HistogramPairs

histogram of analyte pairs
depending upon independent or outcome analyte

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

HistogramPairs function

<dl><dt>inputResults</dt>
<dd>Data frame with model results (output of ProcessResults())</dd>
<dt>type</dt>
<dd>'independent' or 'outcome'. 'outcome' set as default</dd>
<dt>breaks</dt>
<dd>Number of breaks selected for histogram</dd></dl>

Arguments

histogram of analyte pairs
depending upon independent or outcome analyte — HistogramPairs

<dl>

<dt>inputResults</dt>
<dd>Data frame with model results (output of ProcessResults())</dd>


<dt>type</dt>
<dd>'independent' or 'outcome'. 'outcome' set as default</dd>


<dt>breaks</dt>
<dd>Number of breaks selected for histogram</dd>

</dl>

HistogramPairs: histogram of analyte pairs depending upon independent or outcome analyte

Description

Usage

Value

Arguments