normalize_quantile_batch

This function performs a Quantile Normalization on each sub-group and batch in the data set. It therefore requires grouping information. See
Examples for more information. This approach might perform better than the standard approach, <code>normalize_quantile_all</code>,
if sub-groups are very different (e.g., when comparing cancer vs. normal tissue).
Other sub-flavors are also available:<ul>
<li><code>normalize_quantile_all</code></li>
<li><code>normalize_quantile_batch</code></li>
<li><code>normalize_quantile_smooth</code></li>
</ul>See References for more information.
Note that it is equivalent to the 'Discrete' normalization in Zhao et al. but has been renamed for internal consistency.

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

normalize_quantile_batch function

<dl><dt>data</dt>
<dd>A tidy tibble created by <code>read_featuretable</code>.</dd>
<dt>group_column</dt>
<dd>Which column should be used for grouping? Usually <code>grouping_column = Group</code>. Uses <code>args_data_masking</code>.</dd>
<dt>batch_column</dt>
<dd>Which column contains the batch information? Usually <code>grouping_column = Batch</code>. Uses <code>args_data_masking</code>.</dd></dl>

Arguments

This function performs a Quantile Normalization on each sub-group and batch in the data set. It therefore requires grouping information. See
Examples for more information. This approach might perform better than the standard approach, <code>normalize_quantile_all</code>,
if sub-groups are very different (e.g., when comparing cancer vs. normal tissue).
Other sub-flavors are also available:<ul>
<li><code>normalize_quantile_all</code></li>
<li><code>normalize_quantile_batch</code></li>
<li><code>normalize_quantile_smooth</code></li>
</ul>

See References for more information.
Note that it is equivalent to the 'Discrete' normalization in Zhao et al. but has been renamed for internal consistency.

Normalize intensities across samples using grouped Quantile Normalization with multiple batches — normalize_quantile_batch

<dl>

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


<dt>group_column</dt>
<dd>Which column should be used for grouping? Usually <code>grouping_column = Group</code>. Uses <code>args_data_masking</code>.</dd>


<dt>batch_column</dt>
<dd>Which column contains the batch information? Usually <code>grouping_column = Batch</code>. Uses <code>args_data_masking</code>.</dd>

</dl>

normalize_quantile_batch: Normalize intensities across samples using grouped Quantile Normalization with multiple batches

Description

Usage

Value

Arguments

References

Examples