ORdensity (version 1.0)

ORdensity-class: S4 class for representing potential differentially expressed genes

Description

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

Slots

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 <U+2018>Gene1<U+2019>, ..., <U+2018>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 \((1-\alpha)100\) 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 Mart<U+00ED>nez-Otzeta, J. M. et al. 2020; Irigoien, I., and Arenas, C. 2018).

FP

Average number of false positive permuted cases in the neighbourhood (See Mart<U+00ED>nez-Otzeta, J. M. et al. 2020; Irigoien, I., and Arenas, C. 2018).

dFP

Average density of false positive permuted cases in the neighbourhood (See Mart<U+00ED>nez-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.

Examples

# NOT RUN {
# 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)
# }