PlotPCA

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

PlotPCA function

<dl><dt>inputData</dt>
<dd>IntLimObject output of ReadData()</dd>
<dt>viewer</dt>
<dd>whether the plot should be displayed in the RStudio viewer (TRUE) or
in Shiny/Knittr (FALSE)</dd>
<dt>stype</dt>
<dd>category to color-code by (can be more than two categories)</dd>
<dt>palette</dt>
<dd>choose an RColorBrewer palette ("Set1", "Set2", "Set3",
"Pastel1", "Pastel2", "Paired", etc.) or submit a vector of colors</dd></dl>

Arguments

PCA plots of data for QC — PlotPCA

<dl>

<dt>inputData</dt>
<dd>IntLimObject output of ReadData()</dd>


<dt>viewer</dt>
<dd>whether the plot should be displayed in the RStudio viewer (TRUE) or
in Shiny/Knittr (FALSE)</dd>


<dt>stype</dt>
<dd>category to color-code by (can be more than two categories)</dd>


<dt>palette</dt>
<dd>choose an RColorBrewer palette ("Set1", "Set2", "Set3",
"Pastel1", "Pastel2", "Paired", etc.) or submit a vector of colors</dd>

</dl>

PCA plots of data for QC

PlotPCA: PCA plots of data for QC

Description

Usage

Value

Arguments