Description Creating Objects Slots Methods Author(s) References See Also Examples

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

In practice, users will use the `pairedStat`

function to
construct an object of the `NewmanPaired`

class. Hand
construction is strongly discouraged.

`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.

- 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.

Kevin R. Coombes [email protected]

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.

1 | ```
showClass("NewmanPaired")
``` |

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