scale_level

Scales the intensities of all features using
$$\widetilde{x}_{ij}=\frac{x_{ij}-\overline{x}_{i}}{\overline{x}_{i}}$$
where $\widetilde{x}_{ij}$ is the intensity of sample $j$, feature $i$ after scaling,
$x_{ij}$ is the intensity of sample $j$, feature $i$ before scaling and $\overline{x}_{i}$ is the mean of intensities of feature $i$ across all samples
In other words, it performs centering (<code>scale_center</code>) and divides by the feature mean, thereby focusing on the relative intensity.

Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012) <doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017) <doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001) <doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad et al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle et al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020) <doi:10.1038/s41598-020-72664-6>) transformation, centering and scaling (Van Den Berg et al. (2006) <doi:10.1186/1471-2164-7-142>) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) <doi:10.21105/joss.01686>) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'.

Yannik Schermer

metamorphr

Tidy and Streamlined Metabolomics Data Workflows

scale_level function

<dl><dt>data</dt>
<dd>A tidy tibble created by <code>read_featuretable</code>.</dd></dl>

Arguments

Scale intensities of features using level scaling — scale_level

<dl>

<dt>data</dt>
<dd>A tidy tibble created by <code>read_featuretable</code>.</dd>

</dl>

scale_level: Scale intensities of features using level scaling

Description

Usage

Value

Arguments

References

Examples