Jurkat293T

datasets

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.

Neo Chung

jackstraw

Statistical Inference for Unsupervised Learning

Jurkat293T function

<p>A data frame with 3381 rows correspoding to single cells and 10 columns corresponding to the top 10 principal components</p>

Jurkat293T: A Jurkat:293T equal mixture dataset from Zheng et. al. (2017)

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