  Partial least squares, as a dimension reduction method, has become
increasingly important for its ability to deal with problems with a large
number of variables. Since noisy variables may weaken the performance of the
model, the sparse partial least squares (SPLS) technique has been proposed to
identify important variables and generate more interpretable results. However,
the small sample size of a single dataset limits the performance of
conventional methods. An effective solution comes from gathering information
from multiple comparable studies. The integrative analysis holds an important
status among multi-datasets analyses. The main idea is to improve estimation
results by assembling raw datasets and analyzing them jointly. In this paper,
we develop an integrative SPLS (iSPLS) method using penalization based on the
SPLS technique. The proposed approach consists of two penalties. The first
penalty conducts variable selection under the context of integrative analysis;
The second penalty, a contrasted one, is imposed to encourage the similarity of
estimates across datasets and generate more reasonable and accurate results.
Computational algorithms are provided. Simulation experiments are conducted to
compare iSPLS with alternative approaches. The practical utility of iSPLS is
shown in the analysis of two TCGA gene expression data.
