Intro
Normalization is an important consideration for any analysis, and is common to include in many sequencing datasets.
Performing normalization will generate a new icon meant (generically) to indicate that an adjustment has been applied, moving (or reshaping) data:
Within the normalization platform different lists of parameters can be added. Adding a new parameter list will automatically include all previously non-normalized parameters (not included in the prior parameter lists):
Each parameter list added to the normalization platform will be normalized in the order of those parameter listed. And only one normalization can be applied to a given parameter per sample/workspace. In other words parameters included in two parameter lists will only be normalized in the first list. If you leave the default parameter list for all parameters non-normalized prior to defining other parameter lists then the secondary list will be ignored (by definition).
Note: Certain types of normalization that eliminate ‘Zero’ values from the data matrix will have a performance impact on certain calculations (such as PCA).
Basic Features
Running the Platform
Select the sample on which to perform normalization and click the Normalization icon from within the Discovery band of the Analyze tab of the Workspace:
Within the Normalization platform select parameters to normalize:
Set normalization of events
Counts Per ‘N’ Reads
Researchers will frequently want to normalize data based on a particular number of counts per cell – For this type of normalization there is the “Counts Per…” option within the platform:
Before (left) and After CPM Normalization, above.
Custom
Normalization based on a parameter is meant to be used for analyses that contain a custom derived parameter which attributes normalization factors per cell (usually generated in R or other third party software). We don’t generally recommend using this feature to normalize parameters based on a non-derived feature, though in some cases this could be useful (normalization to housekeeping genes for example).
Before (left) and After Normalization to Max Expression of HouseKeeping Geneset
Advanced Features
The advanced features within this platform allow for greater control and nuance in the way parameter-sets are treated with regard to normalization.
Normalizing Parameters
Example 1 – Standardizing RNA Expression to Ab Expression:
Before (left) and After normalization to antibody parameters.
Factors
Entering a scaling factor within the “scale by” field will multiply parameter, event or sample values by that number:
Before (left) and After re-scale parameters by factor of 0.1.
Shifts
Entering a number within the “then shift by” field will slide values on the scale by a corresponding value:
If you have trouble using normalization or achieving a desired normalization reach out to support: seqgeq@flowjo.com