CINdex (version 1.0.2)

ttest.cyto.cin.heatmap: Performs T test on cytoband level CIN data, and plots heatmap

Description

ttest.cyto.cin.heatmap to perform T test to find differentially expressed cytobands. It also plots a heatmap after performing heirarchical clustering. When to use this function: #Step 1: Run cytoband CIN - using run.cin.chr(). #Step 2: Plot cytoband level heatmap - using comp.heatmap(). #Step 3: Go through heatmaps as select one appropriate threshold. Load the file. #Step 4: Call this function. More details and tutorial are given in the accompanying vignette

Usage

ttest.cyto.cin.heatmap(cytobands.cin.obj, clinical.inf, genome.ucsc, file.ext = "gainT_lossT_unnorm", folder.name = "output_ttest", combine.cyto.flag = FALSE)

Arguments

cytobands.cin.obj

(eg. cytobands.cin_2.25_1.75_unnormalized_amp.Rdata), a list in which each cell is chromosome cin matrix

clinical.inf

In a clinical.inf.Rdata is a two columns array, the 1st column is samplename, the 2nd is the label

genome.ucsc

Reference sequence

file.ext

Provide a meaningful file name extension. Ideally include the gain, loss threshold settings

folder.name

Name of folder where the output files will be generated

combine.cyto.flag

Whether or not to save the combine cytobands as a uni array rather than a list

Value

#Outputs: 1. cyto.cin.uni.file.ext.Rdata (eg. cyto.cin.uni.gainT_lossT_unnormalized.Rdata) 2. Heatmaps: eg. CIN relapse-free VS relapse for gainT_lossT_unnormalized_dendrogram.pdf 3. Raw CIN array for the corresponding heatmap: #ttest.cyto.cin4heatmap.gainT_lossT_unnormalized.csv #ttest.cyto.cin4heatmap.gainT_lossT_unnormalized.Rdata 4. T test results for all cytobands on the whole genome #ttest.cytobands.cin.gainT_lossT_unnormalized.txt

See Also

See accompaying vignette for a detailed end to end workflow tutorial

Examples

#For this example, we load an example cytoband CIN data
data("cytobands.cin")
data("clin.crc") # sample names with group information
data("hg18.ucsctrack") #hg18 reference file
ttest.cyto.cin.heatmap(cytobands.cin.obj = cytobands.cin,
clinical.inf = clin.crc, genome.ucsc = hg18.ucsctrack)