9. PCA: this is a fully automated module that performs Horn’s parallel analysis indicating the number of PCs capturing non-random variation in the dataset to help the user determine whether 2 or 3 principal components should be used in the clustering module implemented later in the pipeline. PCA scores plots for the first two PCs are computed on per-cell and per-sample bases to visualize how single-cells and tissue sample are distributed with respect to each other. Ridge plots are also computed to visualize histogram alignment across marker channels.
YAML configurations
| Parameter | Default | Description |
|---|---|---|
channelExclusionsPCA | [ ] | (list of strs) Immunomarkers to exclude from PCA analysis. |
samplesToRemovePCA | [ ] | (list of strs) Samples to exclude from PCA analysis. |
dimensionPCA | 2 | (int) Number of PCs to compute. |
pointSize | 90.0 | (float) Scatter point size for sample scores plot. |
labelPoints | True | (bool) Annotate scatter points with condition abbreviations from sampleMetadata configuration. |
distanceCutoff | 0.15 | (float) Maximum distance between data points in PCA scores plot to be annotated with a common label. Useful for increasing visual clarity of PCA plots containing many data points. Applicable when labelPoints is True. |
conditionsToSilhouette | [ ] | (list of strs) Abbreviated condition names whose corresponding scores plot points will be greyed out, left unannotated, and sent to the back of the plot (zorder). Useful for increasing visual clarity of PCA plots containing many data points. |