wheel_plot: Visualize probabilistic classification results
In maehrlab/thymusatlastools: Tools for analysis of single-cell transcriptomic data

Visualize probabilistic classification results

1
2
3

wheel_plot(dge, results_path, mlr_mod = NULL, class_labels = NULL, fig_name,
  colour_by = NULL, facet_by = NULL, style = "points",
  wheel_order = NULL, do.density = NULL)

`dge`	a Seurat object
`results_path`	folder to place output in
`mlr_mod`	a glmnet multiclass logistic regression model. Give this or 'class_labels', not both. You can feed the output of 'train_save_classifier' into the 'mlr_mod' argument.
`class_labels`	atomic character vector naming columns in the Seurat object that contain class probabilities. Give this or 'mlr_mod', not both. If names(class_labels) is filled in, then that's how the spokes get labeled.
`fig_name`	filename for output.
`colour_by`	Variable in Seurat object to map color to; default is none
`facet_by`	Variable in Seurat object to facet resulting plots by; default is none
`style`	If "points", plot each cell as a dot. If "density", then instead of plotting points, plot 2d density contours. If "hexagons", do AWESOME HEXAGON BINNING YEAHHHHHHH HEXAGONS.
`wheel_order`	Deprecated.
`do.density`	Deprecated.
`fig_name`	filename for output.