BACKGROUND
the study of the genomic imbalances in a variety of different diseases, including cancer, is a major step towards the understanding of disease development.
in cancer cells, for example, dna copy number increases have been shown to be one of the mechanisms by which oncogenes and drug resistance genes can be activated, whereas loss of dna material may cause inactivation of tumor suppressor genes.
knowledge of copy-number aberrations can have also immediate clinical use in diagnosis and in some cases can provide useful prognostic information.
association of dna copy-number aberrations with prognosis has been found for a variety of tumor types, including prostate cancer  <cit> , breast cancer  <cit> , gastric cancer  <cit>  and lymphoma  <cit> .
chromosomal comparative genomic hybridization  is a molecular cytogenetic method for the detection of chromosomal imbalances and it has been extensively used for studying copy number alterations in various cancer types since it was first described in  <dig>  <cit> .
as classical cgh has an average resolution of 10– <dig> megabases, it is able to detect changes affecting relatively large chromosomal regions.
the introduction of array-based platforms has therefore greatly improved genomic profiling and currently, two technologies are mainly used for screening of dna copy number; the bac  and the oligonucleotide-based cgh arrays.
bac-based cgh arrays were amongst the first genomic arrays to be introduced  <cit>  and are routinely used to detect single copy changes in the genome, owing to their high sensitivity.
however, producing bac clones for array cgh  is expensive and time-consuming and, due to the large size of bacs, the limits of bac acgh resolution have been reached.
oligonucleotide acgh  <cit>  allow flexibility in probe design, greater coverage, and much higher resolution.
the latter depends on array design and the cell type homogeneity.
moreover, oligonucleotides can more easily be produced for any organism for which the genome has been sequenced.
today, as noted in  <cit> , the acgh field is evolving towards oligonucleotide acgh.
ijssel  <cit>  has published a genome-wide validation of their in-house spotted oligo acgh using bac arrays, for human and mouse samples.
these samples consist of one human gastric tumour and two different mouse tumours, and the comparison between bac and oligo acgh was performed on the genomic profiles.
in this study, ijssel distinguished two kinds of noise, the technical noise and the true genomic copy number polymorphisms  <cit> .
various algorithms for data denoising exist such as the non-parametric method called circular binary segmentation   <cit>  which splits the chromosomes into regions of equal copy number.
cbs is identified as one of the best segmentation methods  <cit> .
here, we present a comprehensive comparison between the two acgh platforms using available data from  <dig> human advanced prostate cancer  obtained at two independent sites with the same batch of dna.
this comprehensive comparison has been performed on raw data sets and data analysed using cbs  <cit> .
this represents the first genome-wide statistical validation of the oligo array platform.
in addition we have developed a statistical method to identify bac outliers that could represent microevents.
RESULTS
probe distribution and noise levels for the bac and oligo platforms
the repartitions of bacs and oligos on the human genome were first compared.
the uniformity of the  <dig> bacs and the  <dig> oligos repartition along the human genome was tested using the kolmogorov-smirnov test , considering each chromosome arm as an interval and each bac and oligo as a point.
bacs are uniformly distributed along all the chromosomes whereas oligos are not, except on chromosome  <dig> and arms 7p, 10p and 19p.
a log-ratio comparison of the oligo and bac cgh platforms
the comparison was restricted to the  <dig> autosomes as the available data were produced with the sex mismatch for bac acgh and not for oligo acgh.
each bac value was compared with the log-ratios of the corresponding oligos, i.e oligos positioned between the start and the end of the bac.
this comparison is not straight-forward as the mean number of oligos per bac across the  <dig> patients is only  <dig> .
the details of the distribution of the number of oligos per bac are shown in figure  <dig>  a one-sample student test was performed for the  <dig> bacs that overlap with at least  <dig> oligos, so that the test had enough power.
there were  <dig> bac log-ratios significantly different from their corresponding oligo log-ratios .
to provide a more global comparison, a complementary analysis was carried out on the  <dig> bac values corresponding to at least one oligo in the  <dig> patients.
the comparison was done by computing the kendall correlation for all pairs of bac and oligo log-ratios, the oligo value being a mean oligo log-ratio.
the pair values are plotted on figure  <dig>  the correlation gives a significant p-value inferior to 1e -  <dig> with τ =  <dig> .
a τ value of  <dig>  indicates that both values of a pair increase or decrease with a probability of  <dig> %.
when the kendall's correlation test is performed excluding bacs containing fewer than  <dig> oligos, τ reaches  <dig> .
this comparison shows that there is a significant correlation between the bac and the oligo platform results.
comparison between oligo and bac cgh using segmentation
oligo and bac cgh platforms were compared after segmentation into regions of equal copy number as the individual number of copies can be corrupted with noise.
the circular binary segmentation method   <cit>  was used both for bac and oligo data.
this is illustrated in figure  <dig> for chromosome  <dig> from patient  <dig>
a statistical comparison was performed to assess the divergence of the bac and oligo data in the regions delimited by the segmentation.
each segment is considered as a log-ratios sample and two comparisons were made.
firstly, we compared globally all the bac and oligo segments.
each bac segment was compared with the corresponding oligos, located between the start and the end of the bac segment.
this was also done reciprocally with the oligo segments as the starting point, as the comparison is asymmetrical.
the pair segment mean values of all patient samples are shown in figure  <dig>  the kendall correlation gives a p-value inferior to 1e -  <dig>  with τ significantly greater  than the value of  <dig>  achieved without segmentation.
secondly, all the segments were compared individually.
before comparing the segment samples, a shapiro test was performed to determine whether their distributions are gaussian.
a student test was used to compare the gaussian distributions, otherwise a wilcoxon-mann-whitney test was applied.
a 1% false discovery rate  <cit>  was then used to avoid problems related to multiple testing.
using these approaches we have identified  <dig> of  <dig>  bac segments with values divergent from oligo values  and  <dig> of  <dig>  oligo divergent segments .
to complete this result the same comparison was done with a false discovery rate of 5% and 10%.
for 5% the number of divergent segments are then  <dig> and  <dig> for bac versus oligo and oligo versus bac respectively and for 10%,  <dig> and  <dig>
finally, the comparison of the two platforms was focused on the detection of aberrant events.
we considered as aberrations all segments with mean number of copies lower than  <dig>  or higher than  <dig> .
the bac platform detected  <dig> deletions and  <dig> amplifications among the  <dig> patient samples whereas the oligo platform detected  <dig> deletions and  <dig> amplifications with median lengths of  <dig> mbp and  <dig>  mbp respectively.
this shows that oligo acgh detect more, smaller chromosomal events than bac acgh.
regarding the genomic location of these segments,  <dig> segments were found by both platforms either deleted or amplified with an overlapping region comprising more than 90% of each segment.
it is important to note that their median length is  <dig> mbp, indicating that large chromosomal aberrations are found by both platforms.
in conclusion, segmentation enhances the correlation between the two platforms.
another result is that the oligo acgh reveal many more, smaller aberrations.
detection of outliers in bac segments
in order to perform a more in-depth analysis of both platforms, that could then be applied to the identification of target genes and regions of genetic alteration, we developed a statistical model to detect bac outliers and validated the results with the oligo cgh platform.
an advantage of the oligo cgh platform is its ability to detect microevents.
in this section we show that, to some extent, the bac acgh platform, and more precisely bac outliers within segments, could indicate microevents.
bac outliers can be detected by modelling their log-ratios with a gaussian distribution n where μs is the theoretical mean of the bac segment s and σ the standard deviation across all segments and all patients.
however to avoid misestimating σ by including too many outliers, this parameter has been estimated on the least varying patient,  <dig>  containing n =  <dig> bacs.
let μ^
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaaiigacuwf8oqbgaqcaaaa@2e79@s and σ^
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaaiigacuwfdpwcgaqcaaaa@2e86@ be the estimators of μs and σ. to detect outliers the following classical calculus was performed considering each bac value x inside its corresponding segment s of size ns.
for each bac, μ was reestimated by computing a new μs on the ns -  <dig> value left after excluding the x value.
x−μ^s=x−μs−⇔ns−1σ=ns−1σ−ns−1σ
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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x7atnaabaaaleaacqwgzbwcaeqaaogaeiykakcabagae83wdmhaaiabgkhitmaalaaabawaaoaaaeaacqwgubgbdawgaawcbagaem4camhabeaakiabgkhitiabigdaxawcbeaakiabcicaoiqb=x7atzaajawaasbaasqaaiabdohazbqabagccqghsislcqwf8oqbdawgaawcbagaem4camhabeaakiabcmcapaqaaiab=n8azbaaaaa@74b2@
as ns−1σ
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaamaakaaabagaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmasqabagccqggoaakcqwg4baecqghsisliigacqwf8oqbdawgaawcbagaem4camhabeaakiabcmcapaqaaiab=n8azbaaaaa@3b00@ and ns−1σ
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaamaakaaabagaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmasqabagccqggoaakiigacuwf8oqbgaqcamaabaaaleaacqwgzbwcaeqaaogaeyoei0iae8hvd02aasbaasqaaiabdohazbqabagccqggpaqkaeaacqwfdpwcaaaaaa@3ced@ obey asymptotically the n and n law respectively, the distribution of ns−1σ
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaamaakaaabagaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmasqabagccqggoaakcqwg4baecqghsisliigacuwf8oqbgaqcamaabaaaleaacqwgzbwcaeqaaogaeiykakcabagae83wdmhaaaaa@3b10@ is the n law.
besides, it is known that the law of nσ^2σ
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaaiabd6gauhgaciqb=n8azzaajawaawbaasqabeaaieaacqgfyagmaaaakeaacqwfdpwcaaaaaa@32e1@ is asymptotically χn−12
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaaiigacqwfhpwydaqhaawcbagaemoba4maeyoei0iaegymaedabagaegomaidaaaaa@32cb@.
from this it is inferred that: t=ns−1nsσnσ^2σ2
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwg0badcqgh9aqpdawcaaqaamaalaaabawaaoaaaeaacqwgubgbdawgaawcbagaem4camhabeaakiabgkhitiabigdaxawcbeaakiabcicaoiabdiha4jabgkhitggaciqb=x7atzaajawaasbaasqaaiabdohazbqabagccqggpaqkaeaadagcaaqaaiabd6gaunaabaaaleaacqwgzbwcaeqaaaqabagccqwfdpwcaaaabawaaoaaaeaadawcaaqaaiabd6gaujqb=n8azzaajawaawbaasqabeaacqaiyagmaaaakeaacqggoaakcqwgubgbcqghsislcqaixaqmcqggpaqkcqwfdpwcdaahaawcbeqaaiabikdayaaaaaaabeaaaaaaaa@4cfe@ obeys asymptotically a student's law tn- <dig>  t is simplified to : t=x−μ^sσ^nsn
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwg0badcqgh9aqpdawcaaqaaiabdiha4jabgkhitggaciqb=x7atzaajawaasbaasqaaiabdohazbqabaaakeaacuwfdpwcgaqcaaaadagcaaqaamaalaaabagaeiikagiaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmcqggpaqkcqggoaakcqwgubgbcqghsislcqaixaqmcqggpaqkaeaacqwgubgbdawgaawcbagaem4camhabeaakiabd6gaubaaasqabaaaaa@4700@.
a p-value is computed for each bac using the following procedure:
for each segment do
for each bac inside current segment do
produce a sample of all the segment log-ratios except the current bac one
compute a p-value for the bac value x according to formula
pnsn)
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwgqbaudaqadaqaaiabdsfaunaabaaaleaacqwgubgbcqghsislcqaixaqmaeqaaogaeyyzim7aasaaaeaacqwg4baecqghsisliigacuwf8oqbgaqcamaabaaaleaacqwgzbwcaeqaaagcbagaf83wdmnbakaaaawaaoaaaeaadawcaaqaaiabcicaoiabd6gaunaabaaaleaacqwgzbwcaeqaaogaeyoei0iaegymaejaeiykakiaeiikagiaemoba4maeyoei0iaegymaejaeiykakcabagaemoba42aasbaasqaaiabdohazbqabagccqwgubgbaaaaleqaaagccagloagaayzkaaaaaa@4db4@
done
done
all p-values are treated according to  <cit>  taking fdr = 1%.
this model was used on the  <dig> patient samples.
<dig> outliers were detected in the genomic profiles of the patients, across all the chromosomes.
figure  <dig> shows  <dig> isolated bac outliers  and two groups of bac outliers  on chromosome  <dig> of patient sample  <dig>  isolated bac outliers point to putative microevents  whereas groups of bac outliers may indicate larger aberred regions.
finally to validate statistically this result, the kendall's correlation with oligos was computed for the  <dig> bac outliers with at least one corresponding oligo giving τ =  <dig>  and a p-value of 1e -  <dig>
discussion
the conceptual advantage of a high density oligo acgh is that it can reveal microdeletions or amplifications at the gene level that may contribute to gene transcript variation and that are not detected on a bac platform.
indeed the oligo platform has an average  <dig> kbp spatial resolution that enables to span all the well characterised genes  providing sufficient coverage for a genome wide survey of dna aberrations.
different sizes of microdeletions, from  <dig> to  <dig> probes have been detected by the cbs method, such as the microdeletion containing the potential tumor suppressor atbf <dig>  <cit> .
in this paper, oligo acgh results are validated using the  <dig> k bac acgh platform on data from  <dig> patients.
the clinical material used in the prima project is extremely unusual, displaying greater than 75% tumour cellularity.
in general, other than in very advanced cancers, the level of tumour cellularity would be significantly lower for prostatic material and this together with lymphocytic infiltration might be expected to add to the noise component of the oligo acgh signal and reduce concordance both with bac array data and gene expression microarrays.
the number of patients involved in this experiment allowed us to perform statistically significant analyses.
indeed there are  <dig> bacs that overlap with at least  <dig> oligos and  <dig> bacs with at least one oligo in the  <dig> patients.
the validation was first carried out directly, by matching each bac to the corresponding oligo raw data, and then indirectly, using a segmentation algorithm called cbs  <cit> , which gave more significant results.
the kendall's correlation on the raw data was significant, with τ =  <dig> , and was improved by segmentation, reaching a value of  <dig> .
correlation was therefore increased more than two fold when compared to the direct comparison, confirming the observation of ijssel  <cit> , that bac and oligo profiles are very similar after smoothing.
this global analysis of all bac and oligo segments was complemented by an individual comparison, where each segment from bac or oligo platforms was tested against its corresponding oligo or bac data set.
14% and 11% of bac and oligo segments respectively were found to be divergent.
these percentages can be lowered  by scaling the oligo values as the log-ratios are higher for oligos than for bacs again in agreement with ijssel  <cit>  .
however these statistical tests only reveal very divergent segments, indeed taking false discovery rates equal to  <dig> or 10% gives higher percentages .
so this number of divergent segments should be regarded as a lower bound.
indeed there were only few similar copy number aberrations between the two platforms as the oligo platform presented the advantage to detect more, smaller chromosomic aberrations.
secondly, we compared the noise level between the two platforms on raw and segmented data.
computing the autocorrelation along each chromosome, we observed that the baseline variation for bacs is lower than for oligos in agreement with previous studies by ijssel  <cit>  and ylstra  <cit>  who both computed the standard deviation on regions without copy number changes.
using the regions of equal copy numbers produced by cbs, the means of the oligo and bac segments standard deviations have been computed giving respectively  <dig>  and  <dig>  confirming the previous result.
bacs with large insert clones display a lower variation compared with the oligo platform.
however, the trade-off is a lower sensibility for bacs compared to oligos.
besides, the standard deviation value of  <dig>  of the oligo platform, is small from a statistical point of view.
indeed, the confidence interval of the true number of copies inside segments is small.
for instance for a segment with  <dig> oligos inside, a standard deviation of  <dig>  means that for a mean value of  <dig> copies, the interval is  <dig>  –  <dig>  .
in spite of the many advantages of the oligo acgh platform, there is still some value in using the bac platform.
bacs are distributed uniformly on the human genome so that regions not previously found to be involved in cancer or non-coding regions are covered.
this terra incognita may be interesting for further investigation, in particular to search for micrornas and repeated sequences.
in addition, a bac outlier detection model that could point to putative microevents has been introduced in this article.
the bac outlier values have been compared with the corresponding oligo values with good correlation.
a large number of these bac outliers  were found in regions without known genes according to ucsc .
however these bac outliers represent large regions of  <dig> kbp and the potential microevents must still be precisely located and biologically validated.
CONCLUSIONS
we have performed a large scale comparison of oligo and bac platforms using a set of  <dig> patient samples.
first, we have established statistically the reliability of the oligo platform for the identification of chromosomic events.
moreover the oligo platform outperforms the bac technology for the detection of more, smaller aberrations.
taking advantage of this large set of data, we have developped a statistical model, that highlights that bacs may detect putative microevents.
hopefully, this result will incite researchers to reconsider the potential use of bac data for more in-depth investigation of new data, as well as for the numerous publically available bac cgh data.
the challenge in future studies will be the routine establishment of banks of well-defined laser captured material, so that the greater sensitivity of these platforms can be successfully exploited.
in order to obtain a more complete picture of cancer, attempts could then be made to combine the acgh approaches with transcriptomic and proteomic technologies.

 the study of the genomic imbalances in a variety of different diseases, including cancer, is a major step towards the understanding of disease development. in cancer cells, for example, dna copy number increases have been shown to be one of the mechanisms by which oncogenes and drug resistance genes can be activated, whereas loss of dna material may cause inactivation of tumor suppressor genes. knowledge of copy-number aberrations can have also immediate clinical use in diagnosis and in some cases can provide useful prognostic information. association of dna copy-number aberrations with prognosis has been found for a variety of tumor types, including prostate cancer, breast cancer, gastric cancer and lymphoma. chromosomal comparative genomic hybridization is a molecular cytogenetic method for the detection of chromosomal imbalances and it has been extensively used for studying copy number alterations in various cancer types since it was first described in. as classical cgh has an average resolution of 10– megabases, it is able to detect changes affecting relatively large chromosomal regions. the introduction of array-based platforms has therefore greatly improved genomic profiling and currently, two technologies are mainly used for screening of dna copy number; the bac and the oligonucleotide-based cgh arrays. bac-based cgh arrays were amongst the first genomic arrays to be introduced and are routinely used to detect single copy changes in the genome, owing to their high sensitivity. however, producing bac clones for array cgh is expensive and time-consuming and, due to the large size of bacs, the limits of bac acgh resolution have been reached. oligonucleotide acgh allow flexibility in probe design, greater coverage, and much higher resolution. the latter depends on array design and the cell type homogeneity. moreover, oligonucleotides can more easily be produced for any organism for which the genome has been sequenced. today, as noted in, the acgh field is evolving towards oligonucleotide acgh. ijssel has published a genome-wide validation of their in-house spotted oligo acgh using bac arrays, for human and mouse samples. these samples consist of one human gastric tumour and two different mouse tumours, and the comparison between bac and oligo acgh was performed on the genomic profiles. in this study, ijssel distinguished two kinds of noise, the technical noise and the true genomic copy number polymorphisms. various algorithms for data denoising exist such as the non-parametric method called circular binary segmentation which splits the chromosomes into regions of equal copy number. cbs is identified as one of the best segmentation methods. here, we present a comprehensive comparison between the two acgh platforms using available data from human advanced prostate cancer obtained at two independent sites with the same batch of dna. this comprehensive comparison has been performed on raw data sets and data analysed using cbs. this represents the first genome-wide statistical validation of the oligo array platform. in addition we have developed a statistical method to identify bac outliers that could represent microevents.  probe distribution and noise levels for the bac and oligo platforms the repartitions of bacs and oligos on the human genome were first compared. the uniformity of the bacs and the oligos repartition along the human genome was tested using the kolmogorov-smirnov test, considering each chromosome arm as an interval and each bac and oligo as a point. bacs are uniformly distributed along all the chromosomes whereas oligos are not, except on chromosome and arms 7p, 10p and 19p. a log-ratio comparison of the oligo and bac cgh platforms the comparison was restricted to the autosomes as the available data were produced with the sex mismatch for bac acgh and not for oligo acgh. each bac value was compared with the log-ratios of the corresponding oligos, i.e oligos positioned between the start and the end of the bac. this comparison is not straight-forward as the mean number of oligos per bac across the patients is only. the details of the distribution of the number of oligos per bac are shown in figure a one-sample student test was performed for the bacs that overlap with at least oligos, so that the test had enough power. there were bac log-ratios significantly different from their corresponding oligo log-ratios. to provide a more global comparison, a complementary analysis was carried out on the bac values corresponding to at least one oligo in the patients. the comparison was done by computing the kendall correlation for all pairs of bac and oligo log-ratios, the oligo value being a mean oligo log-ratio. the pair values are plotted on figure the correlation gives a significant p-value inferior to 1e - with τ =. a τ value of indicates that both values of a pair increase or decrease with a probability of %. when the kendall's correlation test is performed excluding bacs containing fewer than oligos, τ reaches. this comparison shows that there is a significant correlation between the bac and the oligo platform results. comparison between oligo and bac cgh using segmentation oligo and bac cgh platforms were compared after segmentation into regions of equal copy number as the individual number of copies can be corrupted with noise. the circular binary segmentation method was used both for bac and oligo data. this is illustrated in figure for chromosome from patient a statistical comparison was performed to assess the divergence of the bac and oligo data in the regions delimited by the segmentation. each segment is considered as a log-ratios sample and two comparisons were made. firstly, we compared globally all the bac and oligo segments. each bac segment was compared with the corresponding oligos, located between the start and the end of the bac segment. this was also done reciprocally with the oligo segments as the starting point, as the comparison is asymmetrical. the pair segment mean values of all patient samples are shown in figure the kendall correlation gives a p-value inferior to 1e - with τ significantly greater than the value of achieved without segmentation. secondly, all the segments were compared individually. before comparing the segment samples, a shapiro test was performed to determine whether their distributions are gaussian. a student test was used to compare the gaussian distributions, otherwise a wilcoxon-mann-whitney test was applied. a 1% false discovery rate was then used to avoid problems related to multiple testing. using these approaches we have identified of bac segments with values divergent from oligo values and of oligo divergent segments. to complete this result the same comparison was done with a false discovery rate of 5% and 10%. for 5% the number of divergent segments are then and for bac versus oligo and oligo versus bac respectively and for 10%, and finally, the comparison of the two platforms was focused on the detection of aberrant events. we considered as aberrations all segments with mean number of copies lower than or higher than. the bac platform detected deletions and amplifications among the patient samples whereas the oligo platform detected deletions and amplifications with median lengths of mbp and mbp respectively. this shows that oligo acgh detect more, smaller chromosomal events than bac acgh. regarding the genomic location of these segments, segments were found by both platforms either deleted or amplified with an overlapping region comprising more than 90% of each segment. it is important to note that their median length is mbp, indicating that large chromosomal aberrations are found by both platforms. in conclusion, segmentation enhances the correlation between the two platforms. another result is that the oligo acgh reveal many more, smaller aberrations. detection of outliers in bac segments in order to perform a more in-depth analysis of both platforms, that could then be applied to the identification of target genes and regions of genetic alteration, we developed a statistical model to detect bac outliers and validated the results with the oligo cgh platform. an advantage of the oligo cgh platform is its ability to detect microevents. in this section we show that, to some extent, the bac acgh platform, and more precisely bac outliers within segments, could indicate microevents. bac outliers can be detected by modelling their log-ratios with a gaussian distribution n where μs is the theoretical mean of the bac segment s and σ the standard deviation across all segments and all patients. however to avoid misestimating σ by including too many outliers, this parameter has been estimated on the least varying patient, containing n = bacs. let μ^ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaaiigacuwf8oqbgaqcaaaa@2e79@s and σ^ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaaiigacuwfdpwcgaqcaaaa@2e86@ be the estimators of μs and σ. to detect outliers the following classical calculus was performed considering each bac value x inside its corresponding segment s of size ns. for each bac, μ was reestimated by computing a new μs on the ns - value left after excluding the x value. x−μ^s=x−μs−⇔ns−1σ=ns−1σ−ns−1σ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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x7atnaabaaaleaacqwgzbwcaeqaaogaeiykakcabagae83wdmhaaiabgkhitmaalaaabawaaoaaaeaacqwgubgbdawgaawcbagaem4camhabeaakiabgkhitiabigdaxawcbeaakiabcicaoiqb=x7atzaajawaasbaasqaaiabdohazbqabagccqghsislcqwf8oqbdawgaawcbagaem4camhabeaakiabcmcapaqaaiab=n8azbaaaaa@74b2@ as ns−1σ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaamaakaaabagaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmasqabagccqggoaakcqwg4baecqghsisliigacqwf8oqbdawgaawcbagaem4camhabeaakiabcmcapaqaaiab=n8azbaaaaa@3b00@ and ns−1σ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaamaakaaabagaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmasqabagccqggoaakiigacuwf8oqbgaqcamaabaaaleaacqwgzbwcaeqaaogaeyoei0iae8hvd02aasbaasqaaiabdohazbqabagccqggpaqkaeaacqwfdpwcaaaaaa@3ced@ obey asymptotically the n and n law respectively, the distribution of ns−1σ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaamaakaaabagaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmasqabagccqggoaakcqwg4baecqghsisliigacuwf8oqbgaqcamaabaaaleaacqwgzbwcaeqaaogaeiykakcabagae83wdmhaaaaa@3b10@ is the n law. besides, it is known that the law of nσ^2σ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaaiabd6gauhgaciqb=n8azzaajawaawbaasqabeaaieaacqgfyagmaaaakeaacqwfdpwcaaaaaa@32e1@ is asymptotically χn−12 mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaaiigacqwfhpwydaqhaawcbagaemoba4maeyoei0iaegymaedabagaegomaidaaaaa@32cb@. from this it is inferred that: t=ns−1nsσnσ^2σ2 mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwg0badcqgh9aqpdawcaaqaamaalaaabawaaoaaaeaacqwgubgbdawgaawcbagaem4camhabeaakiabgkhitiabigdaxawcbeaakiabcicaoiabdiha4jabgkhitggaciqb=x7atzaajawaasbaasqaaiabdohazbqabagccqggpaqkaeaadagcaaqaaiabd6gaunaabaaaleaacqwgzbwcaeqaaaqabagccqwfdpwcaaaabawaaoaaaeaadawcaaqaaiabd6gaujqb=n8azzaajawaawbaasqabeaacqaiyagmaaaakeaacqggoaakcqwgubgbcqghsislcqaixaqmcqggpaqkcqwfdpwcdaahaawcbeqaaiabikdayaaaaaaabeaaaaaaaa@4cfe@ obeys asymptotically a student's law tn- t is simplified to: t=x−μ^sσ^nsn mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwg0badcqgh9aqpdawcaaqaaiabdiha4jabgkhitggaciqb=x7atzaajawaasbaasqaaiabdohazbqabaaakeaacuwfdpwcgaqcaaaadagcaaqaamaalaaabagaeiikagiaemoba42aasbaasqaaiabdohazbqabagccqghsislcqaixaqmcqggpaqkcqggoaakcqwgubgbcqghsislcqaixaqmcqggpaqkaeaacqwgubgbdawgaawcbagaem4camhabeaakiabd6gaubaaasqabaaaaa@4700@. a p-value is computed for each bac using the following procedure:    for each segment do       for each bac inside current segment do          produce a sample of all the segment log-ratios except the current bac one          compute a p-value for the bac value x according to formula          pnsn) mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwgqbaudaqadaqaaiabdsfaunaabaaaleaacqwgubgbcqghsislcqaixaqmaeqaaogaeyyzim7aasaaaeaacqwg4baecqghsisliigacuwf8oqbgaqcamaabaaaleaacqwgzbwcaeqaaagcbagaf83wdmnbakaaaawaaoaaaeaadawcaaqaaiabcicaoiabd6gaunaabaaaleaacqwgzbwcaeqaaogaeyoei0iaegymaejaeiykakiaeiikagiaemoba4maeyoei0iaegymaejaeiykakcabagaemoba42aasbaasqaaiabdohazbqabagccqwgubgbaaaaleqaaagccagloagaayzkaaaaaa@4db4@       done    done all p-values are treated according to taking fdr = 1%. this model was used on the patient samples. outliers were detected in the genomic profiles of the patients, across all the chromosomes. figure shows isolated bac outliers and two groups of bac outliers on chromosome of patient sample isolated bac outliers point to putative microevents whereas groups of bac outliers may indicate larger aberred regions. finally to validate statistically this result, the kendall's correlation with oligos was computed for the bac outliers with at least one corresponding oligo giving τ = and a p-value of 1e - discussion the conceptual advantage of a high density oligo acgh is that it can reveal microdeletions or amplifications at the gene level that may contribute to gene transcript variation and that are not detected on a bac platform. indeed the oligo platform has an average kbp spatial resolution that enables to span all the well characterised genes providing sufficient coverage for a genome wide survey of dna aberrations. different sizes of microdeletions, from to probes have been detected by the cbs method, such as the microdeletion containing the potential tumor suppressor atbf. in this paper, oligo acgh results are validated using the k bac acgh platform on data from patients. the clinical material used in the prima project is extremely unusual, displaying greater than 75% tumour cellularity. in general, other than in very advanced cancers, the level of tumour cellularity would be significantly lower for prostatic material and this together with lymphocytic infiltration might be expected to add to the noise component of the oligo acgh signal and reduce concordance both with bac array data and gene expression microarrays. the number of patients involved in this experiment allowed us to perform statistically significant analyses. indeed there are bacs that overlap with at least oligos and bacs with at least one oligo in the patients. the validation was first carried out directly, by matching each bac to the corresponding oligo raw data, and then indirectly, using a segmentation algorithm called cbs, which gave more significant results. the kendall's correlation on the raw data was significant, with τ =, and was improved by segmentation, reaching a value of. correlation was therefore increased more than two fold when compared to the direct comparison, confirming the observation of ijssel, that bac and oligo profiles are very similar after smoothing. this global analysis of all bac and oligo segments was complemented by an individual comparison, where each segment from bac or oligo platforms was tested against its corresponding oligo or bac data set. 14% and 11% of bac and oligo segments respectively were found to be divergent. these percentages can be lowered by scaling the oligo values as the log-ratios are higher for oligos than for bacs again in agreement with ijssel. however these statistical tests only reveal very divergent segments, indeed taking false discovery rates equal to or 10% gives higher percentages. so this number of divergent segments should be regarded as a lower bound. indeed there were only few similar copy number aberrations between the two platforms as the oligo platform presented the advantage to detect more, smaller chromosomic aberrations. secondly, we compared the noise level between the two platforms on raw and segmented data. computing the autocorrelation along each chromosome, we observed that the baseline variation for bacs is lower than for oligos in agreement with previous studies by ijssel and ylstra who both computed the standard deviation on regions without copy number changes. using the regions of equal copy numbers produced by cbs, the means of the oligo and bac segments standard deviations have been computed giving respectively and confirming the previous result. bacs with large insert clones display a lower variation compared with the oligo platform. however, the trade-off is a lower sensibility for bacs compared to oligos. besides, the standard deviation value of of the oligo platform, is small from a statistical point of view. indeed, the confidence interval of the true number of copies inside segments is small. for instance for a segment with oligos inside, a standard deviation of means that for a mean value of copies, the interval is –. in spite of the many advantages of the oligo acgh platform, there is still some value in using the bac platform. bacs are distributed uniformly on the human genome so that regions not previously found to be involved in cancer or non-coding regions are covered. this terra incognita may be interesting for further investigation, in particular to search for micrornas and repeated sequences. in addition, a bac outlier detection model that could point to putative microevents has been introduced in this article. the bac outlier values have been compared with the corresponding oligo values with good correlation. a large number of these bac outliers were found in regions without known genes according to ucsc. however these bac outliers represent large regions of kbp and the potential microevents must still be precisely located and biologically validated.  we have performed a large scale comparison of oligo and bac platforms using a set of patient samples. first, we have established statistically the reliability of the oligo platform for the identification of chromosomic events. moreover the oligo platform outperforms the bac technology for the detection of more, smaller aberrations. taking advantage of this large set of data, we have developped a statistical model, that highlights that bacs may detect putative microevents. hopefully, this result will incite researchers to reconsider the potential use of bac data for more in-depth investigation of new data, as well as for the numerous publically available bac cgh data. the challenge in future studies will be the routine establishment of banks of well-defined laser captured material, so that the greater sensitivity of these platforms can be successfully exploited. in order to obtain a more complete picture of cancer, attempts could then be made to combine the acgh approaches with transcriptomic and proteomic technologies. 
taking advantage of a large set of human samples treated by the two technologies, a statistical model has been developed to show that the bac platform could also detect microevents. these microevents were validated by the oligo platform results. currently, two main technologies are used for screening of dna copy number; the bac and the recently developed oligonucleotide-based cgh arrays which are capable of detecting small genomic regions with amplification or deletion. this article presents a genome-wide statistical validation of the oligo array platform on a large set of patient samples and demonstrates statistically its superiority over the bac platform for the identification of chromosomic events. in addition, this analysis has enabled the development of a statistical model to discriminate bac outliers that might indicate microevents. in this paper, we present an exhaustive comparison between the two cgh platforms, undertaken at two independent sites using the same batch of dna from advanced prostate cancers. the comparison was performed directly on the raw data and a significant correlation was found between the two platforms. the correlation as well as the discriminative power of these platforms has never been compared statistically on a significant set of human patient samples. the correlation was greatly improved when the data were averaged over large chromosomic regions using a segmentation algorithm.
 currently, two main technologies are used for screening of dna copy number; the bac and the recently developed oligonucleotide-based cgh arrays which are capable of detecting small genomic regions with amplification or deletion. the correlation as well as the discriminative power of these platforms has never been compared statistically on a significant set of human patient samples.  in this paper, we present an exhaustive comparison between the two cgh platforms, undertaken at two independent sites using the same batch of dna from advanced prostate cancers. the comparison was performed directly on the raw data and a significant correlation was found between the two platforms. the correlation was greatly improved when the data were averaged over large chromosomic regions using a segmentation algorithm. in addition, this analysis has enabled the development of a statistical model to discriminate bac outliers that might indicate microevents. these microevents were validated by the oligo platform results.  this article presents a genome-wide statistical validation of the oligo array platform on a large set of patient samples and demonstrates statistically its superiority over the bac platform for the identification of chromosomic events. taking advantage of a large set of human samples treated by the two technologies, a statistical model has been developed to show that the bac platform could also detect microevents. 