06-data-normalization.Rmd

# Data normalization

In this chapter, we will introduce the method of data normalization in scMINER.

We recommend to use **`log2CPM`** method for normalization: the raw counts in each cell are normalized to a library size of 1 million, followed by log2 transformation.

```{r normalize-eset}
pbmc14k_log2cpm.eset <- normalizeSparseEset(pbmc14k_filtered.eset, scale_factor = 1000000, log_base = 2, log_pseudoCount = 1)

exprs(pbmc14k_log2cpm.eset)[1:5,1:5]
```

This normalized and log-transformed SparseEset object can be directly used for Mutual Information-based clustering, network inference and other downstream analysis. 

Don't forget to save the SparseEset object after data normalization.
```{r save-eset-obj-after-normalization, eval=FALSE}
saveRDS(pbmc14k_log2cpm.eset, file = "/your-path/PBMC14k/DATA/pbmc14k_log2CPM_annotated.rds")
```