control_adjustment: Calculates a corrected control group, discovers outliers in...
In TTMap: Two-Tier Mapper: a clustering tool based on topological data analysis

Description Usage Arguments Details Value Author(s) See Also Examples

control_adjustment function finds outliers in the control group and removes them

1 2	control_adjustment(normal.pcl, tumor.pcl, normalname, dataname, org.directory = "", A = 1, e = 0, meth = 0, P = 1.1, B = 0)

`normal.pcl`	the control matrix with annotation as obtained by $CTRL from `make_matrices`
`tumor.pcl`	the disease/test data matrix with annotation as obtained by $TEST from `make_matrices`
`normalname`	A name for the corrected control files
`dataname`	the name of the project
`org.directory`	where the outputs should be saved
`A`	integer if A=0 then the difference to the median is calculated otherwise the difference to the mean.
`e`	integer giving how far to the median an outlier is at least
`meth`	value or method that defines how to replace outliers, default is set to replace by the median
`P`	if more than P percent of features are outliers the feature is removed, by default all are kept
`B`	Batch vector a vector for normal and test samples with a same number corresponding to a same batch

control_adjustment calculates a corrected control group, discovers outliers in it.

Several files are created

`paste(org.directory,normalname,".normMesh",sep = "")`	The normal matrix with only common features with the test matrix. This file is only created if the two have different rows
`paste(org.directory,dataname,".normMesh",sep = "")`	The test matrix with only common features with the normal matrix. This file is only created if the two have different rows.
`mean_vs_variance.pdf`	A pdf showing a plot of the mean (X axis) against the variances (Y axis) of each feature
`mean_vs_variance_after_correction.pdf`	A pdf showing a plot of the mean (X axis) against the variances (Y axis) of each feature after correction of the control group
`na_numbers_per_row.txt`	number of outliers per row
`na_numbers_per_col.txt`	number of outliers per column

And values of ttmap_part1_ctrl_adj

`e`	Selected criteria for what is an outlier
`tag.pcl`	Annotation of features, ID of features and weight
`Normal.mat`	The control matrix without annotation and only with the common rows with Disease.mat
`Disease.mat`	The test/disease matrix without annotation and only with the common rows with Disease.mat
`flat.Nmat`	A list $mat being the corrected control matrix $m a record of the different numbers of removed genes per sample
`record`	numbers recording the number of columns in Disease.mat and Normal.mat
`B`	The batch vector B introduced in the begining
`U1`	The different batches in Normal.mat
`U2`	The different batches in Disease.mat

Rachel Jeitziner

hyperrectangle_deviation_assessment,ttmap 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);