Learn R Programming

⚠️There's a newer version (1.46.0) of this package.Take me there.

macat (version 1.40.0)

MicroArray Chromosome Analysis Tool

Description

This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'.

Copy Link

Version

Version

1.40.0

License

Artistic-2.0

Maintainer

J Toedling

Last Published

February 15th, 2017

Functions in macat (1.40.0)

loaddatapkg

Load data package
discretize

Discretize expression values
discretizeAll

Discretize complete expression matrix
kernelize

Smooth expression values or scores
stjd

Subset Microarray Data from St.Jude Children Research Hospital (USA)
Auxiliary Computation Functions

Auxiliary Functions for Computations in MACAT
getResults

Access results of 'evalScoring'
html

HTML functions for MACAT.
buildMACAT

Create MACAT list from objects in workspace
compute.sliding

Compute and plot smoothing of expression values or scores along the chromosome
kernelizeAll

Smooth expression data for all chromosomes
discreteKernelize

Discretize and smooth expression values
discretize.tscores

Discretize regularized t-scores
kernelizeToPython

Smooth expression values and write to file
Kernels

various kernel functions for computations in MACAT
plot.MACATevalScoring

Plot function for MACATevalScoring objects.
preprocessedLoader

Read in data and produce MACAT list
evalScoring

Score differential expression, assess significance, and smooth scores along the chromosome
evaluateParameters

Evaluate Performance of Kernel Parameters by Cross-validation
scoring

Compute (regularized) t-scores for gene expression data
Python data format

Flat file format