IndividualBatchEffectExploration.Rmd

# Seurat Individual Batch Effect Exploration

## Descripiton

Explore the individual batch effect by 

* Dimplot split by individual

* Fractions of individuals per cluster


## Load seurat object

```{r, cache=TRUE}
combined <- get(load('data/Demo_CombinedSeurat_SCT_Preprocess.RData'))
```

## Explore individual distribution by `Dimplot`

```{r Dimplot, cache=TRUE}
Idents(object = combined) <- 'Individual'
DimPlot(object = combined, reduction = "umap", label = T) 
DimPlot(object = combined, reduction = "tsne", label = T) 
```

## Calculate individual distribution per cluster with different resolution

```{r histogram, cache=TRUE}
df <- data.table(combined@meta.data)
sel.meta <- c("Individual", str_c('SCT_snn_res.', c(1, 1.5, 1.8)))
df <- df[, sel.meta, with = FALSE]


df[, 2:4] %>% imap(~ {
    freq1 <- df[, .N, keyby = .(.x, Individual)]
    freq1[, total := sum(N), by = .(.x)]
    freq1[, ratio := N / total]
    
    linesize = .35
    fontsize = 8
    
    ggplot(freq1, aes(fill = Individual, y = ratio, x = .x)) +
      geom_bar(position = "stack", stat = "identity") +
      scale_fill_brewer(palette = "Dark2") +
      xlab('Clsuter') +
      ggtitle(.y) +
      scale_y_continuous(breaks = seq(0, 1, 0.1),
                         expand = c(0, 0),
                         name = 'Percentage') +
      theme_bw() +
      theme(
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor = element_blank(),
        strip.background = element_blank(),
        panel.border = element_rect(size = linesize),
        axis.ticks = element_blank(),
        axis.text.x = element_text(size = 5)
      ) +
      coord_flip()
})

```