From a set of p-values, computes posterior probabilities that a feature should be truly included.
For example, membership inclusion in a given cluster can be improved by filtering low quality members.
In using PCA and related methods, it helps select variables that are truly associated with given latent variables.

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).

Neo Chung

jackstraw

Statistical Inference for Unsupervised Learning

Wei Hao

Alejandro Ochoa

pip function

<dl><dt>pvalue</dt>
<dd>a vector of p-values.</dd>
<dt>group</dt>
<dd>a vector of group indicators (optional).
If provided, PIP analysis is stratified.
Assumes groups are in 1:k where k is the number of unique groups.</dd>
<dt>pi0</dt>
<dd>a vector of pi0 values (optional).
Its length has to be either 1 or equal the number of groups.</dd>
<dt>verbose</dt>
<dd>If TRUE, reports information.</dd>
<dt>...</dt>
<dd>optional arguments for <code>lfdr</code> to control a local FDR estimation.</dd></dl>

Arguments

Neo Christopher Chung <a href="/link/nchchung%40gmail.com?package=jackstraw&version=1.3.17" data-mini-rdoc="jackstraw::nchchung@gmail.com">nchchung@gmail.com</a>
John R. Yamamoto-Wilson

Author

Compute posterior inclusion probabilities (PIPs) — pip

<dl>

<dt>pvalue</dt>
<dd>a vector of p-values.</dd>


<dt>group</dt>
<dd>a vector of group indicators (optional).
If provided, PIP analysis is stratified.
Assumes groups are in 1:k where k is the number of unique groups.</dd>


<dt>pi0</dt>
<dd>a vector of pi0 values (optional).
Its length has to be either 1 or equal the number of groups.</dd>


<dt>verbose</dt>
<dd>If TRUE, reports information.</dd>


<dt>...</dt>
<dd>optional arguments for <code>lfdr</code> to control a local FDR estimation.</dd>

</dl>

Neo Christopher Chung <a href='mailto:nchchung@gmail.com'>nchchung@gmail.com</a>
John R. Yamamoto-Wilson

pip: Compute posterior inclusion probabilities (PIPs)

Description

Usage

Value

Arguments

Author

Details

References