clustify | R Documentation |
Compare scRNA-seq data to reference data.
clustify(input, ...) ## Default S3 method: clustify( input, ref_mat, metadata = NULL, cluster_col = NULL, query_genes = NULL, n_genes = 1000, per_cell = FALSE, n_perm = 0, compute_method = "spearman", pseudobulk_method = "mean", verbose = TRUE, lookuptable = NULL, rm0 = FALSE, obj_out = TRUE, seurat_out = TRUE, vec_out = FALSE, rename_prefix = NULL, threshold = "auto", low_threshold_cell = 0, exclude_genes = c(), if_log = TRUE, organism = "hsapiens", plot_name = NULL, rds_name = NULL, expand_unassigned = FALSE, ... ) ## S3 method for class 'Seurat' clustify( input, ref_mat, cluster_col = NULL, query_genes = NULL, n_genes = 1000, per_cell = FALSE, n_perm = 0, compute_method = "spearman", pseudobulk_method = "mean", use_var_genes = TRUE, dr = "umap", seurat_out = TRUE, obj_out = TRUE, vec_out = FALSE, threshold = "auto", verbose = TRUE, rm0 = FALSE, rename_prefix = NULL, exclude_genes = c(), metadata = NULL, organism = "hsapiens", plot_name = NULL, rds_name = NULL, expand_unassigned = FALSE, ... ) ## S3 method for class 'SingleCellExperiment' clustify( input, ref_mat, cluster_col = NULL, query_genes = NULL, per_cell = FALSE, n_perm = 0, compute_method = "spearman", pseudobulk_method = "mean", use_var_genes = TRUE, dr = "umap", seurat_out = TRUE, obj_out = TRUE, vec_out = FALSE, threshold = "auto", verbose = TRUE, rm0 = FALSE, rename_prefix = NULL, exclude_genes = c(), metadata = NULL, organism = "hsapiens", plot_name = NULL, rds_name = NULL, expand_unassigned = FALSE, ... )
input |
single-cell expression matrix or Seurat object |
... |
additional arguments to pass to compute_method function |
ref_mat |
reference expression matrix |
metadata |
cell cluster assignments,
supplied as a vector or data.frame.
If data.frame is supplied then |
cluster_col |
column in metadata that contains cluster ids per cell. Will default to first column of metadata if not supplied. Not required if running correlation per cell. |
query_genes |
A vector of genes of interest to compare. If NULL, then common genes between the expr_mat and ref_mat will be used for comparision. |
n_genes |
number of genes limit for Seurat variable genes, by default 1000, set to 0 to use all variable genes (generally not recommended) |
per_cell |
if true run per cell, otherwise per cluster. |
n_perm |
number of permutations, set to 0 by default |
compute_method |
method(s) for computing similarity scores |
pseudobulk_method |
method used for summarizing clusters, options are mean (default), median, truncate (10% truncated mean), or trimean, max, min |
verbose |
whether to report certain variables chosen and steps |
lookuptable |
if not supplied, will look in built-in table for object parsing |
rm0 |
consider 0 as missing data, recommended for per_cell |
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 = TRUE |
low_threshold_cell |
option to remove clusters with too few cells |
exclude_genes |
a vector of gene names to throw out of query |
if_log |
input data is natural log, averaging will be done on unlogged data |
organism |
for GO term analysis, organism name: human - 'hsapiens', mouse - 'mmusculus' |
plot_name |
name for saved pdf, if NULL then no file is written (default) |
rds_name |
name for saved rds of rank_diff, if NULL then no file is written (default) |
expand_unassigned |
test all ref clusters for unassigned results |
use_var_genes |
if providing a seurat object, use the variable genes (stored in seurat_object@var.genes) as the query_genes. |
dr |
stored dimension reduction |
single cell object with identity assigned in metadata, or matrix of correlation values, clusters from input as row names, cell types from ref_mat as column names
# Annotate a matrix and metadata clustify( input = pbmc_matrix_small, metadata = pbmc_meta, ref_mat = cbmc_ref, query_genes = pbmc_vargenes, cluster_col = "classified", verbose = TRUE ) # Annotate using a different method clustify( input = pbmc_matrix_small, metadata = pbmc_meta, ref_mat = cbmc_ref, query_genes = pbmc_vargenes, cluster_col = "classified", compute_method = "cosine" ) # Annotate a Seurat object clustify( s_small3, cbmc_ref, cluster_col = "RNA_snn_res.1", obj_out = TRUE, per_cell = FALSE, dr = "tsne" ) # Annotate (and return) a Seurat object per-cell clustify( input = s_small3, ref_mat = cbmc_ref, cluster_col = "RNA_snn_res.1", obj_out = TRUE, per_cell = TRUE, dr = "tsne" )
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