pcainfo

Perform Principal Component Analysis (PCA) on log-expression data.

A 'shiny' app that supports both dual and bulk RNA-seq, with the dual
RNA-seq functionality offering the flexibility to perform either a
sequential approach (where reads are mapped separately to each genome)
or a combined approach (where reads are aligned to a single merged
genome). The user-friendly interface automates the analysis process,
providing step-by-step guidance, making it easy for users to navigate
between different analysis steps, and download intermediate results
and publication-ready plots.

Carmine Fruggiero

inDAGO

A GUI for Dual and Bulk RNA-Sequencing Analysis

Gaetano Aufiero

pcainfo function

<dl><dt>logcounts</dt>
<dd>Numeric matrix. Log-CPM values (genes × samples), e.g., from edgeR::cpm..</dd>
<dt>center</dt>
<dd>Logical. If TRUE, center variables by subtracting the mean (default: TRUE).</dd>
<dt>scale</dt>
<dd>Logical. If TRUE, scale variables to unit variance (default: FALSE).</dd></dl>

Arguments

pcainfo — pcainfo

<dl>

<dt>logcounts</dt>
<dd>Numeric matrix. Log-CPM values (genes × samples), e.g., from edgeR::cpm..</dd>


<dt>center</dt>
<dd>Logical. If TRUE, center variables by subtracting the mean (default: TRUE).</dd>


<dt>scale</dt>
<dd>Logical. If TRUE, scale variables to unit variance (default: FALSE).</dd>

</dl>

pcainfo: pcainfo

Description

Usage

Value

Arguments

Details