraints’, Journal of Machine Learning Research 5:1457-1469, 2004.

参见----------See Also----------

fabia, fabias, fabiap, spfabia, fabi, fabiasp, mfsc, nmfdiv, nmfeu, nmfsc, plot, extractPlot, extractBic, plotBicluster, Factorization, projFuncPos, projFunc, estimateMode, makeFabiaData, makeFabiaDataBlocks, makeFabiaDataPos, makeFabiaDataBlocksPos, matrixImagePlot, summary, show, showSelected, fabiaDemo, fabiaVersion
fabia，fabias，fabiap，spfabia，fabi，fabiasp，mfsc，nmfdiv，nmfeu，nmfsc，plot，extractPlot，extractBic，plotBicluster，Factorization，projFuncPos，projFunc ，estimateMode，makeFabiaData，makeFabiaDataBlocks，makeFabiaDataPos，makeFabiaDataBlocksPos，matrixImagePlot，summary，show showSelected，fabiaDemo，fabiaVersion

举例----------Examples----------

#---------------[---------------]
# TEST[试验]
#---------------[---------------]

dat <- makeFabiaDataBlocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]

resEx <- fabias(X,3,0.6,50)

## Not run: [＃无法运行：]
#-----------------[-----------------]
# DEMO1: Toy Data[DEMO1：玩具资料]
#-----------------[-----------------]

n = 1000
l= 100
p = 10

dat <- makeFabiaDataBlocks(n = n,l= l,p = p,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
ZC <- dat[[3]]
LC <- dat[[4]]

gclab <- rep.int(0,l)
gllab <- rep.int(0,n)
clab <- as.character(1:l)
llab <- as.character(1:n)
for (i in 1:p){
for (j in ZC[i]){
   clab[j] <- paste(as.character(i),"_",clab[j],sep="")
}
for (j in LC[i]){
   llab[j] <- paste(as.character(i),"_",llab[j],sep="")
}
gclab[unlist(ZC[i])] <- gclab[unlist(ZC[i])] + p^i
gllab[unlist(LC[i])] <- gllab[unlist(LC[i])] + p^i
}

groups <- gclab

#### FABIAS[＃＃＃FABIAS]

resToy2 <- fabias(X,13,0.6,400)

extractPlot(resToy2,ti="FABIAS",Y=Y)

raToy2 <- extractBic(resToy2)

if ((raToy2$bic[[1]][1]>1) && (raToy2$bic[[1]][2])>1) {
plotBicluster(raToy2,1)
}
if ((raToy2$bic[[2]][1]>1) && (raToy2$bic[[2]][2])>1) {
plotBicluster(raToy2,2)
}
if ((raToy2$bic[[3]][1]>1) && (raToy2$bic[[3]][2])>1) {
plotBicluster(raToy2,3)
}
if ((raToy2$bic[[4]][1]>1) && (raToy2$bic[[4]][2])>1) {
plotBicluster(raToy2,4)
}

colnames(resToy2@X) <- clab

rownames(resToy2@X) <- llab

plot(resToy2,dim=c(1,2),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy2,dim=c(1,3),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy2,dim=c(2,3),label.tol=0.1,col.group = groups,lab.size=0.6)

#------------------------------------------[------------------------------------------]
# DEMO2: Laura van't Veer's gene expression  [DEMO2：劳拉·范特韦埃尔的基因表达]
#       data set for breast cancer [乳腺癌数据集]
#------------------------------------------[------------------------------------------]

avail <- require(fabiaData)

if (!avail) {
message("")
message("")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃“）]
message("Package 'fabiaData' is not available: please install.")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃“）]
} else {

data(Breast_A)

X <- as.matrix(XBreast)

resBreast2 <- fabias(X,5,0.6,300)

extractPlot(resBreast2,ti="FABIAS Breast cancer(Veer)")

raBreast2 <- extractBic(resBreast2)

if ((raBreast2$bic[[1]][1]>1) && (raBreast2$bic[[1]][2])>1) {
plotBicluster(raBreast2,1)
}
if ((raBreast2$bic[[2]][1]>1) && (raBreast2$bic[[2]][2])>1) {
plotBicluster(raBreast2,2)
}
if ((raBreast2$bic[[3]][1]>1) && (raBreast2$bic[[3]][2])>1) {
plotBicluster(raBreast2,3)
}
if ((raBreast2$bic[[4]][1]>1) && (raBreast2$bic[[4]][2])>1) {
plotBicluster(raBreast2,4)
}

plot(resBreast2,dim=c(1,2),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(1,3),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(1,4),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(1,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(2,3),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(2,4),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(2,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(3,4),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(3,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast2,dim=c(4,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)

}

#-----------------------------------[-----------------------------------]
# DEMO3: Su's multiple tissue types[DEMO3：苏的多种组织类型]
#       gene expression data set [基因表达数据集]
#-----------------------------------[-----------------------------------]

avail <- require(fabiaData)

if (!avail) {
message("")
message("")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃“）]
message("Package 'fabiaData' is not available: please install.")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃“）]
} else {

data(Multi_A)

X <- as.matrix(XMulti)

resMulti2 <- fabias(X,5,0.6,300)

extractPlot(resMulti2,ti="FABIAS Multiple tissues(Su)")

raMulti2 <- extractBic(resMulti2)

if ((raMulti2$bic[[1]][1]>1) && (raMulti2$bic[[1]][2])>1) {
plotBicluster(raMulti2,1)
}
if ((raMulti2$bic[[2]][1]>1) && (raMulti2$bic[[2]][2])>1) {
plotBicluster(raMulti2,2)
}
if ((raMulti2$bic[[3]][1]>1) && (raMulti2$bic[[3]][2])>1) {
plotBicluster(raMulti2,3)
}
if ((raMulti2$bic[[4]][1]>1) && (raMulti2$bic[[4]][2])>1) {
plotBicluster(raMulti2,4)
}

plot(resMulti2,dim=c(1,2),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(1,3),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(1,4),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(1,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(2,3),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(2,4),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(2,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(3,4),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(3,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti2,dim=c(4,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)

}

#-----------------------------------------[-----------------------------------------]
# DEMO4: Rosenwald's diffuse large-B-cell[DEMO4：罗森沃尔德的弥漫性大B单元]
#       lymphoma gene expression data set [淋巴瘤基因表达数据集]
#-----------------------------------------[-----------------------------------------]

avail <- require(fabiaData)

if (!avail) {
message("")
message("")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃“）]
message("Package 'fabiaData' is not available: please install.")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃“）]
} else {

data(DLBCL_B)

X <- as.matrix(XDLBCL)

resDLBCL2 <- fabias(X,5,0.6,300)

extractPlot(resDLBCL2,ti="FABIAS Lymphoma(Rosenwald)")

raDLBCL2 <- extractBic(resDLBCL2)

if ((raDLBCL2$bic[[1]][1]>1) && (raDLBCL2$bic[[1]][2])>1) {
plotBicluster(raDLBCL2,1)
}
if ((raDLBCL2$bic[[2]][1]>1) && (raDLBCL2$bic[[2]][2])>1) {
plotBicluster(raDLBCL2,2)
}
if ((raDLBCL2$bic[[3]][1]>1) && (raDLBCL2$bic[[3]][2])>1) {
plotBicluster(raDLBCL2,3)
}
if ((raDLBCL2$bic[[4]][1]>1) && (raDLBCL2$bic[[4]][2])>1) {
plotBicluster(raDLBCL2,4)
}

plot(resDLBCL2,dim=c(1,2),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(1,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(1,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(1,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(2,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(2,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(2,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(3,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(3,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL2,dim=c(4,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)

}

## End(Not run)[＃结束（不运行）]

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