Partial Least square discriminant analysis( PLS-DA)

Question

[uvika85]

hi.. could anyone help me to understand PLS-DA. what is loading value and VIP scores? I don't have any statistics background Thanks

Answer 1

[Kevin Blighe]

You do not require a statistics background in order to understand it.

Please read this: Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra

Extracts from this:

2.2 Partial Least Squares (PLS)

PLS is a multivariate projection method for modelling a relationship between independent variables X and dependent variable(s) Y. PLS has been used in various disciplines such as chemistry, economics, medicine, psychology, and pharmaceutical science where both the independent and dependent variables are available (Blanco et al., 2000; Shao et al., 2004; Kourti, 2005).

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3.1 PCA loading

The PCA loading coefficients represent the importance of each individual feature in a reduced dimension. For example, pi2 indicates the degree of importance of the second original feature in the ith PC dimension. In general, two-dimensional loading plots (e.g., p1–p2 loading plot) provide useful information to identify important features in the first and second PC dimensions. However, such a use of PCA loading coefficients for feature selection can be extended into A PC of interest. A PCA loading-based feature selection index for the jth original feature FSIPCAj is given by

I write more on PCA, here: PCA in a RNA seq analysis (see Giovanni's answer, too)

I also write more on PCA component loadings, here: Determine the variables that drive variation among each PC

3.3 VIP in PLS-DA

PLS-DA is a special form of PLS for a classification purpose, which explains maximum separation between defined classes of samples. PLS-DA is performed by a PLS regression against a dummy matrix Y that indicates class membership (Barker and Rayens, 2003). Each sample is assigned a value of 1 or 0 depending on whether or not it belongs to a specific class. The statistical information obtained from this PLS-DA model can be used to determine which features of X are important in determining class membership of Y (Musumarra et al., 2004).

From that final extract, it is then explained that the VIP (Variable Importance in Projection) is computed using:

• a PLS-DA weight
• a percentage of the explained residual sum of squares
• a total percentage of the explained residual sum of squares

There are many of these feature selection algorithms around (I even have my own) - there is no way to state whether one is better from the other.

Kevin