clustify_lists: Main function to compare scRNA-seq data to gene lists.

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

Main function to compare scRNA-seq data to gene lists.

Description

Main function to compare scRNA-seq data to gene lists.

Usage

clustify_lists(input, ...)

## Default S3 method:
clustify_lists(
  input,
  marker,
  marker_inmatrix = TRUE,
  metadata = NULL,
  cluster_col = NULL,
  if_log = TRUE,
  per_cell = FALSE,
  topn = 800,
  cut = 0,
  genome_n = 30000,
  metric = "hyper",
  output_high = TRUE,
  lookuptable = NULL,
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  rename_prefix = NULL,
  threshold = 0,
  low_threshold_cell = 0,
  verbose = TRUE,
  input_markers = FALSE,
  details_out = FALSE,
  ...
)

## S3 method for class 'Seurat'
clustify_lists(
  input,
  metadata = NULL,
  cluster_col = NULL,
  if_log = TRUE,
  per_cell = FALSE,
  topn = 800,
  cut = 0,
  marker,
  marker_inmatrix = TRUE,
  genome_n = 30000,
  metric = "hyper",
  output_high = TRUE,
  dr = "umap",
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  verbose = TRUE,
  details_out = FALSE,
  ...
)

## S3 method for class 'SingleCellExperiment'
clustify_lists(
  input,
  metadata = NULL,
  cluster_col = NULL,
  if_log = TRUE,
  per_cell = FALSE,
  topn = 800,
  cut = 0,
  marker,
  marker_inmatrix = TRUE,
  genome_n = 30000,
  metric = "hyper",
  output_high = TRUE,
  dr = "umap",
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  verbose = TRUE,
  details_out = FALSE,
  ...
)

Arguments

input	single-cell expression matrix, Seurat object, or SingleCellExperiment
...	passed to matrixize_markers
marker	matrix or dataframe of candidate genes for each cluster
marker_inmatrix	whether markers genes are already in preprocessed matrix form
metadata	cell cluster assignments, supplied as a vector or data.frame. If data.frame is supplied then cluster_col needs to be set. Not required if running correlation per cell.
cluster_col	column in metadata with cluster number
if_log	input data is natural log, averaging will be done on unlogged data
per_cell	compare per cell or per cluster
topn	number of top expressing genes to keep from input matrix
cut	expression cut off from input matrix
genome_n	number of genes in the genome
metric	adjusted p-value for hypergeometric test, or jaccard index
output_high	if true (by default to fit with rest of package), -log10 transform p-value
lookuptable	if not supplied, will look in built-in table for object parsing
obj_out	whether to output object instead of cor matrix
seurat_out	output cor matrix or called seurat object (deprecated, use obj_out instead)
vec_out	only output a result vector in the same order as metadata
rename_prefix	prefix to add to type and r column names
threshold	identity calling minimum correlation score threshold, only used when obj_out = T
low_threshold_cell	option to remove clusters with too few cells
verbose	whether to report certain variables chosen and steps
input_markers	whether input is marker data.frame of 0 and 1s (output of pos_neg_marker), and uses alternate enrichment mode
details_out	whether to also output shared gene list from jaccard
dr	stored dimension reduction

Value

matrix of numeric values, clusters from input as row names, cell types from marker_mat as column names

Examples

# Annotate a matrix and metadata

# Annotate using a different method
clustify_lists(
    input = pbmc_matrix_small,
    marker = cbmc_m,
    metadata = pbmc_meta,
    cluster_col = "classified",
    verbose = TRUE,
    metric = "jaccard"
)

NCBI-Hackathons/clustifyR documentation built on Aug. 31, 2024, 5:35 a.m.