Non-parametric Jackstraw for Principal Component Analysis
Significance test for association between variables and their
estimated latent variables

Statistical Inference of Variables Driving Systematic Variation
Neo Christopher Chung <nchchung@gmail.com>, John D. Storey
<jstorey@princeton.edu>, Wei Hao <whao@princeton.edu>
Significance test for association between variables
and their estimated latent variables.
Latent variables may be estimated by principal component analysis (PCA),
logistic factor analysis (LFA), and other techniques.

Neo Christopher Chung <nchchung@gmail.com>, Wei Hao <whao@princeton.edu>, John D. Storey <jstorey@princeton.edu>

Statistical Inference for Unsupervised Learning
Test for association between the observed data
and their systematic patterns of variations, that are often extracted by unsupervised learning.
Systematic patterns may be captured by latent variables using principal component analysis (PCA), factor analysis (FA), and related methods. This allows one to, for example, obtain principal components (PCs) and conduct rigorous statistical testing for association between observed variables and PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and other algorithms, finds subpopulations among the observed variables. The jackstraw test can estimate statistical significance of cluster membership, so that one can evaluate the strength of membership assignments. This package also includes several related methods to support statistical inference and probabilistic feature selection for unsupervised learning.
corpcor, cluster, ClusterR, qvalue, methods, lfa, stats

Test for association between the observed data
and their systematic patterns of variations.
The jackstraw enables statistical testing for association between observed variables and latent variables, as captured by principal component analysis (PCA), factor analysis (FA), or other estimates. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds subpopulations among the observed variables. The jackstraw estimates statistical significance of cluster membership, including unsupervised evaluation of cell identities in single cell RNA-seq. P-values and posterior probabilities allows one to rigorously evaluate the strength of cluster membership assignments. See the GitHub repository for the latest developments and further helps.
corpcor, cluster, methods, ClusterR, qvalue, lfa, stats,
irlba, rsvd