GFA-package | R Documentation |

GFA does factor analysis for multiple data sets having matched observations, for exploratory or predictive data analysis.

The posterior distribution of GFA model parameters can be inferred with
function `gfa`

, once the priors have been defined with
`getDefaultOpts`

. The priors are widely customizable, with two
recommended setups: (i) dense group-sparse components (default; similar to
package CCAGFA that provides variational Bayesian inference for the same
model) and (ii) components interpretable as biclusters. It is recommended
to preprocess the data with function `normalizeData`

.
Functions are provided for predicting missing data, choosing a prior for
the residual noise, identifying robust components and
visualizing the inferred model. A simple toy example of the pipeline
is provided as demo(GFApipeline), and a more elaborate one as
demo(GFAexample). Finally, the experiment presented in (Bunte, Leppaaho,
Saarinen and Kaski: Sparse group factor analysis for biclustering of
multiple data sources, Bioinformatics, 32(16):2457–2463, 2016) can
be replicated with demo(GFAdream). Most of the computational complexity
of the package is related to matrix operations, which can be parallelized
inherently by using e.g. OpenBLAS libraries.

```
#Data generation
X <- matrix(rnorm(20*3),20,3) #Latent variables
W <- matrix(rnorm(30*3),30,3) #Projection matrix
Y <- tcrossprod(X,W) + matrix(rnorm(20*30),20,30) #Observations
Y <- sweep(Y, MARGIN=2, runif(30), "+") #Feature means
Y <- list(Y[,1:10], Y[,11:30]) #Data grouping
#Model inference and visualization
norm <- normalizeData(Y, type="center") #Centering
opts <- getDefaultOpts() #Model options
#Fast runs for the demo, default options recommended in general
opts[c("iter.burnin", "iter.max")] <- c(500, 1000)
res <- gfa(norm$train, K=5, opts=opts) #Model inference
rec <- reconstruction(res) #Reconstruction
recOrig <- undoNormalizeData(rec, norm) #... to original space
vis <- visualizeComponents(res, Y, norm) #Visualization
```

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