PLS-DA与PCA降维结合的实现流程及步骤合理性咨询

Hey there! Let’s walk through the PLS-DA workflow clearly, and check if your current steps have any conceptual missteps. First, let’s recap your setup to make sure we’re on the same page:

• Your input matrix X is 188×528 (188 samples, 465 features – I assume that’s a small typo, but the core logic holds either way)
• Response variable Y is a 188-length binary vector with labels 1 and 2
• PCA on X returned 187 non-zero eigenvalues

一、Standard PLS-DA Workflow (Step-by-Step) #

Let’s start with the standard, validated process for PLS-DA to set a baseline:

• Step 1: Preprocess Your Data
  • First, standardize X: almost always mean-center each feature, then scale to unit variance (unless your features have inherent, meaningful scales like absolute concentrations). PLS is highly sensitive to feature scales, so this step is non-negotiable for reliable results.
  • For your binary Y, the 1/2 coding is fine – you don’t need one-hot encoding for most PLS-DA implementations.
• Step 2: Train the PLS-DA Model
  • Don’t pre-project X with PCA first – that’s the big one. PLS-DA is designed to extract latent variables (LVs) that maximize covariance between X and Y directly. PCA only captures variance in X regardless of your response, so pre-projecting breaks the link PLS needs to learn class-separating patterns.
  • The 187 non-zero PCA eigenvalues just tell you X has a rank of 187 (which makes sense: with 188 samples, the maximum possible rank of X is 188, but real-world data is almost always rank-deficient by a small margin). This is a descriptive detail, not a directive for preprocessing.
  • The critical choice here is picking the optimal number of LVs. Use cross-validation (e.g., 10-fold) to plot prediction error vs. number of LVs – pick the smallest number where the error stops dropping significantly. You can also use permutation testing to confirm your model isn’t overfitting random noise.
• Step 3: Validate the Model
  • Use classification metrics like accuracy, sensitivity, specificity, or AUC-ROC (great for binary tasks) on a held-out test set (if you split your data first) or cross-validation results.
  • Calculate Variable Importance in Projection (VIP) scores to identify which features are driving the class separation – this is key for interpreting your results.
• Step 4: Project & Interpret
  • Once your model is finalized, project samples onto the first few PLS LVs to make score plots – these will show you how well the model separates your two classes.

二、Checking Your Current Operations #

From what you’ve shared, the main conceptual gap is the plan to pre-project X using PCA components. As I noted above, this is unnecessary and can hurt your model’s performance because you’re discarding the variance in X that’s tied to your class labels before PLS-DA can use it.

If you were planning to run PLS-DA on the PCA-reduced X, that’s not the standard approach. PLS-DA already handles dimensionality reduction by selecting LVs that are relevant to Y – let it do its job directly on the preprocessed original X.

Quick Adjustments for Your Workflow #

• Ditch the PCA projection step for X
• Start with scaling/centering X
• Train PLS-DA on the preprocessed X and your binary Y
• Use cross-validation to pick the optimal number of LVs
• Validate with standard classification metrics and interpret using VIP scores/score plots

内容的提问来源于stack exchange，提问作者ITA