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CellTopic

Source: R/SpaTopic.R
CellTopic.Rd

Through deconvolution and clustering information, the SpaTopic obtains CellTopics representing the spatial domains.

Usage

CellTopic(
  spot_celltype,
  spot_clusters,
  cluster = NULL,
  num_topics = NULL,
  percent = NULL,
  Binarization = FALSE,
  meta.cell = FALSE,
  k = NULL,
  method = "complete"
)

Arguments

spot_celltype

A data frame of the result of deconvolution, row is spot, col is celltype.

spot_clusters

A data frame contains clustering information for spots, row is spots.

cluster

A character. Use the first column in spot_clusters as the column name if NULL. Or provide your own column names that represent clustering information in spot_clusters.

num_topics

A integer or a vector of integer. The number of topics. default is NULL, find the best number of topics by default. if a vector of integer, find the best number of topics by the given range.

percent

A numeric from 0 to 1. The percent of topics. Default is 0.6.

Binarization

Logical indicating if to choose one topic for each CellTopic. Default is FALSE.

meta.cell

Logical indicating if return MetaTopic, which is a data frame of the cluster result of CellTopic.

k

A integer of how much MetaTopic to choose. If meta.cell = TRUE, k is Requested to given. Default is 4.

method

See hclust.

Value

A list with three data frame and one vector. "CellTopic" is a data frame which can be add to a Seurat object. The "domain_topic" is a data frame, row is CellTopic and col is domain. The "celltype_topic" is a data frame, row is celltype and col is CellTopic. The "Cell_topic" is a vector of which topic be chosen in each CellTopic. If meta.cell = TRUE, the "MetaTopic" will be given, which is a data frame of the cluster result of CellTopic.

Examples

options (warn = -1)
result_list <- CellTopic(spot_celltype,
spot_clusters,
cluster = "spatial.cluster",
num_topics = 13,
percent = 0.7,
Binarization = FALSE,
meta.cell = FALSE,
k = NULL)
head(result_list[["CellTopic"]])
#>        CellTopic        CellTopic1        CellTopic2          CellTopic3
#> 10x10 CellTopic2 0.577382618544806 0.787303032098643 0.00801702438657219
#> 10x13 CellTopic2 0.577382618544806 0.787303032098643 0.00801702438657219
#> 10x14 CellTopic2 0.577382618544806 0.787303032098643 0.00801702438657219
#> 10x15 CellTopic2 0.577382618544806 0.787303032098643 0.00801702438657219
#> 10x16 CellTopic2 0.577382618544806 0.787303032098643 0.00801702438657219
#> 10x17 CellTopic2 0.577382618544806 0.787303032098643 0.00801702438657219
#>               CellTopic4
#> 10x10 0.0853445821596951
#> 10x13 0.0853445821596951
#> 10x14 0.0853445821596951
#> 10x15 0.0853445821596951
#> 10x16 0.0853445821596951
#> 10x17 0.0853445821596951
print(result_list[["domain_topic"]])
#>            spot_domain_1 spot_domain_2 spot_domain_3 spot_domain_4
#> CellTopic1    0.78207686   0.577382619   0.174953872    0.10799194
#> CellTopic2    0.44211741   0.787303032   0.007438506    0.06603564
#> CellTopic3    0.12712585   0.008017024   0.787181571    0.03422577
#> CellTopic4    0.05105707   0.085344582   0.018005238    0.78840065
print(result_list[["celltype_topic"]])
#>                    CellTopic1 CellTopic2   CellTopic3  CellTopic4
#> Acinar_cells       0.04503436 0.03515437 5.404895e-02 0.170331437
#> Ductal_cells       0.11062714 0.14213899 3.273553e-06 0.028090541
#> Cancer_clone_A     0.03090381 0.02157599 1.770954e-01 0.015409246
#> Cancer_clone_B     0.02943409 0.01671419 1.662732e-01 0.010863856
#> DCs                0.07268275 0.06557971 2.767503e-02 0.141798548
#> Tuft_cells         0.06113322 0.04374492 5.155769e-02 0.141003229
#> pDCs               0.07777535 0.08179763 6.142109e-02 0.033754490
#> Endocrine_cells    0.07244471 0.06235805 3.860767e-02 0.144523396
#> Endothelial_cells  0.05015768 0.07488563 4.325394e-02 0.119765243
#> Macrophages        0.07516032 0.07263288 6.357115e-02 0.023052507
#> Mast_cells         0.09171737 0.06166093 5.590543e-02 0.053970823
#> T_cells_._NK_cells 0.08466616 0.06391425 4.966116e-02 0.020327972
#> Monocytes          0.07849903 0.09473090 5.823277e-02 0.056077913
#> RBCs               0.08112008 0.10899827 4.804330e-02 0.001741445
#> Fibroblasts        0.03864393 0.05411328 1.046500e-01 0.039289352
print(result_list[["Cell_topic"]])
#>     CellTopic1     CellTopic2     CellTopic3     CellTopic4 
#> "3_11_4_5_7_2"   "2_8_1_11_3"         "9_12"        "13_10"