plotPC

This functions plots the percentage variability explained the first one hundred (or <code>pcaComp</code>) pricipal components of the PCA performed in the function <code>pruneKnn</code> if the parameter <code>large</code> was TRUE. The selected number of principal components (if <code>pcaComp</code> was NULL) is determined by an elbow criterion and highlighted by a blue circle.

Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) <DOI:10.1038/nmeth.4662>). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) <DOI:10.1186/s13059-023-02974-1>).

Dominic Grc3<bc>n

RaceID

Identification of Cell Types, Inference of Lineage Trees, and
Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Dominic Grün

plotPC function

<dl><dt>res</dt>
<dd>List object with k nearest neighbour information returned by <code>pruneKnn</code> function.</dd>
<dt>logDiff</dt>
<dd>logical. If <code>TRUE</code>, then plot log2 of the difference in variability explained by PC i and PC i+1.</dd></dl>

Arguments

Function to plot the selected number of principal components — plotPC

<dl>

<dt>res</dt>
<dd>List object with k nearest neighbour information returned by <code>pruneKnn</code> function.</dd>


<dt>logDiff</dt>
<dd>logical. If <code>TRUE</code>, then plot log2 of the difference in variability explained by PC i and PC i+1.</dd>

</dl>

plotPC: Function to plot the selected number of principal components

Description

Usage

Arguments