MarginalEffectsGraph

Creates a dataframe of the marginal effect of phenotype

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

MarginalEffectsGraph function

<dl><dt>dataframe</dt>
<dd>from MarginalEffectsGraphDataframe</dd>
<dt>title</dt>
<dd>for graph</dd>
<dt>ylab</dt>
<dd>outcome analyte in pair</dd>
<dt>xlab</dt>
<dd>independent analyte in pair</dd></dl>

Arguments

Creates a dataframe of the marginal effect of phenotype — MarginalEffectsGraph

<dl>

<dt>dataframe</dt>
<dd>from MarginalEffectsGraphDataframe</dd>


<dt>title</dt>
<dd>for graph</dd>


<dt>ylab</dt>
<dd>outcome analyte in pair</dd>


<dt>xlab</dt>
<dd>independent analyte in pair</dd>

</dl>

MarginalEffectsGraph: Creates a dataframe of the marginal effect of phenotype

Description

Usage

Value

Arguments