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"
)
#> list of markers instead of matrix, only supports jaccard
#> similarity computation completed, matrix of 9 x 13, preparing output
#>                    CD4 T       CD8 T Memory CD4 T  CD14+ Mono Naive CD4 T
#> Naive CD4 T  0.001246883 0.001246883            0 0.003750000           0
#> Memory CD4 T 0.001246883 0.000000000            0 0.003750000           0
#> CD14+ Mono   0.000000000 0.000000000            0 0.003750000           0
#> B            0.000000000 0.000000000            0 0.003750000           0
#> CD8 T        0.001246883 0.000000000            0 0.002496879           0
#> FCGR3A+ Mono 0.000000000 0.000000000            0 0.003750000           0
#> NK           0.000000000 0.001246883            0 0.003750000           0
#> DC           0.000000000 0.000000000            0 0.003750000           0
#> Platelet     0.001166861 0.000000000            0 0.003508772           0
#>                       NK           B  CD16+ Mono CD34+ Eryth          Mk
#> Naive CD4 T  0.003750000 0.000000000 0.000000000     0     0 0.000000000
#> Memory CD4 T 0.003750000 0.000000000 0.000000000     0     0 0.000000000
#> CD14+ Mono   0.002496879 0.000000000 0.000000000     0     0 0.000000000
#> B            0.002496879 0.002496879 0.000000000     0     0 0.000000000
#> CD8 T        0.003750000 0.000000000 0.000000000     0     0 0.000000000
#> FCGR3A+ Mono 0.001246883 0.000000000 0.001246883     0     0 0.000000000
#> NK           0.003750000 0.000000000 0.000000000     0     0 0.000000000
#> DC           0.002496879 0.001246883 0.000000000     0     0 0.000000000
#> Platelet     0.002336449 0.002336449 0.000000000     0     0 0.003508772
#>                       DC        pDCs
#> Naive CD4 T  0.000000000 0.000000000
#> Memory CD4 T 0.000000000 0.000000000
#> CD14+ Mono   0.000000000 0.000000000
#> B            0.000000000 0.000000000
#> CD8 T        0.000000000 0.000000000
#> FCGR3A+ Mono 0.000000000 0.000000000
#> NK           0.000000000 0.000000000
#> DC           0.001246883 0.002496879
#> Platelet     0.000000000 0.000000000