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'.
|Author||Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling|
|Date of publication||None|
|Maintainer||Joern Toedling <email@example.com>|
buildMACAT: Create MACAT list from objects in workspace
compute_sliding: Compute and plot smoothing of expression values or scores...
discreteKernelize: Discretize and smooth expression values
discretize: Discretize expression values
discretizeAll: Discretize complete expression matrix
discretize_tscores: Discretize regularized t-scores
evalScoring: Score differential expression, assess significance, and...
evaluateParameters: Evaluate Performance of Kernel Parameters by Cross-validation
get_results: Access results of 'evalScoring'
html: HTML functions for MACAT.
kernelize: Smooth expression values or scores
kernelizeAll: Smooth expression data for all chromosomes
kernelizeToPython: Smooth expression values and write to file
kernels: various kernel functions for computations in MACAT
loaddatapkg: Load data package
macat-internal: Auxiliary Functions for Computations in MACAT
plot_MACATevalScoring: Plot function for MACATevalScoring objects.
preprocessedLoader: Read in data and produce MACAT list
pythondata: Flat file format
scoring: Compute (regularized) t-scores for gene expression data
stjd: Subset Microarray Data from St.Jude Children Research...