An object of class ORdensity includes all potential differentially expressed genes given microarray data measured in two experimental conditions.

`Exp_cond_1`

Matrix including microarray data measured under experimental condition 1.

`Exp_cond_2`

matrix including microarray data measured under experimental condition 2.

`labels`

Vector of characters identifying the genes, by default rownames(Exp_cond_1) is inherited. If NULL, the genes are named ‘Gene1’, ..., ‘Genen' according to the order given in

`Exp_cond_1`

.`B`

Numeric value indicating the number of permutations. By default,

`B`

=100.`scale`

Logical value to indicate whether the scaling of the difference of quantiles should be done.

`alpha`

Numeric value used by the method to calculate the percentile

*(1-α)100*of all the elements of the matrix with the permuted samples. By default 0.05.`fold`

Numeric value, by default

`fold`

=10. It controls the number of partitions.`probs`

Vector of numerics. It sets the quantiles to be considered. By default

`probs = c(0.25, 0.5, 0.75)`

.`weights`

Vector of numerics. It controls the weights given to the quantiles set in

`probs`

. By default`weights = c(1/4, 1/2, 1/4)`

.`numneighbours`

Numeric value to set the number of nearest neighbours. By default

`numneighbours=10`

.`numclustoseek`

Numeric value to set the number of maximum clusters to consider. By default

`numclustoseek=10`

.`out`

List containing the potential DE genes and their characteristics.

`OR`

Outlyingness index (See Martínez-Otzeta, J. M. et al. 2020; Irigoien, I., and Arenas, C. 2018).

`FP`

Average number of false positive permuted cases in the neighbourhood (See Martínez-Otzeta, J. M. et al. 2020; Irigoien, I., and Arenas, C. 2018).

`dFP`

Average density of false positive permuted cases in the neighbourhood (See Martínez-Otzeta, J. M. et al. 2020; Irigoien, I., and Arenas, C. 2018).

`char`

Matrix holding internal computations. Non-developers should left this parameter as default.

`bestKclustering`

Number of clusters for partitioning the data. It is advisable to let the object to automatically estimate the best partition.

`verbose`

Boolean indicating if log messages are going to be printed.

`parallel`

Boolean indicating if parallel process is used.

`nprocs`

Integer indicating the number of processors to be used. If nprocs is 0 or negative, the number of processors detected in the machine is used.

`replicable`

Boolean indicating if the same seed is used for the pseudorandom number generation.

`seed`

Integer used as seed by the pseudorandom number generator.

1 2 3 4 5 6 7 | ```
# To create an instance of a class ORdensity given data from 2 experimental conditions
simexpr_reduced <- simexpr[c(1:15,101:235),]
x <- simexpr_reduced[, 3:32]
y <- simexpr_reduced[, 33:62]
EXC.1 <- as.matrix(x)
EXC.2 <- as.matrix(y)
myORdensity <- new("ORdensity", Exp_cond_1 = EXC.1, Exp_cond_2 = EXC.2, B = 20)
``` |

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