Normalizing columns

A number of normalization methods can be written as expressions, or used when transforming data. See the links at the end of this topic for a description of the theory behind each method.

Note: Some of the functionality described here can only be authored or accessed using the installed Spotfire client.

In the expression examples below, the following values are used:

Columns: E and A, where E is the column to normalize and A is a baseline column.

Percentile value: P

Normalize by mean

[E]/Avg([E])

[E]*Avg([A])/Avg([E])

Normalize by trimmed mean

[E]/TrimmedMean([E], P)

[E]*TrimmedMean([A], P)/TrimmedMean([E], P)

Normalize by percentile

[E]/Percentile([E], P)

[E]*Percentile([A], P)/Percentile([E], P)

Scale between 0 and 1

If(Max([E])= Min([E]), 0.5, ([E]-Min([E]))/(Max([E])-Min([E])))

Subtract the mean

[E]-Avg([E])

Subtract the median

[E]-Median([E])

Normalization by signed ratio

If([E]>[A], [E]/[A], -[A]/[E])

Normalization by log ratio

Log10([E]/[A])

Normalization by log ratio in standard deviation units

Log10([E]/[A])/StdDev(Log10([E]/[A]))

Z-score calculation

([E]-Avg([E]))/StdDev([E])

Normalize by standard deviation

[E]/StdDev([E])