单细胞降维最佳PC数量选取

筛选标准

1.主成分累积贡献大于90%
2.PC本身对方差贡献小于5%
3.两个连续PCs之间差异小于0.1%

#开始分析----------------------------

#将要检测的seurat对象传递给sce
# sce=
  
# Determine percent of variation associated with each PC   
pct <- sce [["pca"]]@stdev / sum( sce [["pca"]]@stdev) * 100


# Calculate cumulative percents for each PC
cumu <- cumsum(pct)


# Determine which PC exhibits cumulative percent greater than 90% and % variation associated with the PC as less than 5
co1 <- which(cumu > 90 & pct < 5)[1]
co1

# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[1:length(pct) - 1] - pct[2:length(pct)]) > 0.1), decreasing = T)[1] + 1


# last point where change of % of variation is more than 0.1%.
co2

# Minimum of the two calculation
pcs <- min(co1, co2)
pcs

# Create a dataframe with values
plot_df <- data.frame(pct = pct,   cumu = cumu,   rank = 1:length(pct))


# Elbow plot to visualize 
ggplot(plot_df, aes(cumu, pct, label = rank, color = rank > pcs)) + 
  geom_text() + 
  geom_vline(xintercept = 90, color = "grey") + 
  geom_hline(yintercept = min(pct[pct > 5]), color = "grey") +
  theme_bw()

转载来源： 生信技能树jimmy

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