annotationCloud: Word cloud visualization of cell-type annotations from the...

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Word cloud visualization of cell-type annotations from the Miko scoring pipeline

Description

Given outputs from the Miko scoring pipeline, the top cell-type annotations fare visualized using word clouds.

Usage

annotationCloud(
  object,
  object.group = "seurat_clusters",
  score,
  score.group,
  score.cell.type,
  score.p,
  score.fdr = NULL,
  score.coherence.fraction = NULL,
  score.frequent.flier = NULL,
  fdr.correction = T,
  p.threshold = 0.05,
  coherence.threshold = 0.8,
  show.n.terms = 15,
  verbose = T
)

Arguments

object	Seurat Object.
object.group	name of object meta data field specifying cluster membership. Default is "seurat_clusters".
score	vector of Miko scores
score.group	vector of group memberships
score.cell.type	vector of cell-type names/labels.
score.p	vector of p values.
score.fdr	vector of fdr values. Optional.
score.coherence.fraction	vector of coherence fractions. See coherentFraction(...) for details.
score.frequent.flier	vector of logicals specifying whether score belongs to frequent flier.
fdr.correction	Specify whether p-value should be corrected using Benjamini & Hochberg method. Default is T.
p.threshold	p value threshold. Default is 0.05.
coherence.threshold	Numerical [0,1] specifying minimal coherence required to qualify for visualization. Default is 0.8.
show.n.terms	Maximal number of cell-type terms shown in word cloud. Default is 15.
verbose	Logical, specify whether process is printed. Default is T.

Value

list of ggplot handles

Author(s)

Nicholas Mikolajewicz

See Also

mikoScore for miko scoring, coherentFraction for coherence scoring

Examples

df.score_summary <- data.frame(cluster = df.merge$cluster,
                              cell.type = df.merge$gs,
                              miko_score = signif(df.merge$miko_score, 3) ,
                              p =  signif(df.merge$p),
                              fdr =  signif(df.merge$fdr),
                              coherence_fraction =  signif(df.merge$coherence_fraction))


plt.cloud <- annotationCloud(object = so.query_scored,
                             object.group = "seurat_clusters",
                             score = df.score_summary$miko_score,
                             score.group = df.score_summary$cluster,
                             score.cell.type = df.score_summary$cell.type,
                             score.p = df.score_summary$p,
                             score.fdr = df.score_summary$fdr,
                             score.coherence.fraction = df.score_summary$coherence_fraction,
                             score.frequent.flier = NULL,
                             fdr.correction = T,
                             p.threshold = 0.05,
                             coherence.threshold = 0.9,
                             show.n.terms = 15,
                             verbose = T)

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.