Normalize
Gene scoring based on statistics of their expression profiles or information content about cell types.
- Inputs
- Data
- input data set
- Outputs
- Data
- normalized data
- Preprocessor
- preprocessing method
Input to the Normalize widget is a raw count gene expression data matrix with cells in rows and genes in columns. The widget implements a two-step normalization. In the first step, the data is equalized so that for all the cells in a group their median expression value is equal. Zero-valued entries are ignored. In the second step, the data is log-transformed.
The widget outputs, along with normalized data, also a normalization preprocessor. It can be sent to widgets for modeling and model scoring that can use normalization as one of its preprocessing steps.

- Information about the input single cell expression data.
- Equalization step. The cells with the same value of a chosen categorical feature form a group. If no feature is chosen, all cells in the data set will be equalized to have the same gene expression mean.
- Log transform of the gene expression values.
- Tick to automatically process input data and send the result of normalization to the output. If left unchecked, normalization must be triggered manually.