estimate_uncertainty

estimate_uncertainty.glm

estimate_uncertainty.lgmr

<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>
Estimates the measurement uncertainty for each data point using
a Gamma regression.
Calculated as the expected standard deviation for each measurement:
$$
 \text{E}[s_i|\omega,y_{ij}]=\exp({f(y_{ij},\omega)})
 $$
where $\omega$ are the regression parameters and $f$ is a function
describing the mean relationship between $s_i$ and $y_{ij}$.

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

estimate_uncertainty function

<dl><dt>reg</dt>
<dd>A <code>glm</code> gamma regression or <code>lgmr</code> object</dd>
<dt>data</dt>
<dd>A <code>tibble</code> or <code>data.frame</code></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>design_matrix</dt>
<dd>Cell mean design matrix for the data</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
Estimates the measurement uncertainty for each data point using
a Gamma regression.
Calculated as the expected standard deviation for each measurement:
$$
 \text{E}[s_i|\omega,y_{ij}]=\exp({f(y_{ij},\omega)})
 $$
where $\omega$ are the regression parameters and $f$ is a function
describing the mean relationship between $s_i$ and $y_{ij}$.

Estimate measurement uncertainty — estimate_uncertainty

<dl>

<dt>reg</dt>
<dd>A <code>glm</code> gamma regression or <code>lgmr</code> object</dd>


<dt>data</dt>
<dd>A <code>tibble</code> or <code>data.frame</code></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>design_matrix</dt>
<dd>Cell mean design matrix for the data</dd>

</dl>

estimate_uncertainty: Estimate measurement uncertainty

Description

Usage

Value

Arguments

Examples