| name | bioconductor-pcamethods |
| description | Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA o |
| when_to_use | Use when: Handling Outliers: When performing PCA on metabolite or microarray data corrupted with extreme values using a robust singular value decomposition (method="robustPca").; Missing Value Imputation: When estimating missing values (NA) in incomplete datasets using Probabilistic PCA (method="ppca") or Nipals (method="nipals").; Algorithm Comparison: When comparing multiple PCA formulations (e.g., "svd",. Not for: Sparse Single-Cell Data: For single-cell RNA-seq data with high dropout rates, use scry or GLM-PCA instead because they model the count distribution directly.; Complete Datasets: For standard, complete matrices without missing values or outliers, use |
| user-invocable | false |
pcaMethods
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 2.4.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: Biobase
- Imports: BiocGenerics, Rcpp, MASS
- System requirements: Rcpp
- Install:
BiocManager::install("pcaMethods")
When to Use
- Handling Outliers: When performing PCA on metabolite or microarray data corrupted with extreme values using a robust singular value decomposition (
method="robustPca").
- Missing Value Imputation: When estimating missing values (
NA) in incomplete datasets using Probabilistic PCA (method="ppca") or Nipals (method="nipals").
- Algorithm Comparison: When comparing multiple PCA formulations (e.g., "svd", "ppca", "bpca", "svdImpute", "nipals", "nlpca") on the same
ExpressionSet or matrix using a unified pca() interface.
When NOT to Use
- Sparse Single-Cell Data: For single-cell RNA-seq data with high dropout rates, use
scry or GLM-PCA instead because they model the count distribution directly.
- Complete Datasets: For standard, complete matrices without missing values or outliers, use base R's
prcomp or irlba because they are faster and computationally lighter.
Data Requirements
- Input Format: A numeric matrix,
data.frame, or an ExpressionSet (from the Biobase package).
- Missing Values: Missing data should be represented as
NA.
- Normalization: Data is typically mean-centered and scaled (e.g., using the
prep() function with scale="none" and center=TRUE) prior to running PCA.
Key Parameters
- method ("svd"): The PCA algorithm to use (e.g., "svd", "robustPca", "ppca", "bpca", "svdImpute", "nipals", "nlpca").
- nPcs (2): Number of principal components to calculate.
- center (TRUE): Logical indicating whether to mean-center the variables.
- scale ("none"): Scaling method to apply in the
prep() function.
- maxSteps (1000): Maximum number of iterations for iterative algorithms like NLPCA.
- fold (10): Number of folds for cross-validation when using the
Q2() function.
- evalPcs (1:5): The principal components to evaluate when estimating error with
kEstimate().
- em ("nrmsep"): The error metric to use during cross-validation in
kEstimate().
Best Practices
- Pre-processing: Always pre-process data using the
prep() function to handle centering and scaling before running pca().
- Determine Components: Use cross-validation via the
Q2() function to evaluate the optimal number of principal components to retain.
- Outlier Management: When dealing with extreme outliers, set them to
NA and use method="ppca", or use method="robustPca" directly on the complete data.
- Error Estimation: Estimate imputation error using
kEstimate() with cross-validation to ensure the chosen method and number of PCs are appropriate.
Common Pitfalls
- Failing to Center Before Outlier Injection: Creating artificial outliers for testing on un-centered data, which shifts original means and prevents objective comparison. Fix: Use
scale(..., center=TRUE, scale=FALSE) before injecting outliers.
- PPCA Instability: PPCA failing to converge or misestimating values in rare cases. Fix: Use a
while loop checking if sum(abs(Q2)) > 1 to re-run the Q2() function if PPCA becomes unstable.
- Non-Numeric Input: Passing a matrix containing character columns or factors to
pca(), causing the algorithms to fail. Fix: Ensure the input matrix or ExpressionSet contains only numeric values.
Alternatives
- missMDA: For PCA and imputation on incomplete datasets using regularized iterative PCA.
- impute: For K-Nearest Neighbors imputation (
impute.knn) of missing microarray data.
- FactoMineR: For advanced exploratory multivariate data analysis on complete datasets.
- irlba: For fast, truncated SVD and PCA on extremely large, sparse matrices.
Citations
- Liu, L., Hawkins, D.M., Ghosh, S. and Young, S.S. Robust singular value decomposition analysis of microarray data. PNAS, 2003;100:13167–13172.
- Hawkins, D.M., Liu, L. and Young, S.S. Robust Singular Value Decomposition. National Institute of Statistical Sciences, 2001, Tech Report 122.
References
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