clustify_lists {clustifyr} | R 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,
      rename_prefix = NULL,
      threshold = 0,
      low_threshold_cell = 10,
      ...
    )

    ## 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,
      threshold = 0,
      rename_prefix = NULL,
      ...
    )

    ## 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,
      threshold = 0,
      rename_prefix = NULL,
      ...
    )

    ## 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,
      threshold = 0,
      rename_prefix = NULL,
      ...
    )

### 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)
`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
`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
    clustify_lists(
        input = pbmc_matrix_small,
        marker = cbmc_m,
        metadata = pbmc_meta,
        cluster_col = "classified",
        verbose = TRUE
    )

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

[Package clustifyr version 1.0.0 Index]