jackstraw
Statistical Inference for Unsupervised Learning
Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).
- Version1.3.17
- R versionunknown
- LicenseGPL-2
- Needs compilation?No
- Last release09/16/2024
Documentation
Team
Neo Christopher Chung
Wei Hao
Show author detailsRolesAuthorAlejandro Ochoa
Show author detailsRolesAuthorJohn D. Storey
Insights
Last 30 days
This package has been downloaded 1,053 times in the last 30 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 26 times.
Last 365 days
This package has been downloaded 28,143 times in the last 365 days. That's enough downloads to make it mildly famous in niche technical communities. A badge of honor! The day with the most downloads was Oct 07, 2024 with 883 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports7 packages
- Suggests1 package