KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data

Abstract

Abstract Background Most approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across multiple samples. Results Here we extend the approach to allow comparative analyses of two groups of samples and introduce the R implementation of these two approaches. The comparative module allows for a supervised analysis to be performed, to enable the identification of regions that are differentially aberrated between two user-defined classes. We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions (VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented, was also able to locate all the positive control regions and a number of new regions but these regions were highly fragmented. Conclusions KC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a single package without the need for additional segmentation. It is memory efficient and runs on a wide range of machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological information in the aCGH data. The program is freely available from the Bioconductor website http://www.bioconductor.org/ under the terms of the GNU General Public License.

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RESEARCH ARTICLEOpen Access
KC-SMARTR: An R package for detection
of statistically significant aberrations in
multi-experiment aCGH data
Jorma J de Ronde1*, Christiaan Klijn1, Arno Velds3, Henne Holstege2, Marcel JT Reinders1,4, Jos Jonkers2,
Lodewyk FA Wessels1,4
Abstract
Background: Most approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array
Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of
the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour
samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases
its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008).
This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across
multiple samples.
Results: Here we extend the approach to allow comparative analyses of two groups of samples and introduce the
R implementation of these two approaches. The comparative module allows for a supervised analysis to be
performed, to enable the identification of regions that are differentially aberrated between two user-defined
classes.
We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions
(VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented,
was also able to locate all the positive control regions and a number of new regions but these regions were
highly fragmented.
Conclusions: KC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a
single package without the need for additional segmentation. It is memory efficient and runs on a wide range of
machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological
information in the aCGH data.
The program is freely available from the Bioconductor website http://www.bioconductor.org/ under the terms of
the GNU General Public License.
Background
Background and motivation
DNA copy number alterations (CNAs) in tumours are an
important mechanism of deregulation of cancer genes.
CNAs are a consequence of genomic instability, which is
common in human cancers [1]. Various microarray plat-
forms have enabled the genome-wide analysis of CNAs
by array based Comparative Genomic Hybridization
(aCGH) and many different microarray platforms are
currently available for aCGH analysis, including plat-
forms based on bacterial artificial chromosome (BAC)
clones, cDNA clones, SNPs and long oligonucleotides.
Most of these platforms feature measurement points
(probes) at specific positions on the genome with a cer-
tain distance between the consecutive probes.
Array CGH data generally consist of the ratios of (log-
transformed) intensities of fluorescently labeled DNA
from case (disease) versus normal diploid (2 n) control
samples that are measured by the probes on the array.
Although single cell aCGH analysis is possible [2] most
* Correspondence: j.d.ronde@nki.nl
1Department of Bioinformatics and Statistics, The Netherlands Cancer
Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
Full list of author information is available at the end of the article
de Ronde et al. BMC Research Notes 2010, 3:298
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© 2009 de Ronde et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

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aCGH analyses are performed on samples derived from
tissue which contains sub-populations of different cells.
This implies that an aCGH measurement will measure
the average of CNAs of different sub-populations within
the sample. Therefore, discretization of the data may
lead to the loss of valuable biological information. KC-
SMARTR does not discretize the data and makes use of
the continuous signal to preserve all the information
contained in the data. The software package allows
unsupervised analysis to identify recurrent aberrations
across samples as well as supervised analysis to identify
regions that are differentially aberrated between user
defined classes of samples. These analyses are two of the
most commonly performed on aCGH data and KC-
SMARTR combines them in one, easy to use and flex-
ible program.
Implementation
Unsupervised KC-SMART
To identify regions which are significantly aberrated the
KC-SMART method [3] takes into account 1) the non-
discretized signal intensity of a probe; 2) the strength of
neighboring probes and 3) the strength of the probe
across multiple samples. These steps are performed
separately for the gains and losses. First, the probe
intensities are summed across all samples. Next, kernel
convolution is performed across the genome, along with
locally weighted regression to account for unequally dis-
tributed probes. This results in a kernel smoothed esti-
mate of probe intensities, the ‘KC score’. The size of the
kernel has consequences for the type of aberration that
will be detected by the algorithm (see next section).
Finally, the significance threshold is determined using a
permutation based approach and significant aberrations
are defined as the set of probes for which the KC score
exceeds this threshold. The set of genomic scales ran-
ging from the smallest to the largest kernel width is
defined as the ‘scale space’. The KC-SMART analysis is
repeated for a selection of kernel widths from the scale
space to reveal the aberrations that are significant at dif-
ferent genomic scales.
The R implementation that we introduce here permits
calculation of significantly recurrent gains and losses
from aCGH data and features a graphical overview of
these gains and losses (Figure 1a). In addition, the
probes residing in these regions can be retrieved in a
tabular format. Significantly recurrent aberrations are
identified across the scale space, and the results of this
analysis are combined in one graphical overview (Figure
1b). Varying the kernel width allows analysis on differ-
ent biologically relevant scales: a large kernel width will
show gains and losses over large (sub-chromosomal)
regions while a small kernel width will allow the detec-
tion of smaller gains and losses (kilobase or megabase
regions). Obviously, the minimal size of gains and losses
that can be detected also depends on the resolution of
the (aCGH) platform used to measure the signal. The
kernel width, the resolution (i.e. the number of points
sampled from the convoluted kernels) and the signifi-
cance threshold level are all user selectable.
Supervised KC-SMART
In addition to the single class analysis aimed at finding
recurrent CNAs, KC-SMARTR also features a new,
supervised approach to perform a comparative analysis,
i.e. it allows the direct comparison of two groups of
samples. This allows the detection of regions represent-
ing significant, differential copy number changes
between groups, i.e. class-specific CNAs. In contrast to
the unsupervised KC-SMART approach which performs
a kernel convolution on the summed ratios of the
tumor set, the comparative approach performs a kernel
convolution on each individual tumor profile, resulting
in a KC score for each sampling point for each sample.
Then two alternative analysis routes can be followed. In
the first approach, we compute, for each genomic posi-
tion (sampling point), i, the signal-to-noise ratio:
SNR i
ii
if
KC

KC
+
KC
( )
( ) ( )
( )
,
=
−

12
1 2
(1)
where μKC1(i) and μKC2(i) are the averages of the KC
scores at position i over all samples in Groups 1 and 2,
respectively; sKC1,2(i) is the pooled variance over all
samples of the KC scores at position i, and f is a regu-
larization factor equal to the 95thpercentile of the
pooled class standard deviation across all genomic posi-
tions. This factor prevents small variances from domi-
nating the SNR statistic. To identify significantly
differential CNAs, a class label based permutation
scheme is employed to determine the SNR threshold
that satisfies the user-specified false discovery rate. In
the second approach, the smoothed tumor profiles are
employed as input to the SAM package [4], to identify
differentially aberrated loci at a given FDR.
Results
Figure 2 shows an example of the visual output from the
comparative KC-SMARTR analysis of a publicly avail-
able breast cancer aCGH dataset [5] in which the 17q
amplicon (containing the HER2 gene) is clearly identi-
fied as a significant differential CNA in the HER2-posi-
tive breast cancer group. In a recent cross species
comparison study [6] our algorithm was used success-
fully to compare mouse to human aCGH data, showing
the wide range of datasets our method can be applied
to. For a more in depth analysis and comparison of KC-
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SMARTR to other methods we made use of a publicly
available aCGH dataset [7] consisting of copy number
profiles of cell lines derived from B- and T-cell lympho-
ma’s. B- and T-cells are subject to somatic VDJ recom-
bination at the immunoglobulin and T-cell receptor
(TCR) loci, respectively. B- and T-cell lymphomas will
therefore have clonal VDJ recombinations characterized
by regional copy number losses that are specific to the
cell type and provide a positive control in our analysis.
In order to evaluate the KC-SMARTR method and to
exploit these intrinsic positive controls, we divided the
data into two groups: a group consisting of B-cell lym-
phomas and a group of T-cell lymphomas. Given the
fact that VDJ recombination takes place we would
expect the B-cell lymphomas to have lost these variable
regions on chromosomes 2, 14 and 22, compared to the
T-cell lymphomas. Conversely, the T-cell lymphomas
would be expected to show lost regions on chromo-
somes 7 and 14. We expect the rearrangements at the
T and B-cell loci to be small, so we chose to perform
the analysis using a small (200 kb) kernel width. We
were able to recover exactly those regions that are sub-
ject to VDJ recombination as significantly aberrated
regions (See figure 3). In addition to these regions we
also found significantly aberrated regions on chromo-
somes 1 and 6 (See Table 1).
To the best of our knowledge, there is no other pub-
licly available method capable of performing a compara-
tive aCGH analysis. We therefore decided to compare
our method against a t-test on segmented data. In this
approach we segment our data using the DNAcopy
package [8] and perform a t-test between the two
defined groups (i.e. B-cell lymphomas versus T-cell lym-
phomas) using the segment values at each probe loca-
tion, which returns a t-statistic for each probe. To
control the false discovery rate (FDR) we employ the
SAM package to identify significant probes. The signifi-
cant probes are then combined into significant regions
which can be compared to the regions as identified by
KC-SMARTR. Using an FDR setting of 5% the resulting
regions contained all the VDJ control regions and sev-
eral other regions, both overlapping and non-
Figure 1 a) Genome-wide plot of the KC scores of a Nimblegen mouse data set (Klijn C, et al. Unpublished data 2008) using a kernel
width of 1 Mb, the red dotted line indicates the significance threshold determined using an alpha cut-off of 0.05. A large gain on
chromosome 9 and the loss of chromosome 12 clearly stand out, reflecting both the strength and the frequency of the respective gain and loss.
b) Scale space plot, showing the significant regions on chromosomes 7, 10 and 17 for four different kernel widths where the color indicates the
level of significance (ranging from red to yellow where red indicates highly significant aberrations and yellow less significant aberrations).
Figure 2 shows the visual output of the comparative analysis of KC-SMARTR, run on the breast cancer aCGH dataset from Chin [5]
comparing the HER2-positive group (red) to the HER2-negative group (black). A kernel width of 1 Mb and an FDR cut-off of 0.01 were
used. The chromosome 17q amplicon, characteristic for HER2-positive tumors, stands out clearly (the significant region as determined by the
SNR algorithm is indicated as a grey shaded area).
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overlapping with the regions identified by KC-SMARTR
[Additional file 1: Supplemental Table S1]. The amount
of scattering (i.e. many small regions within a larger
region are reported) may depend on the settings of the
segmentation algorithm and the false discovery rate
employed for the t-test. To avoid having to optimize
these settings for every approach, and in the process
most likely overfitting the data and thus biasing the
approaches towards a desired result, we employed the
DNAcopy default settings and used an FDR setting of
5% for the t-test. At these default settings many of the
reported regions are highly scattered. This is in contrast
to the results from the KC-SMARTR analysis which fea-
tures smoothing of the data and incorporates data from
neighboring probes (see Figure 4), a difference that is
also reflected in a higher median sensitivity (91% versus
69%, specificity 15% vs 31% [Additional file 1: Supple-
mental Table S2). Herein lies the strength of the
KC-SMARTR approach, that the user can select the
appropriate kernel width to identify aberrated regions of
relevant size. The kernel width can be chosen such that
noisy data will be smoothed but small aberrations are
reliably detected. Conversely, larger kernel widths can
enable the detection of broader, lower amplitude gains
and losses. This is an important advantage over the t-
test on segmented data that in our example returns a
very fragmented aberrated region that may not corre-
spond to the actual copy number within those regions.
To assess whether the regions identified by KC-
SMARTR that are located outside of known VDJ-regions
are indeed important in tumorigenesis, further func-
tional experiments would be needed.
Discussion
To the best of our knowledge no other software package
exists that allows for a supervised aCGH analysis and as
such we believe our method delivers an important con-
tribution to this field. Also, given the fact that the
method does not make use of discretized data, for
recurrent gain and loss analysis the software gives the
user the flexibility to look for aberrations across differ-
ent genomic scales. Given the ever increasing data set
sizes it is also important to note that our algorithm
scales linearly with the number of probes and number
Figure 3 shows the comparative KC-SMARTR graphical output, run on the B- and T-cell lymphoma dataset. The black line represents the
B-cell lymphomas and the red line the T-cell lymphomas. Using a kernel width of 200 kb and an FDR of 5%, the VDJ regions can clearly be
distinguished as significantly lost in the B-cells on chromosome 2, 14 and 22 and as significantly lost in the T-cells on chromosomes 7 and 14.
The green bars indicate the approximate positions of the immunoglobulin variable regions, the purple bars indicate T-cell receptor variable
regions. Also see Table 1 for a list of identified regions over the entire genome.
Table 1 This table shows the regions that were identified
by KC-SMARTR as being significantly aberrated in the B-
and T-cell lymphoma dataset
Chromosome Region (in kb)Known VDJ loci in region
1
1
1
1
1
2
6
6
6
6
6
6
7
7
14
14
22
51300 - 51300
168900 - 170100
171600 - 171900
172800 - 176700
187500 - 188100
88800 - 89400
1800 - 4200
5400 - 6300
11100 - 11100
13200 - 17100
19800 - 23100
24300 - 24300
38100 - 38700
141900 - 142200
21300 - 22200
105300 - 105900
21300 - 21600
-
-
-
-
-
Ig* Kappa light chain
-
-
-
-
-
-
T-cell receptor Gamma
T-cell receptor Beta
T-cell receptor Alpha
Ig heavy chain
Ig Lambda light chain
*Ig - Immunoglobulin.
All positive control regions (i.e. the regions that are known to be involved in
VDJ recombination) were identified. Additionally, five regions on chromosome
1 and six regions on chromosome 6 were found.
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of samples. To give an indication, on our Opteron 2.7
GHz the analysis of a fairly large Affymetrix SNP 6
(1.78 Million probes) dataset consisting of 61 samples a
comparative analysis took about five and a half hours.
In the future we would like to implement a paralle-
lized algorithm to make use of additional cpu cores that
are frequently available in current machines. This would
speed up the process a lot since most calculations can
be performed in parallel.
Conclusions
KC-SMARTR is a flexible, fast and user-friendly aCGH
tool to determine significantly recurrent CNAs as well
as regions showing significantly differential aberrations
between two groups of samples. On a set of B- and T-
cell lymphomas we were able to locate all positive con-
trol regions (VDJ recombination sites) and a number of
new regions as significantly aberrated. A t-test run on
segmented data was also able to find the positive control
regions but resulted in highly fragmented regions. In
contrast, KC-SMARTR allows the user to set the kernel
width and thereby control the size of the aberrations
that are detected. It features output in both visual and
tabular format, including a scale space analysis, which
allows a visual overview of the aberrations at different
scales. KC-SMARTR offers recurrent CNA and class
specific CNA detection, at different genomic scales, in a
single package without the need for additional segmen-
tation. It is memory efficient and runs on a wide range
of machines. Most importantly, it does not rely on data
discretization and therefore maximally exploits the bio-
logical information in the aCGH data.
Availability and requirements
Project name: KC-SMART
Project home page: http://bioconductor.org/packages/
2.5/bioc/html/KCsmart.html
Operating system(s): Platform independent
Programming language: R
License: GNU General Public License
Installation note: To always get the most up-to-date
version of KC-SMARTR, follow the procedure below.
Update to the latest R and Bioconductor version and
type the following at the R prompt: source (”http://bio-
conductor.org/biocLite.R“) biocLite("KCsmart”)
Additional material
Additional file 1: Supplemental Data. Contains Supplemental Table S1
and Supplemental Table S2.
Acknowledgements
JdR was supported by the Netherlands Genomics Initiative (NGI) through the
Cancer Genomics Centre (CGC).
CK was supported by grants from the Netherlands Organization for Scientific
Research (ZonMw Vidi 917.036.347) and the Dutch Cancer Society (NKI 2006-
3486).
Author details
1Department of Bioinformatics and Statistics, The Netherlands Cancer
Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands.
2Department of Molecular Biology, The Netherlands Cancer Institute,
Plesmanlaan 121, 1066CX Amsterdam, The Netherlands.3Central Microarray
Facility, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX
Amsterdam, The Netherlands.4Faculty of EEMCS, Delft University Of
Technology, 2628 CD Delft, The Netherlands.
Authors’ contributions
JdR participated in the design of the study, wrote code for the software
package, was involved in the analyses and wrote the manuscript, CK and AV
Figure 4 shows the probe ratios for the T- and B-cell lymphoma data (in red and black respectively). The KC-SMARTR profiles (lines) and
significant regions (blocks) using different kernel widths are shown in different colors (see legend for details). In green the regions that are
reported as significant by the t-test on segmented data are shown. The larger kernel widths (100 kb and 200 kb) allow the detection of larger
regions whereas the smaller kernel width (20 kb) allows the detection of smaller regions. In this way the data can be analyzed on different
scales. In contrast, the t-test only reports regions on a single scale.
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participated in the design of the study, wrote code for the software package
and was involved in the analyses, HH participated in the design of the
study, MR, JJ and LW participated in the design of the study and conceived
of the study. All authors have read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 20 August 2010 Accepted: 11 November 2010
Published: 11 November 2010
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doi:10.1186/1756-0500-3-298
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detection of statistically significant aberrations in multi-experiment
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