calculate the variance across the samples

Question

[Omics data mining]

Hello everyone

I am working on spatial transcriptome 10X visum. In order to check the variance across biological replicates, I used followed steps :

1) Read data1 and remove the mitochondrial percentage in object meta data

library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)

data.dir <- "/home/outs"

data1 <- Load10X_Spatial(
  data.dir,
  filename = "filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "C1",
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = NULL,

)

# store mitochondrial percentage in object meta data
ss <- PercentageFeatureSet(data1, pattern = "^MT-", col.name = "percent.mt")



 ss = ss[, ss$nFeature_Spatial > 200 & ss$percent.mt < 25]

ss <- SCTransform(ss, assay = "Spatial", verbose = FALSE)

ss <- RunPCA(ss, assay = "SCT", verbose = FALSE)


as_tibble(HVFInfo(ss),rownames = "Gene") -> variance.data

2) extract the variance in data1 

variance.data %>%  mutate(hypervariable=Gene %in% VariableFeatures(ss)) -> variance.data


############################### data2 ################################# 

3) Read data2 and remove the mitochondrial percentage in object meta data 


library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)

data.dir2 <- "/home/outs2"

data2 <- Load10X_Spatial(
  data.dir2,
  filename = "filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "D1",
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = NULL,

)


# store mitochondrial percentage in object meta data
ss2 <- PercentageFeatureSet(data2, pattern = "^MT-", col.name = "percent.mt")



 ss2 = ss2[, ss$nFeature_Spatial > 200 & ss$percent.mt < 25]

ss2 <- SCTransform(ss2, assay = "Spatial", verbose = FALSE)

ss2 <- RunPCA(ss2, assay = "SCT", verbose = FALSE)


4) extract the variance in data2


as_tibble(HVFInfo(ss2),rownames = "Gene") -> variance.data2

variance.data2 %>%  mutate(hypervariable=Gene %in% VariableFeatures(ss2)) -> variance.data2



############# plot ############################################# 

5) visualization of the variance in each seurat object 

variance.data2$group <- rep("D1", nrow(variance.data2))


variance.data$group <- rep("C1", nrow(variance.data))


 dataf <- rbind(variance.data , variance.data2)

    enter code here

dataf$group <- as.factor(dataf$group)

p <- ggplot(dataf, aes(x=group, y=residual_variance, color=group, shape=group)) + 
  geom_jitter(position=position_jitter(0.2))+
  labs(title="Plot of residual_varianc by group",x="group", y = "residual_variance")
p+scale_color_brewer(palette="Dark2")

I would like to be sure for the steps that I have followed for the comparison of variance across the samples. I would appreciate all the suggestions.