-
Z-score (zScore)
: This method scales the data to have a mean
of 0 and a standard deviation of 1. It subtracts the mean and divides by the standard
deviation for each feature. Mathematically,
Z−score=sd(x)x−(x)
where x
is the value of a marker, x
is the mean of the marker and sd(x)
is the standard deviation of the marker.
-
T-score (tScore)
: T-score is commonly used
in data analysis to transform raw scores into a standardized form.
The standard formula for converting a raw score x
into a T-score is:
T−score=(sd(x)x−(x)×10)+50
where x
is the value of a marker, x
is the mean of the marker
and sd(x)
is the standard deviation of the marker.
-
Range (a.k.a. min-max scaling) (range)
: This method transforms data to
a specific range, between 0 and 1. The formula for this method is:
Range=max(x)−min(x)x−min(x)
-
Mean (mean)
: This method, which helps
to understand the relative size of a single observation concerning
the mean of dataset, calculates the ratio of each data point to the mean value
of the dataset.
Mean=xx
where x
is the value of a marker and x
is the mean of the marker.
-
Deviance (deviance)
: This method, which allows for
comparison of individual data points in relation to the overall spread of
the data, calculates the ratio of each data point to the standard deviation
of the dataset.
Deviance=sd(x)x
where x
is the value of a marker and sd(x)
is the standard deviation of the marker.