extract_bic {fabia} | R Documentation |
extract_bic
: R implementation of extract_bic
.
extract_bic(L,Z,thresZ=0.5,thresL=NULL,lapla=NULL,Psi=NULL)
L |
loading, left matrix. |
Z |
factor, right matrix. |
thresZ |
threshold for sample belonging to bicluster (default 0.5). |
thresL |
threshold for loading belonging to bicluster (if not given it is estimated). |
lapla |
inverse variance of the variational approximation for each sample and each factor. |
Psi |
noise variance vector for observations where independent noise is asumed. |
Essentially the model is the sum of outer products of 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
The hidden dimension p is used for kmeans clustering of L_i and Z_i .
U is the Gaussian noise with a diagonal covariance matrix
which entries are given by Psi
.
The Z is locally approximated by a Gaussian with inverse
variance given by lapla
.
Using these values we can computer for each j the variance Z_i given x_j. Here
x_j = L z_j + u_j
This variance can be used to determine the information content of a bicluster.
The L_i and Z_i are used to extract the bicluster i, where a threshold determines which observations and which samples belong the the bicluster.
In bic
the biclusters are extracted according to the
largest absolute values of the component i, i.e.
the largest values of L_i and the
largest values of Z_i . The factors Z_i
are normalized to variance 1.
The components of bic
are
binp
, bixv
,
bixn
, biypv
, and biypn
.
binp
give the size of the bicluster: number observations and
number samples.
bixv
gives the values of the extracted
observations that have absolute
values above a threshold. They are sorted.
bixn
gives the extracted observation names (e.g. gene names).
biypv
gives the values of the extracted samples that have
absolute values above a threshold. They are sorted.
biypn
gives the names of the extracted samples (e.g. sample names).
In bicopp
the opposite cluster to the biclusters are
give. Opposite means that the negative pattern is present.
The components of opposite clusters bicopp
are
binn
, bixv
,
bixn
, biypnv
, and biynn
.
binp
give the size of the opposite bicluster: number observations and
number samples.
bixv
gives the values of the extracted
observations that have absolute
values above a threshold. They are sorted.
bixn
gives the extracted observation names (e.g. gene names).
biynv
gives the values of the opposite extracted samples that have
absolute values above a threshold. They are sorted.
biynn
gives the names of the opposite
extracted samples (e.g. sample names).
That means the samples are divided into two groups where one group shows large positive values and the other group has negative values with large absolute values. That means a observation pattern can be switched on or switched off relative to the average value.
numn
gives the indexes of bic
with components:
numng
= bix
and numnp
= biypn
.
numn
gives the indexes of bicopp
with components:
numng
= bix
and numnn
= biynn
.
Implementation in R.
bic |
extracted biclusters. |
numn |
indexes for the extracted biclusters. |
bicopp |
extracted opposite biclusters. |
numnopp |
indexes for the extracted opposite biclusters. |
avini |
average over j of the variance Z_i given x_j. |
ini |
for each j the variance Z_i given x_j. |
Sepp Hochreiter
fabi
,
fabia
,
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
,
myImagePlot
,
PlotBicluster
,
Breast_A
,
DLBCL_B
,
Multi_A
,
fabiaDemo
,
fabiaVersion
#--------------- # 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,20,0.1,1.0,1.0,3) rEx <- extract_bic(resEx$L,resEx$Z,lapla=resEx$lapla,Psi=resEx$Psi) rEx$bic[1,] rEx$bic[2,] rEx$bic[3,] ## Not run: #--------------- # DEMO1 #--------------- dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5, of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0, sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0) X <- dat[[1]] Y <- dat[[2]] resToy <- fabia(X,200,0.4,1.0,1.0,13) rToy <- extract_bic(resToy$L,resToy$Z,lapla=resToy$lapla,Psi=resToy$Psi) rToy$avini rToy$bic[1,] rToy$bic[2,] rToy$bic[3,] #--------------- # DEMO2 #--------------- data(Breast_A) X <- as.matrix(XBreast) resBreast <- fabia(X,200,0.1,1.0,1.0,5) rBreast <- extract_bic(resBreast$L,resBreast$Z,lapla=resBreast$lapla,Psi=resBreast$Psi) rBreast$avini rBreast$bic[1,] rBreast$bic[2,] rBreast$bic[3,] ## End(Not run)