get_significant_results

Find effects that are significant in at least one condition

Implements the multivariate adaptive shrinkage (mash)
method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for
estimating and testing large numbers of effects in many conditions
(or many outcomes). Mash takes an empirical Bayes approach to
testing and effect estimation; it estimates patterns of similarity
among conditions, then exploits these patterns to improve accuracy
of the effect estimates. The core linear algebra is implemented in
C++ for fast model fitting and posterior computation.

Peter Carbonetto

mashr

Multivariate Adaptive Shrinkage

Matthew Stephens

Sarah Urbut

Gao Wang

Yuxin Zou

Yunqi Yang

Sam Roweis

David Hogg

Jo Bovy

get_significant_results function

<dl><dt>m</dt>
<dd>the mash result (from joint or 1by1 analysis)</dd>
<dt>thresh</dt>
<dd>indicates the threshold below which to call signals
significant</dd>
<dt>conditions</dt>
<dd>which conditions to include in check (default to all)</dd>
<dt>sig_fn</dt>
<dd>the significance function used to extract
significance from mash object; eg could be ashr::get_lfsr or
ashr::get_lfdr. (Small values must indicate significant.)</dd></dl>

Arguments

Find effects that are significant in at least one condition — get_significant_results

<dl>

<dt>m</dt>
<dd>the mash result (from joint or 1by1 analysis)</dd>


<dt>thresh</dt>
<dd>indicates the threshold below which to call signals
significant</dd>


<dt>conditions</dt>
<dd>which conditions to include in check (default to all)</dd>


<dt>sig_fn</dt>
<dd>the significance function used to extract
significance from mash object; eg could be ashr::get_lfsr or
ashr::get_lfdr. (Small values must indicate significant.)</dd>

</dl>

Find effects that are significant in at least one condition

get_significant_results: Find effects that are significant in at least one condition

Description

Usage

Value

Arguments

Examples