DistPvalues

Visualize the distribution of unadjusted p-values from linear models

This workflow takes analyte levels from two different types of analytes (e.g. gene expression and metabolite abundance), meta-information on each analyte type, and sample outcome and metadata to identify analyte pairs that are significantly associated with a continuous or discrete outcome (e.g. drug response or tumor type). The following references describe the methods in this package: (1) Jalal K. Siddiqui, et al. (2018) <doi:10.1186/s12859-018-2085-6>, (2) Andrew Patt, et al. (2019) <doi:10.1007/978-1-4939-9027-6_23>.

Tara Eicher

IntLIM

Integration of Omics Data Using Linear Modeling

Jalal Siddiqui

Shunchao Wang

Rohith Vanam

Elizabeth Baskin

Kyle Spencer

Ewy Mathe

DistPvalues function

<dl><dt>IntLimResults</dt>
<dd>output of RunIntLim()</dd>
<dt>breaks</dt>
<dd>the number of breaks to use in histogram (see hist() documentation for more details)</dd>
<dt>adjusted</dt>
<dd>Whether or not to plot adjusted p-values. If TRUE (default),
adjusted p-values are plotted. If FALSE, unadjusted p-values are plotted.</dd></dl>

Arguments

Visualize the distribution of unadjusted p-values from linear models — DistPvalues

<dl>

<dt>IntLimResults</dt>
<dd>output of RunIntLim()</dd>


<dt>breaks</dt>
<dd>the number of breaks to use in histogram (see hist() documentation for more details)</dd>


<dt>adjusted</dt>
<dd>Whether or not to plot adjusted p-values. If TRUE (default),
adjusted p-values are plotted. If FALSE, unadjusted p-values are plotted.</dd>

</dl>

DistPvalues: Visualize the distribution of unadjusted p-values from linear models

Description

Usage

Value

Arguments