Seurat小提琴图添加多组P值：全局及组间比较异常排查

Hey there, let's work through why you're seeing that missing value warning and not getting any P-values displayed. Here's a step-by-step breakdown of the fixes:

1. First, Resolve the Missing Value Warning #

The "Removed 6 rows containing missing values" alert tells us some cells have NA values for trans_score1, which breaks the statistical tests. Let's clean that up first:

# Check how many NA values exist in your target feature
sum(is.na(fib_395@meta.data$trans_score1))

# Filter out cells with NA values for trans_score1
fib_395 <- fib_395[, !is.na(fib_395@meta.data$trans_score1)]

2. Verify Your Groups Exist & Have Valid Data #

It’s easy to mistype group names or end up with groups that have zero cells—both issues will make stat_compare_means fail silently. Let’s confirm your comparison groups are present and have enough data:

# Replace "group_column" with the actual name of your grouping column in meta.data
table(fib_395@meta.data$group_column)

Double-check that all groups in my_comparisons appear in this table and have at least 2 cells each (you can’t run statistical tests on groups with 0 or 1 cell).

3. Fix the Plot Syntax & Label Positioning #

Two common pitfalls here are:

• Forgetting to specify group.by in VlnPlot (it defaults to cell identities, which might not match your treatment groups)
• Overlapping labels from global and pairwise tests, making them invisible

Here’s the corrected code:

# Keep your comparison list as is if group names are confirmed correct
my_comparisons <- list( c("hIgG2_mIgG2", "NIS793_mIgG2"), 
                        c("hIgG2_aIL_1b", "NIS793_aIL_1b"))

# Plot with explicit grouping, pairwise tests, and a positioned global test
VlnPlot(fib_395, 
        features = "trans_score1", 
        pt.size = 0,
        group.by = "group_column")  # Replace with your actual grouping column name
  + stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")  # Pairwise non-parametric test
  + stat_compare_means(method = "kruskal.test",  # Global test for multiple groups
                       label.y = max(fib_395@meta.data$trans_score1, na.rm = TRUE) * 1.1)  # Position above the tallest violin

• wilcox.test is standard for pairwise comparisons of non-parametric single-cell data
• kruskal.test is the appropriate global test for comparing more than two groups
• The label.y parameter ensures the global P-value doesn’t overlap with pairwise test labels

4. Double-Check Group Name Spelling #

Even a tiny typo (like uppercase vs lowercase) will break the comparisons. Cross-reference the names in my_comparisons with the output from the table() command in step 2 to ensure perfect matches.

After following these steps, your P-values should display correctly, and the missing value warning should disappear!

内容的提问来源于stack exchange，提问作者mmpp