plotTrainingHistory

Plot training history of a trained SpatialDDLS deep neural network model.

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.

Diego Ma<c3><b1>anes

SpatialDDLS

Deconvolution of Spatial Transcriptomics Data Based on Neural
Networks

Diego Mañanes

Carlos Torroja

Fatima Sanchez-Cabo

plotTrainingHistory function

<dl><dt>object</dt>
<dd><code>SpatialDDLS</code> object with a
<code>trained.model</code> slot.</dd>
<dt>title</dt>
<dd>Title of plot.</dd>
<dt>metrics</dt>
<dd>Metrics to be plotted. If <code>NULL</code> (by default), all
metrics available in the <code>DeconvDLModel</code> object will be
plotted.</dd></dl>

Arguments

Plot training history of a trained SpatialDDLS deep neural network model — plotTrainingHistory

<dl>

<dt>object</dt>
<dd><code>SpatialDDLS</code> object with a
<code>trained.model</code> slot.</dd>


<dt>title</dt>
<dd>Title of plot.</dd>


<dt>metrics</dt>
<dd>Metrics to be plotted. If <code>NULL</code> (by default), all
metrics available in the <code>DeconvDLModel</code> object will be
plotted.</dd>

</dl>

plotTrainingHistory: Plot training history of a trained SpatialDDLS deep neural network model

Description

Usage

Value

Arguments

See Also