Seurat4.0单细胞数据分析 细胞pca值的计算验证

001、前期处理：

https://www.jianshu.com/p/4f7aeae81ef1

基本过程如上图：

001、左图数据为scale.data,  pbmc[["RNA"]]@scale.data

002、中图数据为feature.loadings,  pbmc[["pca"]]@feature.loadings

003、右图数据为：t(pbmc[["pca"]]@cell.embeddings)

002、验证

pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))           ## 运行PCA
std_res <- pbmc[["pca"]]@cell.embeddings                           
std_res <- t(std_res)                                                      ## 将细胞pca结果转置

scal <- pbmc[["RNA"]]@scale.data
scal <- as.data.frame(scal)
features = VariableFeatures(object = pbmc)
scal <- scal[features,]                                                    ## 提取scale数据）
dim(scal)

fea <- pbmc[["pca"]]@feature.loadings
fea <- as.data.frame(fea)                                                  ## 提取feature数据

result <- data.frame(matrix(, nrow = ncol(fea), ncol = 0))
loop = 0
for (i in 1:10) {                                                          ## 利用循环结果计算PCA值，这里计算10个循环
  loop = loop + 1
  c_temp <- vector()
  for (j in 1:ncol(fea)) {
    temp <- 0
    for (k in 1:nrow(scal)) {
      temp <- temp + scal[k,i] * fea[k,j]
    }
    c_temp[j] <- temp
  }
  cat("\nloop: ", loop)
  cat("\n---------:\n")
  result <- cbind(result, c_temp)
}
head(result)[,1:5]                                                       ## 对比计算结果和标准计算结果
head(std_res)[,1:5]

二、利用矩阵乘法实现

pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
std_res <- pbmc[["pca"]]@cell.embeddings
std_res <- t(std_res)                                      ## 标准答案

scal <- pbmc[["RNA"]]@scale.data
scal <- as.data.frame(scal)
features = VariableFeatures(object = pbmc)
scal <- scal[features,]
scal = as.matrix(scal)

scal[1:4, 1:4]
fea <- pbmc[["pca"]]@feature.loadings
fea <- as.data.frame(fea)
fea <- t(fea)
fea <- as.matrix(fea)
dim(fea)
fea[1:4, 1:4]

result = fea %*% scal                                      ## 利用矩阵相乘计算PCA
result[1:4, 1:4]                                           ## 结果比对
std_res[1:4, 1:4]

参考：https://www.bilibili.com/video/BV1Qf4y1s7h1?p=2&vd_source=9f2c87b6a7fd328f19dccf7b7b1f8c1a