find.discriminant.dimensions: Find discriminant dimensions

In carmonalab/ProjecTILs: Reference-based analysis of scRNA-seq data

Find discriminant dimensions

Description

Searches PCA or ICA dimensions where the query set deviates the most from a control set or from the reference map. It can be useful to suggest novel cell states that escape from the main axes of diversity of the UMAP

Usage

find.discriminant.dimensions(
  ref,
  query,
  query.control = NULL,
  query.assay = "RNA",
  state = "largest",
  labels.col = "functional.cluster",
  reduction = "ICA",
  test = c("ks", "t.test"),
  ndim = 50,
  print.n = 3,
  verbose = T
)

Arguments

ref	Seurat object with reference atlas
query	Seurat object with query data
query.control	Optionally, you can compare your query with a control sample, instead of the reference
query.assay	The data slot to be used for enrichment analysis
state	Perform discriminant analysis on this cell state. Can be either: "largest" - Performs analysis on the cell state most represented in the query set(s) "all" - Performs analysis on the complete dataset, using all cells A specific cell state, one of the states in metadata field labels.col
labels.col	The metadata field used to annotate the clusters (default: functional.cluster)
reduction	Which dimensionality reduction to use (either ICA or PCA)
test	Which test to perform between the dataset distributions in each ICA/PCA dimension. One of 'ks' (Kolmogorov-Smirnov) or 't.test' (T-test)
ndim	How many dimensions to consider in the reduced ICA/PCA space
print.n	The number of top dimensions to return to STDOUT
verbose	Print results to STDOUT

Value

A dataframe, where rows are ICA/PCA dimensions. ICA/PCAs are ranked by statistical significance when comparing their distribution between query and control (or query vs. reference map)

Examples

find.discriminant.dimensions(ref, query=query.set)
find.discriminant.dimensions(ref, query=query.set, query.control=control.set)

carmonalab/ProjecTILs documentation built on April 13, 2025, 10:04 p.m.