## Classification with O-PLS-DA

Partial least squares (PLS) is a versatile algorithm which can be used to predict either continuous or discrete/categorical variables. Classification with PLS is termed PLS-DA, where the DA stands for discriminant analysis. The PLS-DA algorithm has many favorable properties for dealing with multivariate data; one of the most important of which is how variable collinearity is dealt with, and the model’s ability to rank variables’ predictive capacities within a multivariate context. Orthogonal signal correction PLS-DA or O-PLS-DA is an extension of PLS-DA which seeks to maximize the explained variance between groups in a single dimension or the first latent variable (LV), and separate the within group variance (orthogonal to classification goal) into orthogonal LVs. The variable loadings and/or coefficient weights from a validated O-PLS-DA model can be used to rank all variables with respect to their performance for discriminating between groups. This can be used part of a dimensional reduction or feature selection task which seek to identify the top predictors for a given model.

Like with most predictive modeling or forecasting tasks, model validation is a critical requirement. Otherwise the produced models maybe overfit or perform no better than coin flips. Model validation is the process of defining the models performance, and thus ensuring that the model’s internal variable rankings are actually informative.

Below is a demonstration of the development and validation of an O-PLS-DA multivariate classification model for the famous Iris data set.

**O-PLS-DA model validation Tutorial**

**Data pretreatment and preparation****Model optimization****Permutation testing****Internal cross-validation****External cross-validation**

The Iris data only contains 4 variables, but the sample sizes are favorable for demonstrating a two tiered testing and training scheme (internal and external cross-validation). However O-PLS really shines when building models with many correlated variables (coming soon).

## Orthogonal Signal Correction Partial Least Squares (O-PLS) in R

I often need to analyze and model very wide data (variables >>>samples), and because of this I gravitate to robust yet relatively simple methods. In my opinion partial least squares (PLS) is a particular useful algorithm. Simply put, PLS is an extension of principal components analysis (PCA), a non-supervised method to maximizing variance explained in X, which instead maximizes the covariance between X and Y(s). Orthogonal signal correction partial least squares (O-PLS) is a variant of PLS which uses orthogonal signal correction to maximize the explained covariance between X and Y on the first latent variable, and components >1 capture variance in X which is orthogonal (or unrelated) to Y.

Because R does not have a simple interface for O-PLS, I am in the process of writing a package, which depends on the existing package pls.

Today I wanted to make a small example of conducting O-PLS in R, and at the same time take a moment to try out the R package knitr and RStudio for markdown generation.

You can take a look at the ** O-PLS/O-PLS-DA tutorials.**

I was extremely impressed with ease of using knitr and generating markdown from code using RStudio. A big thank you to Yihui Xie and the RStudio developers (Joe Cheng). This is an amazing capability which I will make much more use of in the future!

## Multivariate Modeling Strategy

**The following** is an example of a clinical study aimed at identification of circulating metabolites related to disease phenotype or grade/severity/type (tissue histology, 4 classifications including controls).

**The challenge** is to make sense of 300 metabolic measurements for 300 patients.

**The goal** is to identify metabolites related to disease, while accounting covariate meta data such as gender and smoking.

**The steps**

**Exploratory Data Analysis**– principal components analysis (**PCA**)- Statistical Analysis –
**covariate adjustment**and analysis of covariance or ANCOVA **Multivariate Classification Modeling**– orthogonal signal correction partial least squares discriminant analysis (**O-PLS-DA**)

Data exploration is useful for getting an idea of the data structure and to identify unusual or unexpected trends.

PCA above conducted on autoscaled data (300 samples and 300 measurements) was useful for identifying an interesting 2-cluster structure in the sample scores (top left). Unfortunately the goal of the study, disease severity, could not explain this pattern (top center). An unknown covariate was identified causing the observed clustering of samples (top right).

Next various covariate adjustment strategies were applied to the data and evaluated using the unsupervised PCA (bottom left) and the supervised O-PLS-DA.

Even after the initial covariate adjustment for the 2-cluster effect there remained a newly visible covariate (top ;left), the source of which could not me linked to the meta data.

After data pre-treatment and evaluation of testing strategies (top right) the next challenge is to select the best classifiers of disease status. Feature selection was undertaken to improve model performance and simplify its performance.

Variable correlation with O-PLS-DA sample scores and magnitude of variable loading in the model were used to select from the the full feature set (~300) only 64 (21%) top features which explained most of the models classification performance.

**In conclusion **preliminary data exploration was used to identify an unknown source of variance which negatively affected the experimental goal to identify metabolic predictors of disease severity. Multivariate techniques, PCA and O-PLS-DA, were used to identify an optimal data covariate adjustment and hypothesis testing strategy. Finally O-PLS-DA modeling including feature selection, training/testing validations (n=100) and permutation testing (n=100) were used to identify the top features (21%) which were most predictive of patients classifications as displaying or not displaying the disease phenotype.

## Viewing Time-Dimensional Data (in multivariate space)

The idea is that we have collected information about 30 samples at 4 intervals for 200 variables. This makes 30 * 4 * 200 = 24,000 data points!

That is a lot to keep track of if we want to start the data analysis by looking at sample-wise (30) differences in variables (200) which are also dependent on time (4).

One idea is to use orthogonal signal correction partial least squares (O-PLS) to ask the question:

1) what is the most conserved linear ordering of my data based on

2) description of my data = 3 (group)s of samples at 4 (points in time) and the starting point or t= 0 (so a total of 5 points in time).

Here is an example O-PLS scores plot for the samples (30*5 = 150 ) with polygons around the boundaries of each unique sample-group classification ( 3 * 5 = 15).

We can try to summarize the position of each group in this multivariate space (15 * 200) by plotting each groups median score and standard error for the first two O-PLS latent variables (LVs).

Above is an enticing representation of the time-course differences between 3 groups of samples for 5 time measurement points (t= 0, 30, 60, 90 and 120 minutes). Now that we have established how our samples look based on 200 measurements or variables we can examine the variable loadings for this model.

Above the loadings or relative contribution of each variable to the description of the samples is plotted for O-PLS LV1 and 2. Based on the position of the variables in the x-axis (LV1) we can say something about their relative changes in time (because O-PLS samples scores are also distributed in the x-axis with respect with time), and the variable LV2 loading (y-axis) can be used to describe changes/differences between the groups (note sample group classification pattern in the y-axis (LV2) which is independent of the change in time (x-axis, LV1).

Above we can visualize a how the sample and variable descriptions are related. For instance variables far left in the loadings (FA) start out relatively increased and then decrease as samples position increases to the right. Analogously as time increases there is an increase in the majority of variables (note the large cloud of loadings on LV1 (x-axis above)).

Another interesting thing to try is to visualize the change in groups scores which are independent of time = 0 (subtract t=0 abundance for 200 variables from t = 30, 60, 90 and 120 minute time-points on a sample-wise basis).

Above are a baseline (t= 0) normalized changes in time (above left, point color) for three groups of samples (above left, point shape). As before we can study the relationship between samples and variables on a multivariate basis by comparing the samples scores (position in LV 1 and LV2) to variable loadings.

This process (O-PLS) can be helpful for ranking the original 200 variables in two dimensions (2 lists)

1) with respect to change with time (x-axis)

2) difference between groups (y-axis).

It is interesting to note that without baseline adjustment, the group young NGT has the lowest starting FA (group scores at t= 0 are to the right of the other two groups). The relative differences between group t =0 and t = 120 positions can be used to visualize the change in FA over time (decrease, note negative loading in LV1 ).

Finally we can try to connect our multivariate observations with the easily interpretable visualizations of a single variable ( FA baseline adjusted), as a box plot representing the medians (horizontal line center of box plot) and 25-75th qantiles (rectangle top and bottom boundaries ) for the 3 groups over 4 time points.

The box plot visualization above captures a similar trend in the relative position in groups as the one we previously described using all 200 variables. This make sense given the extreme loading observed for FA, and therefore the implied contribution (influence) of this variable on the observed distribution of the sample scores.