?現(xiàn)在10x Visium數(shù)據(jù)基礎的分析思路是將每個spot看作一個細胞，然后參考單細胞轉錄組的分析思路進行分析。但是現(xiàn)在的實驗中，單個spot中包含不僅是一個細胞。如何確定每個spot中包含的細胞，對于空間轉錄組的分析是有幫助的。SPOTlight可以結合單細胞RNA測序信息反卷積空間數(shù)據(jù)，識別每個spot中的細胞類型和比例。

安裝

##安裝穩(wěn)定版

devtools::install_github(“https://github.com/MarcElosua/SPOTlight”)

##安裝開發(fā)版

devtools::install_github(“https://github.com/MarcElosua/SPOTlight”, ref = “devel”)

測試數(shù)據(jù)的獲取

可以從https://github.com/MarcElosua/SPOTlight和https://satijalab.org/seurat/v3.2/spatial_vignette.html，獲取10x空間轉錄組數(shù)據(jù)和配套的單細胞數(shù)據(jù)，作為測試數(shù)據(jù)集。

數(shù)據(jù)的加載與分析

單細胞數(shù)據(jù)：

##單細胞數(shù)據(jù)處理

sc_data <- readRDS(“../data/allen_cortex_dwn.rds”)

##常規(guī)處理

sc_data <- Seurat::SCTransform(sc_data , verbose = FALSE)

sc_data <-sc_data %>% Seurat::RunPCA(verbose = FALSE) %>%

Seurat::RunUMAP(dims = 1:30, verbose = FALSE) %>%

Seurat::FindNeighbors(dims = 1:30, verbose = FALSE) %>%

Seurat::FindClusters(verbose = FALSE)

空間數(shù)據(jù)：

#InstallData(“stxBrain”)

anterior <- LoadData(“stxBrain”, type = “anterior1”)

anterior <- Seurat::SCTransform(anterior, assay = “Spatial”, verbose = FALSE)

anterior <- anterior %>% Seurat::RunPCA(verbose = FALSE) %>%

Seurat::RunUMAP(dims = 1:30, verbose = FALSE) %>%

Seurat::FindNeighbors(dims = 1:30, verbose = FALSE) %>%

Seurat::FindClusters(verbose = FALSE)

簡單的可視化

sc_p <- DimPlot(sc_data,reduction = “umap”,label = T,group.by = “subclass”)*NoLegend()

st_p <- Seurat::SpatialDimPlot(anterior,label = T)*NoLegend()

sc_p+st_p

SPOTlight的使用

1.獲得單細胞不同亞型細胞的marker基因：

###find marker gene

Idents(object = sc_data)

Seurat::Idents(object = sc_data) <- sc_data@meta.data$subclass

cluster_markers_all <- Seurat::FindAllMarkers(object = sc_data,

assay = “SCT”,

slot = “data”,

verbose = TRUE,

only.pos = TRUE,

logfc.threshold = 1,

min.pct = 0.9)

參數(shù)設置：

1）從SCT的data數(shù)據(jù)中獲取差異基因，為了獲得所有可能的差異基因而不僅是高突變基因中的差異基因；

2）只選擇在cluster表達的基因，only.pos=T；

3）選擇在同類細胞中絕大多數(shù)細胞都表達的基因，min.pct=0.9。

2. 利用單細胞數(shù)據(jù)對空間表達數(shù)據(jù)進行反卷積：

set.seed(123)

spotlight_ls <- spotlight_deconvolution(se_sc = sc_data,

counts_spatial = anterior@assays$Spatial@counts,

clust_vr = “subclass”,

cluster_markers = cluster_markers_all,

cl_n = 50,

hvg = 3000,

ntop = NULL,

transf = “uv”,

method = “nsNMF”,

min_cont = 0.09)

?spotlight_deconvolution查看參數(shù)的詳細說明

3. 獲取每個Spot中細胞的組成比例

###獲取每個spot中的細胞比例矩陣

decon_mtrx <- spotlight_ls[[2]]

cell_types_all <- colnames(decon_mtrx)[which(colnames(decon_mtrx) != “res_ss”)]

##將信息添加到空間數(shù)據(jù)中

anterior@meta.data <- cbind(anterior@meta.data, decon_mtrx)

##可視化

SPOTlight::spatial_scatterpie(se_obj = anterior,

cell_types_all = cell_types_all,

img_path = “../data/spatial/tissue_lowres_image.png”,

pie_scale = 0.4)

4. 特定細胞的展示

###展示特定細胞所在位置

p1 <- SPOTlight::spatial_scatterpie(se_obj = anterior,

cell_types_all = cell_types_all,

img_path = “../data/spatial/tissue_lowres_image.png”,

cell_types_interest = “L6b”,

pie_scale = 0.5)

###展示特定細胞的表達情況

p2 <- SpatialFeaturePlot(anterior,

features = “L6b”,

pt.size.factor = 1,

alpha = c(0, 1)) +theme(legend.position = “right”)+

ggplot2::scale_fill_gradientn(

colours = heat.colors(10, rev = TRUE),

limits = c(0, 1))

p1+p2

5. 空間交互信息的獲取展示

在獲取了每個spot的細胞類型后，可以利用get_spatial_interaction_graph獲得細胞在空間中的相互作用的信息。

###空間交互信息

graph_ntw <- get_spatial_interaction_graph(decon_mtrx = decon_mtrx[, cell_types_all])

graph_ntw

[1] Astro–Endo Astro–L2.3.IT Astro–L4

[4] Astro–L5.IT Astro–L5.PT Astro–L6.CT

[7] Astro–L6.IT Astro–L6b Astro–Lamp5

[10] Astro–Macrophage Astro–Meis2 Astro–NP

[13] Astro–Oligo Astro–Peri Astro–Pvalb

[16] Astro–Serpinf1 Astro–SMC Astro–Sncg

[19] Astro–Sst Astro–Vip Astro–VLMC

[22] Endo –L2.3.IT Endo –L4 Endo –L5.IT

###繪圖

library(igraph)

library(RColorBrewer)

deg <- degree(graph_ntw, mode=”all”)

# Get color palette for difusion

edge_importance <- E(graph_ntw)$importance

# Select a continuous palette

qual_col_pals <- brewer.pal.info[brewer.pal.info$category == ‘seq’,]

# Create a color palette

getPalette <- colorRampPalette(brewer.pal(9, “YlOrRd”))

# Get how many values we need

grad_edge <- seq(0, max(edge_importance), 0.1)

# Generate extended gradient palette dataframe

graph_col_df <- data.frame(value = as.character(grad_edge),

color = getPalette(length(grad_edge)),

stringsAsFactors = FALSE)

# Assign color to each edge

color_edge <- data.frame(value = as.character(round(edge_importance, 1)), stringsAsFactors = FALSE) %>%

dplyr::left_join(graph_col_df, by = “value”) %>%

dplyr::pull(color)

# Open a pdf file

plot(graph_ntw,

# Size of the edge

edge.width = edge_importance,

edge.color = color_edge,

# Size of the buble

vertex.size = deg,

vertex.color = “#cde394”,

vertex.frame.color = “white”,

vertex.label.color = “black”,

vertex.label.family = “Ubuntu”, # Font family of the label (e.g.“Times”, “Helvetica”)

layout = layout.circle)

以上，便是SPOTlight的簡單使用的展示，讀者有興趣還可以將SPOTlight獲得結果與Seurat中的單細胞和空間的聯(lián)合分析的結果進行比較。