# asmbPLS:
Predicting and Classfying Patient Phenotypes with Multi-omics Data

Runzhi Zhang, Susmita Datta

## Description

Adaptive Sparse Multi-block Partial Least Square (asmbPLS), a
supervised algorithm, is an extension of the smbPLS, which allows
different quantiles to be used in different blocks of different PLS
components to decide the proportion of features to be retained. The best
combinations of quantiles can be chosen from a set of user-defined
quantile combinations by cross-validation. By doing this, asmbPLS
enables us to do the feature selection for different blocks, and the
selected features can then be further used to predict the outcome. For
example, in biomedical applications, clinical covariates plus different
types of omics data such as microbiome, metabolome, mRNA data,
methylation data, and CNV data might be predictive for patientsâ€™
outcomes such as survival time or response to therapy. Different types
of data could be put in different blocks along with survival time to fit
the asmbPLS model. The fitted model can then be used to predict the
survival of the new samples with the corresponding clinical covariates
and omics data.

In addition, Adaptive Sparse Multi-block Partial Least Square
Discriminant Analysis (asmbPLS-DA) is also included, which extends
asmbPLS for classifying the categorical outcome.

## R package installation

`devtools::install_github("RunzhiZ/asmbPLS")`

If you want to build the vignettes, you should include
`build_vignettes = TRUE`

.

`devtools::install_github("RunzhiZ/asmbPLS", build_vignettes = TRUE, force = TRUE)`

### Common errors for MAC users:

`ld: library not found for -lgfortran`

Solution for **error 1**: install the required tools
https://mac.r-project.org/tools/

`clang: error: unsupported option '-fopenmp'`

Possible solution for **error 2**:
https://stackoverflow.com/questions/43555410/enable-openmp-support-in-clang-in-mac-os-x-sierra-mojave

## Installation error report

If you have more errors installing the R package, please report to
runzhi.zhang@ufl.edu

## Tutorial

Click here to view the
tutorial for the R package

## References

- Zhang R, Datta S: asmbPLS: Adaptive Sparse Multi-block Partial Least
Square for Survival Prediction using Multi-Omics Data. bioRxiv
2023:2023.2004.2003.535442.