ttest.cyto.cin.heatmap: Performs T test on cytoband level CIN data, and plots heatmap
In CINdex: Chromosome Instability Index

Description Usage Arguments Value See Also Examples

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

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

`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

#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 accompaying vignette for a detailed end to end workflow tutorial

#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)