fabia {fabia}R Documentation

Factor Analysis for Bicluster Acquisition: Laplace Prior (FABIA)

Description

fabia: C implementation of fabia.

Usage


fabia(X,cyc,alpha,spl,spz,p,random=NULL,scale=0.0,norm=1,lap=1.0)

Arguments

X the data matrix.
cyc number of cycles to run.
alpha sparseness loadings (0.1 - 1.0).
spl sparseness prior loadings (0.5 - 4.0).
spz sparseness factors (0.5 - 4.0).
p number of hidden factors = number of biclusters.
random random initialization of loadings in [-random,random] (if not given: half of the square root of variance of component).
scale loading vectors are scaled in each iteration to the given variance. zero (default) indicates that non scaling.
norm should the data be standardized, default = 1 (yes, using mean), 2 (yes, using median).
lap minimal value of the variational parameter, default = 1.

Details

Biclusters are found by sparse factor analysis where both the factors and the loadings are sparse.

Essentially the model is the sum of outer products of sparse vectors. The number of summands p is the number of biclusters.

X = L Z + U

X = sum_{i=1}^{p} L_i (Z_i )^T + U

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.

The model selection is performed by a variational approach according to Girolami 2001 and Palmer et al. 2006.

We included a prior on the parameters and minimize a lower bound on the posterior of the parameters given the data. The update of the loadings includes an additive term which pushes the loadings toward zero (Gaussian prior leads to an multiplicative factor).

The code is implemented in C using the Rcpp package.

Value

LZ Estimated Noise Free Data: L Z
L Loadings: L
Z Factors: Z
Psi Noise variance: σ
lapla Variational parameter

Author(s)

Sepp Hochreiter

References

Mark Girolami, ‘A Variational Method for Learning Sparse and Overcomplete Representations’, Neural Computation 13(11): 2517-2532, 2001.

J. Palmer, D. Wipf, K. Kreutz-Delgado, B. Rao, ‘Variational EM algorithms for non-Gaussian latent variable models’, Advances in Neural Information Processing Systems 18, pp. 1059-1066, 2006.

See Also

fabi, fabiap, fabias, fabiasp, mfsc, nmfdiv, nmfeu, nmfsc, nprojfunc, projfunc, make_fabi_data, make_fabi_data_blocks, make_fabi_data_pos, make_fabi_data_blocks_pos, extract_plot, extract_bic, myImagePlot, PlotBicluster, Breast_A, DLBCL_B, Multi_A, fabiaDemo, fabiaVersion

Examples


#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(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 <- fabia(X,50,0.3,1.0,1.0,3)

## Not run: 
#---------------
# DEMO1
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,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]]

resToy <- fabia(X,200,0.4,1.0,1.0,13)

rToy <- extract_plot(X,resToy$L,resToy$Z,ti="FABIA",Y=Y)

#---------------
# DEMO2
#---------------

data(Breast_A)

X <- as.matrix(XBreast)
resBreast <- fabia(X,200,0.1,1.0,1.0,5)

rBreast <- extract_plot(X,resBreast$L,resBreast$Z,"FABIA Breast cancer(Veer)")

#sorting of predefined labels
CBreast

#---------------
# DEMO3
#---------------

data(Multi_A)

X <- as.matrix(XMulti)

resMulti <- fabia(X,200,0.1,1.0,1.0,5)

rMulti <- extract_plot(X,resMulti$L,resMulti$Z,ti="FABIA Multiple tissues(Su)")

#sorting of predefined labels
CMulti

#---------------
# DEMO4
#---------------

data(DLBCL_B)

X <- as.matrix(XDLBCL)

resDLBCL <- fabia(X,200,0.1,1.0,1.0,5)

rDLBCL <- extract_plot(X,resDLBCL$L,resDLBCL$Z,ti="FABIA Lymphoma(Rosenwald)")

#sorting of predefined labels
CDLBCL
## End(Not run)

[Package fabia version 0.1.1 Index]