plotCurves

generate ggplot objects for each of the curve fits in a MALDIassay object

Conduct automated cell-based assays using Matrix-Assisted Laser Desorption/Ionization (MALDI) methods for high-throughput screening of signals responsive to treatments. The package efficiently identifies high variance signals and fits dose-response curves to them. Quality metrics such as Z', V', log2FC, and CRS are provided for evaluating the potential of signals as biomarkers. The methodologies were introduced by Weigt et al. (2018) <doi:10.1038/s41598-018-29677-z> and refined by Unger et al. (2021) <doi:10.1038/s41596-021-00624-z>.

Thomas Enzlein

MALDIcellassay

Automated MALDI Cell Assays Using Dose-Response Curve Fitting

plotCurves function

<dl><dt>object</dt>
<dd>object of class MALDIassay</dd>
<dt>mzIdx</dt>
<dd>numeric, indicies of mz values to plot (see <code>getPeakStatistics()</code>). Note, fc_thresh and R2_thresh filters do not apply if mzIdx is set!</dd>
<dt>errorbars</dt>
<dd>character, add error bars to plot. Either standard error of the mean (<code>sem</code>) or standard deviation (<code>sd</code>) in regards to the measurement replicates or no errorbars (<code>none</code>).</dd></dl>

Arguments

generate ggplot objects for each of the curve fits in a MALDIassay object — plotCurves

<dl>

<dt>object</dt>
<dd>object of class MALDIassay</dd>


<dt>mzIdx</dt>
<dd>numeric, indicies of mz values to plot (see <code>getPeakStatistics()</code>). Note, fc_thresh and R2_thresh filters do not apply if mzIdx is set!</dd>


<dt>errorbars</dt>
<dd>character, add error bars to plot. Either standard error of the mean (<code>sem</code>) or standard deviation (<code>sd</code>) in regards to the measurement replicates or no errorbars (<code>none</code>).</dd>

</dl>

plotCurves: generate ggplot objects for each of the curve fits in a MALDIassay object

Description

Usage

Value

Arguments

Examples