Normalization Method

Select a normalization method.

Choices are summarized in the table below. Available choices differ depending on the process.

Normalization Method	Definition
NONE	• No normalization.
STD	• Centers each column to mean zero (0) and scales each to variance one (1).
MEAN	• Centers to mean zero (0).
MEDIAN	• Centers to median zero (0).
IQR	• Sets the interquartile range to one (1).
RPM	• RPM (Reads Per Million) is a straightforward normalization method for Count data. It divides the raw read count by the total reads or the total mapped reads, and multiplies the result by 1,000,000. • Refer to the RPM Scaling process description for additional information.
Upper Quartile	• Upper Quartile Scaling applies a scaling factor based on upper quartile to scale each column. The within column upper quartile is calculated by excluding the rows of all 0 (or missing ) values. Each upper quartile is further standardized by dividing by the geometric mean among all upper quartiles across columns to be the upper quartile scaling factors. • Refer to the Upper Quartile Scaling process description for additional information.
TMM	• TMM (Trimmed Mean of M component) is a scaling normalization method for RNA-Seq data. The M and A components between the targeting sample (under normalization) and the reference sample are calculated for selecting partial data to take the weighted trimmed mean of the M component as the scaling factor corresponding to the targeting sample. • A certain percentage of the data in the lower and higher range of the M and A components are trimmed out before taking the mean of the M component. • Refer to the TMM Normalization process description for additional information.
KDMM	• KMM (Kernel Density Mean of M component) is similar to TMM. The M and A components between the targeting sample (under normalization) and the reference sample are calculated for estimating the two-dimensional Kernel Density and applying the density for the weighted mean of the M component as the scaling factor corresponding to the targeting sample. • Refer to the KDMM Normalization process description for additional information.
Kernel Density Quantile	• Selects a subset as the training data based on Kernel Density. • Quantile Normalization is applied to the training data set , and all data except for the training data set is normalized.
Kernel Density Loess	• Applies Kernel Density as a weight for Loess modeling.

For more information, refer to the Data Standardize process description.

To Specify a Multiple Testing Method:

Click the radio button corresponding to your choice.