Stefano Maria Pagnotta
Vega Data Description
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A current challenge in biology is the characterization of genetic mutations that
occur as response to a particular disease. Development of array comparative
genomic hybridization (aCGH) technology has been a very important step in
genomic mutation analysis, indeed, it enables copy number measurement in hun-
dreds of thousands of genomic points (called markers or probes). Despite the
high resolution of aCGH, accurate analysis of these data is yet a challenge. In
particular a major diﬃculty in mutation identiﬁcation is the distinction between
driver mutations (that play a fundamental role in cancer progression) and pas-
senger mutations (which are random alterations with no selective advantages).
This document describes classes and functions of GAIA (Genomic Analysis
of Important Aberrations) package. GAIA uses a statistical framework based
distribution of the contemporary mutations expected for non-driver markers.
Afterwards, the computed probability distribution and the observed data are
used to assess the statistical signiﬁcance (in terms of q-value) of each marker.
Finally an iterative “peel-oﬀ” procedure is used to identify the most signiﬁcant
independent regions which have a high probability to correspond to driver mu-
GAIA algorithm is carefully described in .
GAIA package can be loaded in R by using the following command:
Note that by using the installation command biocLite the qvalue dependency
is automatically fulﬁlled. In contrast if GAIA has been manually installed (e.g.
using R CMD INSTALL command) you can install qvalue package by the follow-
will be supported by the data provided in GAIA package.
Marker Descriptor Matrix
called probes). This matrix has a row for each marker and each marker is
described by a set of column reporting the name of the probe and its genomic
position. In particular the matrix has the following columns:
Probe Name : The name of the observed probe;
Chromosome : The chromosome where the probe is located;
Start : The genomic position (in bp) where the probe starts;
The column specifying the End position is optional and when it is missed,
start and end positions are considered to be coincident. This is the case of the
data provided within the package.
In order to load the marker descriptor matrix provided in GAIA use the
This marker descriptor matrix simulates the genomic position for the probes
of 24 chromosomes (each chromosome has 1000 probes). Chromosomes 23 and
24 represents sex chromosomes X and Y respectively.
Aberrant Region Descriptor Matrix
gions.This matrix has a row for each aberrant region and each of them is de-
scribed by the following columns:
Sample Name : The name of the sample in which the aberrant region is
Chromosome : The chromosome where the aberrant region is located;
Start : The genomic position (in bp) where the aberrant region starts;
End : The genomic position (in bp) where the aberrant region ends;
Num of Markers : The number of markers contained in the aberrant region;
Aberration : An integer indicating the kind of the mutation.
The column Aberration can assume only integer values in the range 0, · · · , K −
1 where K is the number of considered aberrations. For example if we are
considering loss, LOH and gain mutations than the only valid values for the
column Aberration are 0, 1 and 2.
In order to load the aberrant region descriptor matrix use the following
This aberrant region descriptor matrix simulates 10 samples for the chromo-
somes described by synthMarkers_Matrix. A summary of the aberrant regions
contained in this matrix follows:
Real aCGH Datset
description of this dataset. Raw data were preprocessed by PennCNV tool 
and segmented by using Vega R/Bioconductor package .
Table 1: synthMarkers_Matrix: Aberrant Region Detail. The column Fre-
quency Percentage reports the percentage of samples containing the indicated
Colorectal Cancer (CRC) Dataset
GeneChip array . Raw data are available in GEO with identiﬁer GSE13429.
Patients of this dataset were diagnosed with microsatellite-stable CRC without
In order to run GAIA on this dataset both aberrant region descriptor matrix
(crc) and marker descriptor matrix (crc_markers) are provided.
works on aCGH data in which hundreds of thousands probes are contemporary
observed, so the data loading phase can be very time consuming. For this reason
in GAIA two data loading functions are provided (see Sections 5.1 and 5.2). By
using this functions the users can load just one time the data and save they into
data objects that can be used for several GAIA executions.
matrix as described in Section 4.1:
> markers_obj <- load_markers(synthMarkers_Matrix)
The marker_obj provides an easy access to the data informations contained
in the marker matrix and and it is organized as a list:
the element marker_obj[[i]] contains the descriptions of all observed mark-
ers for the i-th chromosome and it is organized as a matrix of dimension 2 × M
row of this matrix contains start and end position respectively.
using both the region matrix described in Section 4.2 and the marker descriptor
object created by the function load_markers, in addition the number of the
analyzed samples must be passed as argument:
> cnv_obj <- load_cnv(synthCNV_Matrix, markers_obj, 10)
The cnv_obj is organized as a double list:
the element cnv_obj[[j]][[i]] contains the informations about the j-th
aberration of the i-th chromosome. In particular cnv_obj[[j]][[i]] is a ma-
trix of dimension N × M where N is the number of samples (in the example 10)
and M the the number of observed probes for the i-th chromosome. The element
cnv_obj[[j]][[i]][n,m] is equal to 1 if in the marker m of the chromosome
i a mutation of kind j is observed, it is equal to 0 otherwise.
rithm. In particular runGAIA has the following header:
runGAIA(cnv_obj, markers_obj, output_file_name="", aberrations = -1,
chromosomes = -1, num_iterations = 10, threshold = 0.25)
cnv_obj : The aberrant region descriptor object created by using the function
load_cnv (see Section 5.2);
markers_obj : The marker descriptor object created by the function
load_markers (see Section 5.1);
output_file_name : (default “”) The ﬁle name used to save the signiﬁcant
aberrant regions. If this argument is not speciﬁed the results will be not
saved into a ﬁle;
aberrations : (default −1) The aberrations that will be analyzed. The default
value −1 indicates that all aberration will be analyzed;
chromosomes : (default −1) The chromosomes that will be to analyzed. The
default value −1 indicates that all chromosomes will be analyzed;
num_iterations : (default 10) if the number of permutation steps (if approxi-
mation is equal to -1) - the number of column of the approximation matrix
(if approximation is diﬀerent to -1);
threshold : (default 0.25) Markers having q-values lower than this threshold
are labeled as signiﬁcantly aberrant. This parameter must be a real num-
ber in the range 0 − 1;
more detail see Section 6). This parameter must be a real number in the
range 0 − 1 and by using values lower than −1 homogeneous peel-oﬀ is
disabled and standard peel-oﬀ procedure is used;
approximation : (default=FALSE) if approximation is FALSE then GAIA ex-
plicitly performs the permutations, if it is TRUE then GAIA uses an
approximated approach to compute the null distribution.
Suppose that we want to analyze all aberrations and all chromosomes and
that we want to save the results within the ﬁle CompleteResults.txt, than we
use the following command:
> results <- runGAIA(cnv_obj, markers_obj, "CompleteResults.txt")
Both in the ﬁle CompleteResults.txt and in the matrix variable results we
can found all signiﬁcant aberrant regions. In particular the following column
are used to describe each signiﬁcant aberrant region:
Chromosome : The chromosome where the aberrant region is located;
Aberration Kind : An integer indicating the kind of the mutation;
Region Start [bp ]: The genomic position (in bp) where the aberrant region
q-value : The estimated q-value for the aberrant region.
So with the following command we print the signiﬁcant aberrant regions
having the minimum q-values:
Suppose now that we want to analyze only the aberration 1 on the chro-
mosomes 10, 11 and 14 and that we want to save the results within the ﬁle
Results.txt. In addition we increase the signiﬁcance threshold value from 0.25
to 0.5, than we use the following command:
> results <- runGAIA(cnv_obj, markers_obj, "Results.txt", aberrations=1, chromosomes=c(10, 11, 14), threshold=0.5)
homogeneous peel-oﬀ signiﬁcant regions are detected by using both statistical
signiﬁcance and within-sample homogeneity, so that more accurate results can
be obtained. Homogeneous peel-oﬀ is disabled for values lower than 0.
Homogeneous peel-oﬀ can be used only if two kinds of aberrations are ob-
served. In the next we show as homogeneous peel-oﬀ can be used on CRC
dataset. The ﬁrst step is composed by the loading of the data and the creation
of the respective markers and aberrant regions descriptor objects:
> crc_markers_obj <- load_markers(crc_markers)
> crc_cnv_obj <- load_cnv(crc, crc_markers_obj, 30)
Now we can run GAIA with homogeneous pee-oﬀ on the chromosome 14 by
using the suggested value for hom_threshold of 0.12:
> res <- runGAIA(crc_cnv_obj, crc_markers_obj, "crcResults.txt",
Results of the computation are saved in the ﬁle crcResults.txt
Run GAIA with Approximation
approach performs an approximation of the permutations. In particular, after
computed the frequency of alteration for each sample θ
, GAIA simulates a
of performed approximation) where each column is a vector in which the element
j has a probability for drawing 1 equal to θ
. This asymptotically approximates
approximated in practice. So we suggest to use this approach in high resolution
With the following command we run GAIA in its approximated version on
all chromosomes by performing K = 5000 approximations.
> res <- runGAIA(crc_cnv_obj, crc_markers_obj, "crc_approx_Results.txt", hom_threshold=0.12, num_iterations=5000, approximation=TRUE)
Integration of GAIA and Integrative Genomics
The Integrative Genomics Viewer (IGV)  is a high-performance visualization
tool for interactive exploration of large, integrated datasets. It supports a wide
variety of data types including sequence alignments, microarrays, and genomic
From version 1.5 GAIA produces in output a “.gistic”ﬁle that can be loaded
in IGV so that computed q-values for all analyzed chromosomes can be plotted.
 Morganella,S. et al. (2010) Finding recurrent copy number alterations pre-
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 Venkatachalam,R. et al. (2010) Identiﬁcation of candidate predisposing copy
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 Morganella,S. et al. (2010) VEGA: variational segmentation for copy number
detection. Bioinformatics 26(25), 3020-3027.
 Wang,K. et al. (2007) PennCNV: an integrated hidden Markov model de-
signed for high-resolution copy number variation detection in whole-genome
SNP genotyping data. Genome Research 17, 1665-1674.
 Integrative Genomics Viewer (IGV): http://www.broadinstitute.org/