SCS 60.单细胞空间转录组空间聚类(SPATA2)

Clustering

介绍

分组变量将样本的观测值划分为可以相互比较属性的组。例如，观测值的分组可以是聚类算法的结果或手动空间分割的结果。本教程将展示如何在SPATA2中应用和添加聚类。

# load required packages library(SPATA2) library(SPATAData) library(tidyverse)

object_t269<- readRDS("object_t269.rds") object_t269 <- updateSpataObject(object_t269)

# plot histology plotSurface(object_t269, color_by = "histology", pt_clrp = "npg")

plotImage(object_t269)

2. SPATA2 内的聚类

有许多算法可以将您的样本分成子组。SPATA2 提供了多种聚类算法的封装。那些会立即将结果添加到 SPATA2 对象中的聚类算法，其名称以 run-* 开头，并以 *-Clustering() 结尾。例如：runBayesSpaceClustering()、runKmeansClustering()、runSeuratClustering()。参数名称或命名指定了输出分组变量的名称。可通过getGroupingOptions()获取结果分组变量名称。

# current grouping options getGroupingOptions(object_t269)

## factor factor factor factor ## "tissue_section" "seurat_clusters" "histology" "bayes_space"

# run the pipeline object_t269 <- runBayesSpaceClustering( object = object_t269, name = "bayes_spacev2", # the name of the output grouping variable qs = 5 )

# run PCA based on which clustering is conducted object_t269 <- runPCA(object_t269, n_pcs = 20) object_t269 <- runKmeansClustering( object = object_t269, ks = c(7, 8), methods_kmeans = "Lloyd" )

# results are immediately stored in the objects feature data getGroupingOptions(object_t269)

## factor factor factor factor ## "tissue_section" "seurat_clusters" "histology" "bayes_space" ## factor factor factor ## "bayes_spacev2" "Lloyd_k7" "Lloyd_k8"

# left plot plotSurface( object = object_t269, color_by = "bayes_spacev2", pt_clrp = "uc" )

# right plot plotSurface( object = object_t269, color_by = "Lloyd_k7", pt_clrp = "jco" )

3. SPATA2以外的聚类

聚类可能由多种聚类算法产生。如果这些算法未在SPATA2函数中实现，可以使用addFeatures()函数将它们添加进去。唯一的要求是有一个名为barcodes的变量，用于将组映射到观测值。请注意，变量必须为因子类（factor class），才能被识别为分组变量。

# uses kmeans outside of SPATA2 kmeans_res <- stats::kmeans( x = getPcaMtr(object_t269), centers = 7, algorithm = "Hartigan-Wong" ) head(kmeans_res[["cluster"]])

## GTAGCGCTGTTGTAGT-1 TTGTTTGTGTAAATTC-1 CGTAGCGCCGACGTTG-1 GTAGACAACCGATGAA-1 ## 2 4 4 4 ## ACAGATTAGGTTAGTG-1 TGAGATCAAATACTCA-1 ## 2 2

cluster_df <- as.data.frame(kmeans_res[["cluster"]]) %>% tibble::rownames_to_column(var = "barcodes") %>% magrittr::set_colnames(value = c("barcodes", "kmeans_4_HW")) %>% tibble::as_tibble() cluster_df[["kmeans_4_HW"]] <- as.factor(cluster_df[["kmeans_4_HW"]]) cluster_df

## # A tibble: 3,213 × 2 ## barcodes kmeans_4_HW ## <chr> <fct> ## 1 GTAGCGCTGTTGTAGT-1 2 ## 2 TTGTTTGTGTAAATTC-1 4 ## 3 CGTAGCGCCGACGTTG-1 4 ## 4 GTAGACAACCGATGAA-1 4 ## 5 ACAGATTAGGTTAGTG-1 2 ## 6 TGAGATCAAATACTCA-1 2 ## 7 CTGGTCCTAACTTGGC-1 2 ## 8 TGCACGAGTCGGCAGC-1 3 ## 9 ATAGTCTTTGACGTGC-1 2 ## 10 GGGTGGTCCAGCCTGT-1 3 ## # ℹ 3,203 more rows

# grouping options before adding getGroupingOptions(object_t269)

## factor factor factor factor ## "tissue_section" "seurat_clusters" "histology" "bayes_space" ## factor factor factor ## "bayes_spacev2" "Lloyd_k7" "Lloyd_k8"

# add the cluster results to the meta features object_t269 <- addFeatures( object = object_t269, feature_df = cluster_df ) # grouping options names afterwards getGroupingOptions(object_t269)

## factor factor factor factor ## "tissue_section" "seurat_clusters" "histology" "bayes_space" ## factor factor factor factor ## "bayes_spacev2" "Lloyd_k7" "Lloyd_k8" "kmeans_4_HW"

继续通过可视化结果或使用差异表达分析（DEA）来研究其转录特征。

plotSurface( object = object_t269, color_by = "kmeans_4_HW", pt_clrp = "jama" ) + labs(color = "Kmeans HW")