# Seurat

#### Check number of genes per cell and number of cells per gene

```
selected_c <- WhichCells(alldata, expression = nFeature_RNA > 200)
selected_f <- Matrix::rowSums(x = scRNA_obj[["RNA"]]@counts > 0) >= 100
scRNA_obj = scRNA_obj[selected_f,]
# check min number of cells expressed across all genes
min(Matrix::rowSums(x = scRNA_obj[["RNA"]]@counts > 0))
```

#### Convert h5ad to Seurat

```
library(reticulate)
library(Seurat)
sc = import("scanpy")
data <- sc$read_h5ad("RCC_upload_final_raw_counts.h5ad")
british_count_csc = data$X$transpose()
british_count_csc = british_count_csc$tocsc()

csc_to_dgc <- function(x) {
  new(
    "dgCMatrix",
    i = as.integer(reticulate:::as_r_value(x$indices)),
    p = as.integer(reticulate:::as_r_value(x$indptr)),
    x = as.vector(reticulate:::as_r_value(x$data)),
    Dim = as.integer(x$shape)
  )
  
}
british_count_dgc = csc_to_dgc(british_count_csc)
colnames(british_count_dgc) <- data$obs_names$to_list()
rownames(british_count_dgc) <- data$var_names$to_list()
# Create the Seurat object
british_seurat <- CreateSeuratObject(british_count_dgc, metadata = data$obs)
```


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