CINdex (version 1.0.2)

comp.heatmap: A comprehensive heatmap function that plots Chromosome and Cytoband heatmaps

Description

When the run.cin.chr and run.cyto.chr functions are called, we get Chromosome and Cytoband CIN values for various gain/loss threshold settings. This comp.heatmap function can be used to pick the best threshold for the input data. It plots heatmaps for two groups of interest (case and control) for all the input gain/loss threshold settings. By visually checking the heatmaps, the user can pick the threshold/setting that shows the best contrast between two groups of interest. Steps: #Step 1: Run cytoband CIN or chromosome CIN - using run.cin.chr() or run.cin.cyto() #Step 2: Call this function to create chromosome or cytoband level heatmaps. Pick gain/loss threshold appropriate for data. See vignette for more details.

Usage

comp.heatmap(R_or_C = "Regular", clinical.inf = NULL, genome.ucsc = NULL, in.folder.name = "output_chr_cin", out.folder.name = "output_chr_plots", plot.choice = "png", base.color = "black", thr.gain = c(2.5, 2.25, 2.1), thr.loss = c(1.5, 1.75, 1.9), V.def = 2:3, V.mode = c("sum", "amp", "del"))

Arguments

R_or_C

The value'Regular' plots chromosome level heatmap and 'Cytobands' plots cytoband level heatmaps

clinical.inf

An n*2 matrix, the 1st column is 'sample name', the second is 'label'

genome.ucsc

A Reference genome

in.folder.name

Name of folder where the Chromsome CIN or Cytoband CIN objects are present

out.folder.name

Name of folder where the Chromosome heatmaps or Cytoband heatmaps will be saved

plot.choice

A choice of whether the heatmaps should be .png or .pdf format

base.color

A choice of 'black' or 'white' base color for the heatmap (indicating no instability)

thr.gain

A threshold above which will be set as gain

thr.loss

A threshold below which will be set as loss

V.def

There are 2 different CIN definitions - normalized (value=2) and un-normalized (value=3)

V.mode

There are 3 options: 'sum', 'amp' and 'del'

Value

No value returned. If R_or_C='Regular', it will genearte chromosome level heatmap, If R_or_C='Cytobands',it will generate cytoband level heatmap

See Also

See accompanying vignette for end-to-end tutorial

Examples

###### Example 1 - Chromosome level

## Step 1: Run chromosome CIN
# This is how command should be run:
## Not run: 
# run.cin.chr(grl.seg = grl.data)
# ## End(Not run)
# For this example, we run chr CIN on one threshold only
data("grl.data")
run.cin.chr(grl.seg = grl.data, thr.gain=2.25, thr.loss=1.75, V.def=3, V.mode="sum")

## Step 2: Plot chromosome level heatmap
# This is how the command must be called:
## Not run: 
# comp.heatmap(R_or_C="Regular", clinical.inf=clin.crc, genome.ucsc=hg18.ucsctrack, thr.gain = 2.25,
# thr.loss = 1.75,V.def = 3,V.mode = "sum")
# ## End(Not run)
# For this example, we run chr heatmap on one threshold only
comp.heatmap(R_or_C='Regular', clinical.inf=clin.crc, genome.ucsc=hg18.ucsctrack, thr.gain = 2.25,
thr.loss = 1.75,V.def = 3,V.mode = "sum")

###### Example 2 - Cytoband level

## Step 1 : Run cytoband CIN
# This is how command should be run:
## Not run: 
# run.cin.cyto(grl.seg = grl.data,cnvgr=cnvgr.18.auto, snpgr=snpgr.18.auto,
# genome.ucsc = hg18.ucsctrack)
# 
# ## Step 2: Plot cytoband level heatmap
# 
# comp.heatmap(R_or_C="Cytobands", clinical.inf=clin.crc, genome.ucsc=hg18.ucsctrack,
# thr.gain=2.25, thr.loss=1.75,V.def=3,V.mode="sum")
# ## End(Not run)