generate_data_with_anomalies

Generate High-Dimensional Data with Anomalies

Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) <doi:10.1038/nprot.2011.335>.

Yaoxiang Li

omicsTools

Omics Data Process Toolbox

generate_data_with_anomalies function

<dl><dt>n_samples</dt>
<dd>The number of samples to generate. Defaults to 100.</dd>
<dt>n_features</dt>
<dd>The number of features to generate. Defaults to 2000.</dd></dl>

Arguments

Generate High-Dimensional Data with Anomalies — generate_data_with_anomalies

<dl>

<dt>n_samples</dt>
<dd>The number of samples to generate. Defaults to 100.</dd>


<dt>n_features</dt>
<dd>The number of features to generate. Defaults to 2000.</dd>

</dl>

generate_data_with_anomalies: Generate High-Dimensional Data with Anomalies

Description

Usage

Value

Arguments

Examples