空间主题:从空间解析转录组数据探索肿瘤空间结构的统计学习框架

##install.packages('devtools')
##devtools::install_github('compbioNJU/SpaTopic')
library(SpaTopic)

load("data/spot_clusters.rda")
load("data/spot_celltype.rda")

#spot_clusters[1:5,1:5]
#      row col sizeFactor cluster.init spatial.cluster
#10x10  10  10  4.7761108            1               2
#10x13  10  13  1.0052199            2               2
#10x14  10  14  0.8106812            2               2
#10x15  10  15  0.4987377            2               2
#10x16  10  16  0.4346143            2               2

#spot_celltype[1:5,1:5]
#      Acinar_cells Ductal_cells Cancer_clone_A Cancer_clone_B         DCs
#10x10 5.838572e-02    0.2349066   1.365076e-03   3.892868e-04 0.165860789
#10x13 4.807943e-05    0.9984677   1.654640e-06   9.032885e-06 0.001244634
#10x14 4.701190e-02    0.8373601   4.846860e-03   9.009235e-04 0.003541947
#10x15 5.047613e-02    0.8020465   1.911570e-04   3.325224e-02 0.084113110
#10x16 4.694120e-03    0.9718078   1.719378e-06   6.266388e-04 0.007665514

#result_list: A list with three data frame and one vector. 
#MetaTopic 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. 
#Cell_topic is a vector of which topic be chosen in each CellTopic. 
#If meta.cell = TRUE, one more result will be given in result list, MetaTopic is a data frame of the cluster result of CellTopic.

result_list <- CellTopic(spot_celltype,spot_clusters,cluster = "spatial.cluster", num_topics = 13,percent = 0.7,
            Binarization = FALSE, meta.cell = FALSE, k = NULL)

#show the result
head(result_list[["CellTopic"]])
       CellTopic        CellTopic1        CellTopic2         CellTopic3         CellTopic4
10x10 CellTopic2 0.577382618544802 0.787303032098654 0.0080170243865711 0.0853445821596965
10x13 CellTopic2 0.577382618544802 0.787303032098654 0.0080170243865711 0.0853445821596965
10x14 CellTopic2 0.577382618544802 0.787303032098654 0.0080170243865711 0.0853445821596965
10x15 CellTopic2 0.577382618544802 0.787303032098654 0.0080170243865711 0.0853445821596965
10x16 CellTopic2 0.577382618544802 0.787303032098654 0.0080170243865711 0.0853445821596965
10x17 CellTopic2 0.577382618544802 0.787303032098654 0.0080170243865711 0.0853445821596965

head(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

head(result_list[["celltype_topic"]])
               CellTopic1 CellTopic2   CellTopic3 CellTopic4
Acinar_cells   0.04503436 0.03515437 5.404895e-02 0.17033144
Ductal_cells   0.11062714 0.14213899 3.273553e-06 0.02809054
Cancer_clone_A 0.03090381 0.02157599 1.770954e-01 0.01540925
Cancer_clone_B 0.02943409 0.01671419 1.662732e-01 0.01086386
DCs            0.07268275 0.06557971 2.767503e-02 0.14179855
Tuft_cells     0.06113322 0.04374492 5.155769e-02 0.14100323

head(result_list[["Cell_topic"]])
    CellTopic1     CellTopic2     CellTopic3     CellTopic4 
"3_11_4_5_7_2"   "2_8_1_11_3"         "9_12"        "13_10"