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jackstraw: Statistical Inference for Unsupervised Learning

This R package performs association tests between the observed data and their systematic patterns of variation. Systematic variation can be modeled by latent variables, that are likely arising from biological processes, experimental conditions, and environmental factors. We are often interested in estimating these patterns using principal component analysis (PCA), factor analysis (FA), K-means clustering, partition around medoids (PAM), and related methods. The jackstraw methods learn over-fitting characteristics inherent in unsupervised learning, where the observed data are used to estimate the systematic patterns and to be tested again.

Using a variety of unsupervised learning techniques, the jackstraw provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their systematic patterns of variation. For example, the cell cycle in microarray data may be estimated by principal components (PCs); then, we can use the jackstraw for PCA to identify genes that are significantly associated with these PCs. On the other hand, cell identities in single cell RNA-seq data are identified by K-means clustering; then, the jackstraw for clustering can evaluate reliability of computationally determined cell identities.

The jackstraw tests enable us to identify the variables (or observations) that are driving systematic variation, in an unsupervised manner. Using jackstraw_pca, we can find statistically significant variables with regard to the top r principal components. Alternatively, jackstraw_kmeans can identify the variables that are statistically significant members of clusters. There are many functions to support statistical inference for unsupervised learning, such as finding a number of PCs or clusters and estimating posterior probabilities from jackstraw p-values. Furthermore, this package includes more general and experimental algorithms such as jackstraw_subspace for the dimension reduction techniques and jackstraw_cluster for the clustering algorithms.

Chung, N.C. (2020) Statistical significance of cluster membership for unsupervised evaluation of cell identities. Bioinformatics, 36(10): 3107–3114 https://academic.oup.com/bioinformatics/article/36/10/3107/5788523

Chung, N.C. and Storey, J.D. (2015) Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics, 31(4): 545-554 https://academic.oup.com/bioinformatics/article/31/4/545/2748186

Installation

To use a stable version from CRAN:

install.packages("jackstraw")

To use a development version from GitHub:

install.packages("devtools")
library("devtools")
install_github("ncchung/jackstraw")

Troubleshooting

Bioconductor dependencies may fail to automatically install, namely:

This would result in a warning similar to:

Error: package or namespace load failed forjackstrawin loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]):
 there is no package calledlfa

To solve this problem, please install these two packages manually using the following command:

# install qvalue from Bioconductor
source("https://bioconductor.org/biocLite.R")
biocLite('qvalue')

For now, the current GitHub version of jackstraw depends on updates for lfa, gcatest, and genio present only on these GitHub repositories:

library(devtools)
install_github("alexviiia/lfa")
install_github("alexviiia/gcatest")
install_github("OchoaLab/genio")

Eventually, the Bioconductor versions of lfa and gcatest and CRAN version of genio will have these updates; sorry for the temporary inconvenience.

Thanks to idc9 for raising this issue.

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