hyperrectangle_deviation_assessment {TTMap} | R Documentation |
hyperrectangle_deviation_assessment
function
calculates the
hyperrectangle deviation
assessment (HDA) that calculates the
deviation components using
normal_hda2
which calculates
the normal component of the test
sample and deviation_hda2
which calculates the deviation component.
hyperrectangle_deviation_assessment(x, k = dim(x$Normal.mat)[2], dataname, normalname,Org.directory = getwd())
x |
output object given back
by |
k |
A factor if not all the lines in the control group should be kept |
dataname |
the name of the project |
normalname |
A name for the corrected control files |
Org.directory |
where the outputs should be saved |
The function performs the hyperrectangle deviation assessment (HDA)
Outputs
Tdis.pcl |
The matrix of the deviation components for each test sample |
Tnorm.pcl |
The matrix of the normal components for each test sample |
NormalModel.pcl |
The normal model used |
Values
Dc.Dmat |
the deviation component matrix composed of the deviation components of all the samples in the test group |
m |
the values of the filter function per sample in the test group |
Rachel Jeitziner
control_adjustment
,
hyperrectangle_deviation_assessment
,
ttmap_sgn_genes
##a full example can be found in ttmap_sgn_genes ##-- library(airway) data(airway) airway <- airway[rowSums(assay(airway))>80,] assay(airway) <- log(assay(airway)+1,2) ALPHA <- 1 the_experiment <- TTMap::make_matrices(airway, seq_len(4), seq_len(4) + 4, rownames(airway), rownames(airway)) TTMAP_part1prime <-TTMap::control_adjustment( normal.pcl = the_experiment$CTRL, tumor.pcl = the_experiment$TEST, normalname = "The_healthy_controls", dataname = "Effect_of_cancer", org.directory = tempdir(), e = 0, P = 1.1, B = 0); Kprime <- 4; TTMAP_part1_hda <- TTMap::hyperrectangle_deviation_assessment(x = TTMAP_part1prime, k = Kprime, dataname = "Effect_of_cancer", normalname = "The_healthy_controls");