# 01-NewmanPaired-class: Class "NewmanPaired" In NewmanOmics: Extending the Newman Studentized Range Statistic to Transcriptomics

## Description

Represents the reusults of computing the Newman Paired test statistic on one or more paired samples.

## Creating Objects

In practice, users will use the `pairedStat` function to construct an object of the `NewmanPaired` class. Hand construction is strongly discouraged.

## Slots

`pairedMean`:

A matrix of size N (number of features) by S (number of sample pairs). The mean expression of each feature in each paired sample. Also called "A" in the M-versus-A plots of the microarray era.

`difference`:

A matrix of size N (number of features) by S (number of sample pairs). The difference (perturbed - base) in expression of each feature in each paired sample. Also called "M" in the M-versus-A plots of the microarray era.

`smoothSD`:

A matrix of size N (number of features) by S (number of sample pairs). The results of fitting a loess smooth to the relationship between the `PairedMean` and the observed estimate of standard deviation (i.e., `abs(difference)/sqrt(2)`).

`nuStatistics`:

A matrix of size N (number of features) by S (number of sample pairs). The Newman paired statistics, nu.

`pValues`:

A matrix of size N (number of features) by S (number of sample pairs). Empirical p-values for the Newman statistics.

## Methods

x[i,j]

Select a subset of features or sample pairs.

dim(x)

The dimension, N by S, of the object.

plot(x, y, which = NULL, ask = NULL, high = 0.99, low = 0.01, ...)

Plot the results of the analysis of one sample pair.

hist(x, breaks=101, xlab="P-value", ...)

Plot a histogram of the p-values for one sample-pair.

## Author(s)

Kevin R. Coombes [email protected]

## References

Abrams ZB, Joglekar A, Gershkowitz GR, Sinicropi-yao S, Asiaee A, Carbone DP, Coombes KR. Personalized Transcriptomics: Selecting Drugs Based on Gene Expression Profiles. Preprint.

`pairedStat`, `bankStat`
 `1` ```showClass("NewmanPaired") ```