run_prop_missing

This function calculates the fraction of missing data for each sample run.

Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>. Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>. Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>. Matzke et al. (2013) <doi:10.1002/pmic.201200269>. Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>. Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.

Lisa Bramer

pmartR

Panomics Marketplace - Quality Control and Statistical Analysis
for Panomics Data

Kelly Stratton

Daniel Claborne

Evan Glasscock

Rachel Richardson

David Degnan

Evan Martin

run_prop_missing function

<dl><dt>data_only</dt>
<dd>a \(p \times n\) data.frame, where \(p\) is the number
of peptides and \(n\) is the number of samples.</dd></dl>

Arguments

Author

Calculate the Fraction of Missing Data of Sample Runs — run_prop_missing

<dl>

<dt>data_only</dt>
<dd>a \(p \times n\) data.frame, where \(p\) is the number
of peptides and \(n\) is the number of samples.</dd>

</dl>

run_prop_missing: Calculate the Fraction of Missing Data of Sample Runs

Description

Usage

Value

Arguments

Author