Parameters | Workflows | Normalization Method

Normalization Method
Select a normalization method.
Choices are summarized in the table below. Available choices differ depending on the process.
Centers each column to mean zero (0) and scales each to variance one (1).
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 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 (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.
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.
Quantile Normalization is applied to the training data set, and all data except for the training data set is normalized.
For more information, refer to the Data Standardize process description.
To Specify a Multiple Testing Method:
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