Vega (version 1.20.0)

vegaMC-methods: Class aggregator of VegaMC.

Description

VegaMC enables the detection of driver chromosomal imbalances (deletions, amplifications and loss of heterozygosities (LOHs)) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of aCGH data. Segmented regions are then used into a statistical framework to distinguish between driver and passenger mutations. In this way, significant imbalances can be detected by the associated p-value. VegaMC has been implemented to be easily integrated with the output produced by PennCNV and with the Genoset eSet Objects. VegaMC produces in output two web pages allowing a rapid navigation between both detected regions and altered genes. In the web page summarizing the altered genes, the user finds the link to the respective Ensembl gene web page.

Usage

vegaMC(dataset, output_file_name="output", beta=0.5, 
    min_region_bp_size=1000, correction=FALSE, 
    loss_threshold=-0.2, gain_threshold=0.2, 
    baf=TRUE, loh_threshold=0.75, loh_frequency=0.8, bs=1000, 
    pval_threshold=0.05, html=TRUE, getGenes=TRUE, 
    mart_database="ensembl", ensembl_dataset="hsapiens_gene_ensembl")

Arguments

dataset

Dataset file following the PennCNV format: The first three columns describe the name, the chromosome and the position respectively. The other columns of the matrix report the LRR and the BAF (if available) of each sample. Note that observations must be ordered by the respective genomic position.

output_file_name

(Default code{output}) File name used to save the results.

beta

(Default 0.5) This parameter is used to compute the stop condition. It is used to calculate the maximum jump allowed in scale parameter updating. If beta=0 then the resulting segmentation will be composed of a region for each probe (all regions will contain just a probe). In contrast, if beta is very large, then the segmentation will contain just a region for each chromosome.

min_region_bp_size

(Default 1000) VegaMC deletes from the list the regions shorter then this size (in bp).

correction

(Default FALSE) If this parameter is TRUE multiple testing corrections is performed.

loss_threshold

(Default -0.2) Values used to mark a region as a deletion (loss). If the wighted mean of a region is lower than this threshold, then the region is marked as a deletion (loss).

gain_threshold

(Default 0.2) Values used to mark a region as an amplification (gain). If the wighted mean of a region is greater than this threshold, then the region is marked as an amplification (gain).

baf

(Default TRUE) This parameter specifies if the dataset contains BAF measurements (default TRUE. If BAF is available, then VegaMC is able to compute LOH imbalances.

loh_threshold

(Default 0.75) Threshold used to distinguish between homozygous and heterozygous genotypes. If the BAF is greater than loh_threshold or lower then (1-loh_threshold) then the respective probe is considered to be homozygous.

loh_frequency

(Default 0.8) Minimum fraction of homozygous probes needed for marking a region as LOHs. Regions with a fraction of homozygous probes greater than this threshold are marked as LOH.

bs

(Default 1000) Number of permutation bootstraps performed to compute the null distribution.

pval_threshold

(Default 0.05) Significance level used to reject the null hypothesis. If the p-value of an aberration (loss, gain, LOH) is not greater than this threshold, then the region is considered to be significant and, consequently, it is considered a driver mutation.

html

(Default TRUE) If this value is TRUE, then in output will be produced an html file called output_file_name.html in which a summary of all detected regions is reported.

getGenes

(Default TRUE) If this value is TRUE, then in output will be produced an html file called output_file_nameGenes.html in which the list of all genes overlapping the significant regions is reported.

mart_database

(Default ensembl) BioMart database name you want to connect to. Possible database names can be retrieved with the function listMarts of biomaRt package.

ensembl_dataset

(Default hsapiens_gene_ensembl) BioMart dataset used to get information from Ensembl BioMart database. In order to obtain the list of all available dataset use the command listDatasets of biomaRt package.

Value

• After the execution of this function, a matrix containing all information on the detected regions is returned. This object is a matrix having a row for each detected regions described by the following columns:
• Chromosome
  The chromosome in which the region is located.
• bp Start
  The position in which the region starts (in bp).
• bp End
  The position in which the region ends (in bp).
• Region Size
  The size of the regions (in bp).
• Mean
  The weighted mean of the region computed on all samples.
• Loss p-value
  The p-value associated to the probability to have a driver deletion.
• Gain p-value
  The p-value associated to the probability to have a driver amplification.
• LOH p-value
  The p-value associated to the probability to have a driver LOH.
• % Loss
  The percentage of samples showing a deletion for this region.
• % Gain
  The percentage of samples showing an amplification for this region.
• % LOH
  The percentage of samples showing a LOH for this region.
• Probe Size
  The number of probes composing the region.
• Loss Mean
  Mean of LRR computed only on the samples that show a loss.
• Gain Mean
  Mean of LRR computed only on the samples that show a gain.
• LOH Mean
  Mean of LRR computed only on the samples that show a LOH.
• Focal-score Loss
  Focal Score associated to deletion.
• Focal-score Gain
  Focal Score associated to amplification.
• Focal-score LOH
  Focal Score associated to LOH.
• This matrix is automatically saved in the current work directory as a tab delimited file. For default the name used to asave the file is 'output'.

References

Morganella S., and Ceccarelli M. VegaMC: a R/bioconductor package for fast downstream analysis of large array comparative genomic hybridization datasets. Bioinformatics, 28(19):2512-4 (2012).

Examples

## Run VegaMC 
    file.copy(system.file("example/breast_Affy500K.txt", package="VegaMC"), ".")
    results <- vegaMC("breast_Affy500K.txt", "results", html=FALSE, 
                        getGenes=FALSE)