CAFE (version 1.8.0)

ProcessCels:

Processing CEL files

Description

Normalizes and computes relative expressions for all CEL files in work directory

Usage

ProcessCels(threshold.over=1.5,threshold.under=(2/3),remove_method=1,
local_file=NULL)

Arguments

threshold.over

Determines the threshold, as a multiple of median value, where probes are considered overexpressed. Default is 1.5

threshold.under

Determines the threshold, as a fraction of median value, where probes are considered underexpressed. Default is 2/3

remove_method

Determines which method is used to remove multiple probesets that are annotated to map to the same gene. The default option, 1, will keep 1 probeset with the following priority: 1): nnn_at; 2): nnn_a_at; 3): nnn_s_at; 4): nnn_x_at; 5): lowest nnn if multiple probes still exist

If remove_method=2, probesets will only be removed if several probesets of the same gene map to the exact same location. In the case that many probesets map to the same location, one probeset will be retained according to the priority of option 1 above.

If remove_method=0, no multiple probesets will be removed

local_file

Use a local - previously downloaded - UCSC file (e.g. http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/ affyU133Plus2.txt.gz) instead of directly retrieving the file instead.

Value

list

$whole

named list, where each element is a data.frame corresponding to a *.CEL file - containing columns: 1): "ID" (Affy ID number); 2): "Sym" (gene Symbol); 3): "Value" (Expression values); 4): "LogRel" (Relative expressions); 5): "Loc" (Chromosomal locations); 6): "Chr" (Chromosome number); 7): "Band" (Cytoband); 8): "Arm" (Chromosomal arm)

$over

same as $whole, but contains only those probes which are deemed overexpressed

$under

same as $whole, but contains only those probes which are deemd underexpressed

Details

this function uses the RMA algorithm to normalize *.CEL files in work directory. It then computes relative expressions for every probe on every sample. Locations for probesets are downloaded from UCSC, as the standard BioConductor annotations do not map probeset location (they only map the location to the corresponding gene). Multiple probesets belonging to the same gene are removed as described above. The function then determines which probes are overexpressed and underexpressed relative to the median probeset values across all samples. Finally,the relative expressions are log2-transformed.

Examples

## Not run: 
# data <- ProcessCels()
# ## End(Not run)