plot_cor: Plot similarity measures on a tSNE or umap

In NCBI-Hackathons/RClusterCT: Classifier for Single-cell RNA-seq Using Cell Clusters

Plot similarity measures on a tSNE or umap

Description

Plot similarity measures on a tSNE or umap

Usage

plot_cor(
  cor_mat,
  metadata,
  data_to_plot = colnames(cor_mat),
  cluster_col = NULL,
  x = "UMAP_1",
  y = "UMAP_2",
  scale_legends = FALSE,
  ...
)

Arguments

cor_mat	input similarity matrix
metadata	input metadata with per cell tsne or umap coordinates and cluster ids
data_to_plot	colname of data to plot, defaults to all
cluster_col	colname of clustering data in metadata, defaults to rownames of the metadata if not supplied.
x	metadata column name with 1st axis dimension. defaults to "UMAP_1".
y	metadata column name with 2nd axis dimension. defaults to "UMAP_2".
scale_legends	if TRUE scale all legends to maximum values in entire correlation matrix. if FALSE scale legends to maximum for each plot. A two-element numeric vector can also be passed to supply custom values i.e. c(0, 1)
...	passed to plot_dims

Value

list of ggplot objects, cells projected by dr, colored by cor values

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "classified"
)

plot_cor(
    cor_mat = res,
    metadata = pbmc_meta,
    data_to_plot = colnames(res)[1:2],
    cluster_col = "classified",
    x = "UMAP_1",
    y = "UMAP_2"
)

NCBI-Hackathons/RClusterCT documentation built on April 23, 2024, 11:19 p.m.