process_bootstrap_data

internal

Provides a comprehensive framework for time series omics analysis,
integrating changepoint detection, smooth and shape-constrained trends,
and uncertainty quantification. It supports gene- and transcript-level inferences,
p-value aggregation for improved power, and both case-only and case-control designs.
It includes an interactive 'shiny' interface. The methods are described in Yates et al. (2024) <doi:10.1101/2024.12.22.630003>.

Luke Yates

cpam

Changepoint Additive Models for Time Series Omics Data

Michael Charleston

Jazmine Humphreys

Steven Smith

process_bootstrap_data function

<dl><dt>data_long</dt>
<dd>Long format data</dd>
<dt>catch</dt>
<dd>Overdispersion calculations</dd>
<dt>boot</dt>
<dd>Bootstrap summary</dd></dl>

Arguments

Process bootstrap data — process_bootstrap_data

<dl>

<dt>data_long</dt>
<dd>Long format data</dd>


<dt>catch</dt>
<dd>Overdispersion calculations</dd>


<dt>boot</dt>
<dd>Bootstrap summary</dd>

</dl>

Process bootstrap data

process_bootstrap_data: Process bootstrap data

Description

Usage

Value

Arguments