psrn

<a href="https://lifecycle.r-lib.org/articles/stages.html#experimental"><img src="figures/lifecycle-experimental.svg?package=baldur&version=0.0.4" alt="[Experimental]"></a>
This function generates a pseudo-reference by taking the geometric mean of
each peptide across all samples. Each peptide in each sample is then divided
by the pseudo-reference. Then, the median ratio of all ratios is used as an
estimate to use for normalizing differences in loading concentration. All
features in each sample is then divided by their corresponding estimate.
All estimates are based on features without missing values.
For details see anders2010differential;textualbaldur.

Statistical decision in proteomics data using a hierarchical
Bayesian model. There are two regression models for describing the
mean-variance trend, a gamma regression or a latent gamma mixture
regression. The regression model is then used as an Empirical Bayes
estimator for the prior on the variance in a peptide. Further, it assumes
that each measurement has an uncertainty (increased variance) associated
with it that is also inferred. Finally, it tries to estimate the posterior
distribution (by Hamiltonian Monte Carlo) for the differences in means for
each peptide in the data. Once the posterior is inferred, it integrates the
tails to estimate the probability of error from which a statistical decision
can be made.
See Berg and Popescu for details (<doi:10.1016/j.mcpro.2023.100658>).

Philip Berg

baldur

Bayesian Hierarchical Modeling for Label-Free Proteomics

psrn function

<dl><dt>data</dt>
<dd>data.frame</dd>
<dt>id_col</dt>
<dd>a character for the name of the column containing the
name of the features in data (e.g., peptides, proteins, etc.)</dd>
<dt>log</dt>
<dd>boolean variable indicating if the data should be log transformed
after normalization</dd>
<dt>load_info</dt>
<dd>logical; should the load information be output?</dd>
<dt>target</dt>
<dd>target columns to normalize, supports
<code><a href="https://tidyselect.r-lib.org/reference/tidyselect-package.html">tidyselect-package</a></code> syntax. By default, all numerical
columns will be used in the normalization if not specified.</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
This function generates a pseudo-reference by taking the geometric mean of
each peptide across all samples. Each peptide in each sample is then divided
by the pseudo-reference. Then, the median ratio of all ratios is used as an
estimate to use for normalizing differences in loading concentration. All
features in each sample is then divided by their corresponding estimate.
All estimates are based on features without missing values.
For details see anders2010differential;textualbaldur.

Normalize data to a pseudo-reference — psrn

<dl>

<dt>data</dt>
<dd>data.frame</dd>


<dt>id_col</dt>
<dd>a character for the name of the column containing the
name of the features in data (e.g., peptides, proteins, etc.)</dd>


<dt>log</dt>
<dd>boolean variable indicating if the data should be log transformed
after normalization</dd>


<dt>load_info</dt>
<dd>logical; should the load information be output?</dd>


<dt>target</dt>
<dd>target columns to normalize, supports
<code><a href='https://tidyselect.r-lib.org/reference/tidyselect-package.html'>tidyselect-package</a></code> syntax. By default, all numerical
columns will be used in the normalization if not specified.</dd>

</dl>

psrn: Normalize data to a pseudo-reference

Description

Usage

Value

Arguments

References

Examples