read_counts

<p>Quantifications of bulk RNA-seq data of liver samples from 12 male mice.
This includes only a subset of mice and genes from the experiment, for size.</p>

datasets

Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and 'DESeq2' (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>.

Thomas Brooks

dependentsimr

Simulate Omics-Scale Data with Dependency

read_counts function


<div class="section" id="read-counts">
<h3><code>read_counts</code></h3><p></p><p>
</p><p>A tibble with 1,000 rows and 13 columns.
Each column is one mouse sample from GSE151923.
Each row is a ENSEMBL mouse gene/transcript with the quantified values for each sample.
Gene identifiers are in the first column.</p>
</div>


Format

GSE151923: cortex from 6-month-old wildtype C57BL/6 mice — read_counts


<div class='section' id='read-counts'>
<h3><code>read_counts</code></h3>


<p>A tibble with 1,000 rows and 13 columns.
Each column is one mouse sample from GSE151923.
Each row is a ENSEMBL mouse gene/transcript with the quantified values for each sample.
Gene identifiers are in the first column.</p>
</div>


read_counts: GSE151923: cortex from 6-month-old wildtype C57BL/6 mice

Description

Usage

Arguments

Format

`read_counts`