add_pcs

Add Principal Components Analysis Loadings to a tidy or triple omics
 dataset.

Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.

Sean Hackett

romic

R for High-Dimensional Omic Data

Calico Life Sciences LLC 

add_pcs function

<dl><dt>tomic</dt>
<dd>Either a <code>tidy_omic</code> or <code>triple_omic</code> object</dd>
<dt>value_var</dt>
<dd>An abundance value to use with <code>hclust</code></dd>
<dt>center_rows</dt>
<dd>center rows before performing PCA</dd>
<dt>npcs</dt>
<dd>number of principal component loadings to add to samples
(default is number of samples)</dd>
<dt>missing_val_method</dt>
<dd>Approach to remove missing values:<dl>
 <dt>drop_features</dt>
<dd>Drop features with missing values</dd></dl></dd> <dt>drop_samples</dt>
<dd>Drop samples which are missing all features,
 then drop features</dd> <dt>impute</dt>
<dd>Impute missing values</dd>
</dl>
<dt>label_percent_varex</dt>
<dd>If true then PCs will be labelled by the percent
of variability they explain.</dd>
<dt>verbose</dt>
<dd>extra reporting messages</dd>

Arguments

Add PCA Loadings — add_pcs

<dl>

<dt>tomic</dt>
<dd>Either a <code>tidy_omic</code> or <code>triple_omic</code> object</dd>


<dt>value_var</dt>
<dd>An abundance value to use with <code>hclust</code></dd>


<dt>center_rows</dt>
<dd>center rows before performing PCA</dd>


<dt>npcs</dt>
<dd>number of principal component loadings to add to samples
(default is number of samples)</dd>


<dt>missing_val_method</dt>
<dd>Approach to remove missing values:<dl>
 <dt>drop_features</dt>
<dd>Drop features with missing values</dd>

 <dt>drop_samples</dt>
<dd>Drop samples which are missing all features,
 then drop features</dd>

 <dt>impute</dt>
<dd>Impute missing values</dd>


</dl></dd>


<dt>label_percent_varex</dt>
<dd>If true then PCs will be labelled by the percent
of variability they explain.</dd>


<dt>verbose</dt>
<dd>extra reporting messages</dd>

</dl>

add_pcs: Add PCA Loadings

Description

Usage

Value

Arguments

Examples