Differential expression analysis can be used to compare cell types. Here, we will compare malignant and oligodendrocyte cells to identify genes differentially regulated in malignant cells from the Oligodendroglioma subtype. This comparison is of interest because malignant cells in Oligodendroglioma are thought to originate from oligodendrocytes. 

Normalize counts

To eliminate any zero values in the data set, add a small offset. 

The normalization options for single-cell RNA-Seq data are the same as for pooled or bulk RNA-Seq data.

Filter samples

To analyze only the Oligodendroglioma subtype, we can filter the samples.

A Filtered Counts data node will be created with only cells that are from Oligodendroglioma samples and are classified as either malignant or oligodendrocyte. 

Identify differentially expressed genes

The configuration options include cell-level attributes. Some of these are inherited from the sample level, like Type. Here, we want to compare only cells of a particular type from particular classification so we will include Type and Classification. 

Next, we will set up a comparison between malignant cells and oligodendrocytes in Oligodendroglioma.

Because we are analyzing sparse data, we need to relax the default low expression filter. By default the low expression filter is set a minimum of 1 average normalized read; here, we will turn it off.