脚本更新---借助banksy的力量实现HD数据的多样本整合

suppressMessages({
library(Seurat)
library(argparse)
library(dplyr)
library(ggplot2)
library(Banksy)
library(SeuratWrappers)
library(harmony)
})


sample1 <- Load10X_Spatial(data.dir = '/home/samples/DB/Spatial/visium_data/HC_1L', bin.size = c(16))

sample2 <- Load10X_Spatial(data.dir = '/home/samples/DB/Spatial/visium_data/HC_1T', bin.size = c(16))

sample1 <- FindVariableFeatures(sample1)
sample2 <- FindVariableFeatures(sample2)
sample1 <- ScaleData(sample1)
sample2 <- ScaleData(sample2)

# we select 50,0000 cells and create a new 'sketch' assay
sample1 <- SketchData(
  object = sample1,
  ncells = 50000,
  method = "LeverageScore",
  sketched.assay = "sketch"
)


# we select 50,0000 cells and create a new 'sketch' assay
sample2 <- SketchData(
  object = sample2,
  ncells = 50000,
  method = "LeverageScore",
  sketched.assay = "sketch"
)

DefaultAssay(sample1) <- "sketch"
DefaultAssay(sample2) <- "sketch"

# perform clustering workflow
sample1 <- FindVariableFeatures(sample1)
sample1 <- ScaleData(sample1)
sample1 <- RunPCA(sample1, assay = "sketch", reduction.name = "pca.sketch")

sample2 <- FindVariableFeatures(sample2)
sample2 <- ScaleData(sample2)
sample2 <- RunPCA(sample2, assay = "sketch", reduction.name = "pca.sketch")

####banksy

sample1 <- RunBanksy(sample1,
  lambda = 0.8, verbose = TRUE,
  assay = "Spatial.016um", slot = "data", features = "variable",
  k_geom = 50
)

sample2 <- RunBanksy(sample2,
  lambda = 0.8, verbose = TRUE,
  assay = "Spatial.016um", slot = "data", features = "variable",
  k_geom = 50
)

####PCA
DefaultAssay(sample1) <- "BANKSY"
sample1<- RunPCA(sample1, assay = "BANKSY", reduction.name = "pca.banksy", features = rownames(object), npcs = 30)

DefaultAssay(sample2) <- "BANKSY"
sample2 <- RunPCA(sample2, assay = "BANKSY", reduction.name = "pca.banksy", features = rownames(object), npcs = 30)

ifnb <- IntegrateLayers(object = ifnb, 
      method = CCAIntegration, 
      orig.reduction = "pca", 
      new.reduction = "integrated.cca",
      verbose = FALSE)

ifnb = list(sample1,sample2)
ifnb <- IntegrateLayers(object = ifnb, 
      method = CCAIntegration, 
      orig.reduction = "pca.banksy", 
      assay = "BANKSY",
      new.reduction = "integrated.cca",
      verbose = FALSE)

# re-join layers after integration
ifnb[["Spatial.016um"]] <- JoinLayers(ifnb[['Spatial.016um']])

ifnb <- FindNeighbors(ifnb, reduction = "integrated.cca", dims = 1:30)
ifnb <- FindClusters(ifnb, cluster.name = "banksy_cluster", resolution = 0.5)

ifnb <- IntegrateLayers(
  object = ifnb, method = RPCAIntegration,
  orig.reduction = "pca.banksy", new.reduction = "integrated.rpca",
  assay = "BANKSY",
  verbose = FALSE
)

ifnb <- IntegrateLayers(
  object = ifnb, method = HarmonyIntegration,
  orig.reduction = "pca.banksy", new.reduction = "harmony",assay = "BANKSY",
  verbose = FALSE
)