PearsonMetrics (Gemma 1.31.4 API)

java.lang.Object
- ubic.gemma.core.analysis.expression.coexpression.links.AbstractMatrixRowPairAnalysis
- - ubic.gemma.core.analysis.expression.coexpression.links.PearsonMetrics

All Implemented Interfaces:

MatrixRowPairAnalysis

Direct Known Subclasses:

SpearmanMetrics
```
public class PearsonMetrics
extends AbstractMatrixRowPairAnalysis
```
A correlation analysis for a given data set, designed for selection of values based on criteria set by the user. On the first pass over the data, a histogram is filled in to hold the distribution of the values found. You can set criteria to have the correlations actually stored in a (sparse) matrix. This can take a lot of memory if you store everything! The correlation is only calculated if it isn't stored in the matrix, and values can be tested against a threshold. This class is used in reality by one pass over the data to fill in the histogram. This is used to help select a threshold. A second pass over the data is used to select correlations that meet the criteria. Probes that do not map to genes are not used.

Author:

Paul Pavlidis

Field Summary
- Fields inherited from class ubic.gemma.core.analysis.expression.coexpression.links.AbstractMatrixRowPairAnalysis
  HARD_LIMIT_MIN_NUM_USED
- Fields inherited from interface ubic.gemma.core.analysis.expression.coexpression.links.MatrixRowPairAnalysis
  NUM_BINS

Constructor Summary

Constructors
Constructor Description

PearsonMetrics(ExpressionDataDoubleMatrix dataMatrix)

PearsonMetrics(ExpressionDataDoubleMatrix dataMatrix, double tmts)

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`calculateMetrics()`	Calculate the linear correlation matrix of a matrix, allowing missing values.
`protected double`	`correlFast(double[] ival, double[] jval, int i, int j)`
`QuantitationType`	`getMetricType()`
`protected void`	`rowStatistics()`	Calculate mean and sumsqsqrt for each row

Methods inherited from class ubic.gemma.core.analysis.expression.coexpression.links.AbstractMatrixRowPairAnalysis
getCrossHybridizationRejections, getHistogramArrayList, getKeepers, getMatrix, getNumUniqueGenes, getProbeForRow, getScoreInBin, isUsePvalueThreshold, kurtosis, nullMatrix, numCached, setDuplicateMap, setLowerTailThreshold, setMinNumpresent, setOmitNegativeCorrelationLinks, setPValueThreshold, setUpperTailThreshold, setUseAbsoluteValue, setUsePvalueThreshold, size, toString

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - PearsonMetrics
```
public PearsonMetrics(ExpressionDataDoubleMatrix dataMatrix)
```
  - PearsonMetrics
```
public PearsonMetrics(ExpressionDataDoubleMatrix dataMatrix,
                      double tmts)
```
    Parameters:
    
    dataMatrix - DenseDoubleMatrix2DNamed
    
    tmts - Values of the correlation that are deemed too small to store in the matrix. Setting this as high as possible can greatly reduce memory requirements, but can slow things down.
- Method Detail
  - calculateMetrics
```
public void calculateMetrics()
```
    Calculate the linear correlation matrix of a matrix, allowing missing values. If there are no missing values, this calls PearsonFast.
  - getMetricType
```
public QuantitationType getMetricType()
```
  - rowStatistics
```
protected void rowStatistics()
```
    Calculate mean and sumsqsqrt for each row
    
    Specified by:
    
    rowStatistics in class AbstractMatrixRowPairAnalysis
  - correlFast
```
protected double correlFast(double[] ival,
                            double[] jval,
                            int i,
                            int j)
```