Compare scRNA-seq data to reference data. — clustify (original) (raw)
Compare scRNA-seq data to reference data.
Usage
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 = obj_out,
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",
obj_out = TRUE,
seurat_out = obj_out,
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",
obj_out = TRUE,
seurat_out = obj_out,
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,
...
)Arguments
single-cell expression matrix or Seurat object
additional arguments to pass to compute_method function
reference expression matrix
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.
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.
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.
number of genes limit for Seurat variable genes, by default 1000, set to 0 to use all variable genes (generally not recommended)
if true run per cell, otherwise per cluster.
number of permutations, set to 0 by default
method(s) for computing similarity scores
method used for summarizing clusters, options are mean (default), median, truncate (10% truncated mean), or trimean, max, min
whether to report certain variables chosen and steps
if not supplied, will look in built-in table for object parsing
consider 0 as missing data, recommended for per_cell
whether to output object instead of cor matrix
output cor matrix or called seurat object (deprecated, use obj_out instead)
only output a result vector in the same order as metadata
prefix to add to type and r column names
identity calling minimum correlation score threshold, only used when obj_out = TRUE
option to remove clusters with too few cells
a vector of gene names to throw out of query
input data is natural log, averaging will be done on unlogged data
for GO term analysis, organism name: human - 'hsapiens', mouse - 'mmusculus'
name for saved pdf, if NULL then no file is written (default)
name for saved rds of rank_diff, if NULL then no file is written (default)
test all ref clusters for unassigned results
if providing a seurat object, use the variable genes (stored in seurat_object@var.genes) as the query_genes.
stored dimension reduction
Value
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
Examples
# Annotate a matrix and metadata
clustify(
input = pbmc_matrix_small,
metadata = pbmc_meta,
ref_mat = cbmc_ref,
query_genes = pbmc_vargenes,
cluster_col = "RNA_snn_res.0.5",
verbose = TRUE
)
#> using # of genes: 599
#> similarity computation completed, matrix of 9 x 13, preparing output
#> B CD14+ Mono CD16+ Mono CD34+ CD4 T CD8 T DC
#> 0 0.6443057 0.5213433 0.5625255 0.6480900 0.8891488 0.8707703 0.5419174
#> 1 0.6268758 0.5373744 0.5866177 0.6265186 0.8610826 0.8347482 0.5541873
#> 2 0.5279272 0.9145428 0.8919954 0.5103056 0.4636934 0.4381450 0.7643448
#> 3 0.9093577 0.5347126 0.5908755 0.6248639 0.6411407 0.6269530 0.6036723
#> 4 0.5791953 0.4853982 0.5498177 0.5663773 0.7657468 0.7692801 0.5187820
#> 5 0.5380921 0.8429769 0.9294914 0.5331927 0.4857454 0.4643569 0.7235944
#> 6 0.5042912 0.4399003 0.5092065 0.5350808 0.6755032 0.6926741 0.4817286
#> 7 0.6088123 0.7415535 0.7520484 0.5694074 0.4951613 0.4789794 0.8493399
#> 8 0.1456751 0.2571662 0.2867421 0.2455947 0.1422081 0.1208273 0.1898078
#> Eryth Memory CD4 T Mk Naive CD4 T NK pDCs
#> 0 0.5528441 0.7277404 0.2668581 0.6967073 0.6880495 0.4631464
#> 1 0.5395170 0.7128879 0.2635539 0.6871755 0.6999959 0.4696025
#> 2 0.4559350 0.4553454 0.4227047 0.4564707 0.5917870 0.4738376
#> 3 0.4817829 0.4803367 0.2241017 0.5407149 0.5484213 0.5823418
#> 4 0.5007841 0.6323214 0.2961827 0.6036230 0.8257838 0.4536589
#> 5 0.4604308 0.4614474 0.3937726 0.4574654 0.6028156 0.4797367
#> 6 0.4578726 0.5665250 0.2783269 0.5505877 0.8940904 0.4185162
#> 7 0.4470039 0.4764929 0.2948557 0.4475783 0.5643991 0.6825388
#> 8 0.3068445 0.1457308 0.7319575 0.1609963 0.1396979 0.2152614
# Annotate using a different method
clustify(
input = pbmc_matrix_small,
metadata = pbmc_meta,
ref_mat = cbmc_ref,
query_genes = pbmc_vargenes,
cluster_col = "RNA_snn_res.0.5",
compute_method = "cosine"
)
#> using # of genes: 599
#> similarity computation completed, matrix of 9 x 13, preparing output
#> B CD14+ Mono CD16+ Mono CD34+ CD4 T CD8 T DC
#> 0 0.7986227 0.7404425 0.7704809 0.8194404 0.9493149 0.9444251 0.7508430
#> 1 0.7865337 0.7486511 0.7797697 0.8075621 0.9318837 0.9235744 0.7594695
#> 2 0.7527223 0.9627275 0.9460084 0.7577338 0.7096110 0.7061112 0.8919577
#> 3 0.9669867 0.7149343 0.7720125 0.8004738 0.7635724 0.7657181 0.8062767
#> 4 0.7510063 0.7070527 0.7467587 0.7677927 0.8562280 0.8674614 0.7351337
#> 5 0.7590829 0.9172636 0.9762851 0.7746692 0.7286717 0.7274581 0.8800476
#> 6 0.6784059 0.6671718 0.7107553 0.7238537 0.7777913 0.7973584 0.6864934
#> 7 0.8230905 0.8842235 0.9086642 0.8037140 0.7246647 0.7260570 0.9560634
#> 8 0.5463488 0.5932935 0.6068613 0.5953661 0.5558004 0.5516889 0.5730797
#> Eryth Memory CD4 T Mk Naive CD4 T NK pDCs
#> 0 0.7181980 0.8877208 0.7125300 0.8847456 0.8097839 0.7246787
#> 1 0.7101214 0.8855087 0.7122113 0.8731255 0.8102022 0.7241459
#> 2 0.6951238 0.7557225 0.7911713 0.7542713 0.7675894 0.7495702
#> 3 0.6514282 0.7123319 0.6594086 0.7494337 0.6957714 0.7863873
#> 4 0.6826181 0.8232194 0.7188642 0.8123971 0.9018633 0.7085898
#> 5 0.6880012 0.7601400 0.7722167 0.7561998 0.7776798 0.7477358
#> 6 0.6500106 0.7578212 0.6960715 0.7544227 0.9480188 0.6812692
#> 7 0.6874076 0.7519049 0.7448677 0.7491931 0.7595771 0.8503681
#> 8 0.5898023 0.5655621 0.8775257 0.5455913 0.5569351 0.5756617
# Annotate a SingleCellExperiment object
sce <- sce_pbmc()
clustify(
sce,
cbmc_ref,
cluster_col = "clusters",
obj_out = TRUE,
per_cell = FALSE,
dr = "umap"
)
#> object data retrieval complete, moving to similarity computation
#> Variable features not available, using all genes instead
#> consider supplying variable features to `query_genes` argument.
#> using # of genes: 599
#> similarity computation completed, matrix of 9 x 13, preparing output
#> using threshold of 0.7
#> class: SingleCellExperiment
#> dim: 2000 2638
#> metadata(0):
#> assays(2): counts logcounts
#> rownames(2000): PPBP LYZ ... CLIC2 HEMGN
#> rowData names(0):
#> colnames(2638): AAACATACAACCAC AAACATTGAGCTAC ... TTTGCATGAGAGGC
#> TTTGCATGCCTCAC
#> colData names(8): cell_source sum ... type r
#> reducedDimNames(1): UMAP
#> mainExpName: NULL
#> altExpNames(0):
# Annotate a Seurat object
so <- so_pbmc()
clustify(
so,
cbmc_ref,
cluster_col = "seurat_clusters",
obj_out = TRUE,
per_cell = FALSE,
dr = "umap"
)
#> object data retrieval complete, moving to similarity computation
#> using # of genes: 356
#> similarity computation completed, matrix of 9 x 13, preparing output
#> using threshold of 0.7
#> An object of class Seurat
#> 2000 features across 2638 samples within 1 assay
#> Active assay: RNA (2000 features, 2000 variable features)
#> 2 layers present: counts, data
#> 1 dimensional reduction calculated: umap
# Annotate (and return) a Seurat object per-cell
clustify(
input = so,
ref_mat = cbmc_ref,
cluster_col = "seurat_clusters",
obj_out = TRUE,
per_cell = TRUE,
dr = "umap"
)
#> object data retrieval complete, moving to similarity computation
#> using # of genes: 356
#> similarity computation completed, matrix of 2638 x 13, preparing output
#> using threshold of 0.55
#> An object of class Seurat
#> 2000 features across 2638 samples within 1 assay
#> Active assay: RNA (2000 features, 2000 variable features)
#> 2 layers present: counts, data
#> 1 dimensional reduction calculated: umap