ts_filter

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

ts_filter function

<dl><dt>data</dt>
<dd>A tibble or data.frame containing columns:<ul>
<li>target_id (character): Transcript identifiers</li>
<li>time (numeric): Time point of measurement</li>
<li>counts (numeric): Read counts</li>
</ul></dd>
<dt>min_reads</dt>
<dd>minimum reads per transcript per sample</dd>
<dt>min_prop</dt>
<dd>minimum proportion of samples that exceed <code>min_read</code> at a
given time point (default: 3/5)</dd></dl>

Arguments

Removes lowly expressed genes — ts_filter

<dl>

<dt>data</dt>
<dd>A tibble or data.frame containing columns:<ul>
<li>target_id (character): Transcript identifiers</li>
<li>time (numeric): Time point of measurement</li>
<li>counts (numeric): Read counts</li>
</ul></dd>


<dt>min_reads</dt>
<dd>minimum reads per transcript per sample</dd>


<dt>min_prop</dt>
<dd>minimum proportion of samples that exceed <code>min_read</code> at a
given time point (default: 3/5)</dd>

</dl>

ts_filter: Removes lowly expressed genes

Description

Usage

Value

Arguments

Details

Examples