Transform.pdf
Transform expression values
Overview
This module transforms all values in an -Omic Data object, using one of the available Transformation methods
Input Data Requirements
It works on -Omic data types.
To run this module, type MicroArray | Preprocess | Transform.
General Options
Input/Output
- Project & Data: The window includes a dropdown box to select the Project and Data object to be filtered.
- Variables: Selections can be made on which variables should be included in the filtering (options include All variables, Selected variables, Visible variables, and Customized variables (select any pre-generated Lists)).
- Observations: Selections can be made on which observations should be included in the filtering (options include All observations, Selected observations, Visible observations, and Customized observations (select any pre-generated Lists).
- Output name: The user can choose to name the output data object.
Options
- The user can Add a constant to the transformed data, or Multiply by a constant when transforming the data.
- Array Studio allows the user to censor the value if less than a specified value or greater than a specified value. For microarray data, this could be important, as log transforming a value of <1 will result in a negative number. This will prevent that from occurring.
- Transformation methods available include: No transformation, Log2, SLog2, Exp2, SExp2,Nexp2, Log10, NLog10, Exp10, NExp10, Log, Exp, and Percentile.
- User can also mark values as missing of it is less or greater than a cutoff.
- Finally, the user has the option of performing a variety of numeric calculations, using the values for each chip in a numeric design column (Divide, Divided by, Multiply, Add, Subtract, and Subtracted By). This will only be active if the dataset in question contains design columns that have a numeric column type.
The Output type will either be set to Change input data, in which case the original Data object will be permanently changed, or if the user enters a name in the Output name field, the Output type will switch to Transformed Microarray Data, and a new Data object will be created in the Solution Explorer.
|
No Transformation |
Y=X |
This is the default option and leaves the data unchanged |
|
Abs |
Y=Abs(X) |
Report the absolute value of each data point (i.e. remove '-' from values) |
|
Log2 |
Y= log2(X) |
|
|
SLog2 |
Y= sign(X)*(log2(|X|)) |
Performs a logarithm to the base 2 transformation of the absolute value and then applies the appropriate sign. |
|
Exp2 |
Y=2^X |
Exponential base 2 function. Can be used to un-log the Log2 transformed data |
|
SExp2 |
Y=sign(X)*2^|X| |
Performs an absolute value exponential base 2 function and then applies the appropriate sign. |
|
NExp2 |
Y=2^(-x) |
Negative exponential base 2 function. |
|
Log10 |
Y= log10(X) |
Performs a base 10 logarithm transformation |
|
NLog10 |
Y=- log10(X) |
The natural logarithm is the logarithm to the base e, where e is an irrational constant approximately equal to 2.718281828. |
|
Exp10 |
Y=10^X |
This used to mean 10 raised to the power of x. This is the inverse function of log 10(x). |
|
NExp10 |
Y=10^(-x) |
Negative exponential base 10 function. |
|
Log |
Y= loge(X) |
This means logarithm to base e. |
|
Exp |
Y=e^X |
This means the exponential function of x. |
|
Percentile |
p = (R/N)*100 |
p = percentile rank R = rank-order position (1, 2, 3...) of non-missing indices N = total number of data points |
|
ArcSinh |
Y = Log(x + Sqrt(x*x+1)) |
Inverse hyperbolic sine transformation |
It is highly recommended that users log-transform MicroArray expression data when running high-dimensional data analysis.
Output Results
The Output type will either be set to Change input data, in which case the original Data object will be permanently changed, or if the user enters a name in the Output name field, the Output type will switch to Normalized Microarray Data, and a new Data object will be created in the Solution Explorer.
WARNING: If users don't specify output name, the original MicroArray or -Omic data object will be overwritten by the new transformed data.
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