control_adjustment: Calculates a corrected control group, discovers outliers in...

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

Description

control_adjustment function finds outliers in the control group and removes them

Usage

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

Arguments

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

Details

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

Value

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

Author(s)

Rachel Jeitziner

See Also

hyperrectangle_deviation_assessment, ttmap ttmap_sgn_genes

Examples

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    ##--
    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);

jeitziner/TTMap documentation built on May 23, 2019, 4:24 p.m.