gse32472Example

A dataset of 97 normal or BPD samples at 5 days of life, with gestational age, class, and gene expression for 654 genes

datasets

TEMPO (TEmporal Modeling of Pathway Outliers) is a pathway-based outlier detection approach for finding pathways showing
significant changes in temporal expression patterns across conditions. Given a gene expression data set where each sample is characterized by
an age or time point as well as a phenotype (e.g. control or disease), and a collection of gene sets or pathways, TEMPO ranks each pathway
by a score that characterizes how well a partial least squares regression (PLSR) model can predict age as a function of gene expression in the controls
and how poorly that same model performs in the disease. TEMPO v1.0.3 is described in Pietras (2018) <doi:10.1145/3233547.3233559>.

Christopher Pietras

tempoR

Characterizing Temporal Dysregulation

gse32472Example function

A list<dl class="dl-horizontal">
 <dt>data</dt><dd>a matrix of 97 samples and 654 genes</dd>
 <dt>age</dt><dd>a list of sample gestational ages, indexed by sample id</dd>
 <dt>bpd</dt><dd>a list of statuses, indexed by sample id</dd>
 <dt>ctrl</dt><dd>a list of sample ids without BPD</dd>
 <dt>test</dt><dd>a list of sample ids with BPD</dd>
</dl>

Format

A subset of the GSE32472 BPD data set — gse32472Example

A list<dl class='dl-horizontal'>
 <dt>data</dt><dd>a matrix of 97 samples and 654 genes</dd>
 <dt>age</dt><dd>a list of sample gestational ages, indexed by sample id</dd>
 <dt>bpd</dt><dd>a list of statuses, indexed by sample id</dd>
 <dt>ctrl</dt><dd>a list of sample ids without BPD</dd>
 <dt>test</dt><dd>a list of sample ids with BPD</dd>
</dl>

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gse32472Example: A subset of the GSE32472 BPD data set

Description

Usage

Arguments

Format