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

View source: R/main.R

clustify_listsR Documentation

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 = TRUE,
  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",
  seurat_out = TRUE,
  obj_out = TRUE,
  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",
  seurat_out = TRUE,
  obj_out = TRUE,
  vec_out = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  verbose = TRUE,
  details_out = FALSE,
  ...
)

Arguments

input

single-cell expression matrix or Seurat object

...

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/RClusterCT documentation built on July 10, 2022, 6:18 a.m.