Djork-Arné Clevert

cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Current methods detect CNVs as changes in the depth of coverage along chromosomes. Technological or genomic variations in the depth of coverage thus lead to a high false discovery rate (FDR), even upon correction for GC content. In the context of association studies between CNVs and disease, a high FDR means many false CNVs, thereby decreasing the discovery power of the study after correction for multiple testing. We propose ‘Copy Number estimation by a Mixture Of PoissonS’ (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections. We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1–FDR) and recall for both gains and losses in all benchmark data sets. The software cn.MOPS is publicly available as an R package at http://www.bioinf.jku.at/software/cnmops/ and at Bioconductor.

FABIA: factor analysis for bicluster acquisition

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques. Results: On 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches. Availability: FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html Contact: hochreit@bioinf.jku.at Supplementary information: Supplementary data are available at Bioinformatics online.

I/NI-calls for the exclusion of non-informative genes

MOTIVATION DNA microarray technology typically generates many measurements of which only a relatively small subset is informative for the interpretation of the experiment. To avoid false positive results, it is therefore critical to select the informative genes from the large noisy data before the actual analysis. Most currently available filtering techniques are supervised and therefore suffer from a potential risk of overfitting. The unsupervised filtering techniques, on the other hand, are either not very efficient or too stringent as they may mix up signal with noise. We propose to use the multiple probes measuring the same target mRNA as repeated measures to quantify the signal-to-noise ratio of that specific probe set. A Bayesian factor analysis with specifically chosen prior settings, which models this probe level information, is providing an objective feature filtering technique, named informative/non-informative calls (I/NI calls). RESULTS Based on 30 real-life data sets (including various human, rat, mice and Arabidopsis studies) and a spiked-in data set, it is shown that I/NI calls is highly effective, with exclusion rates ranging from 70% to 99%. Consequently, it offers a critical solution to the curse of high-dimensionality in the analysis of microarray data. AVAILABILITY This filtering approach is publicly available as a function implemented in the R package FARMS (www.bioinf.jku.at/software/farms/farms.html).

Imaging of aortic abnormalities with contrast-enhanced ultrasound. A pictorial comparison with CT

Aortic abnormalities are commonly encountered and may represent a diagnostic challenge in patients with acute or chronic clinical symptoms. Contrast-enhanced ultrasound (CEUS) with low mechanical index (low MI) is a new promising method in the diagnosis and follow-up of pathological aortic lesions. CEUS with SonoVue allows a more rapid and noninvasive diagnosis, especially in critical patients because of its bedside availability. This review compares CEUS findings with those documented on computed tomography angiography (CTA), allowing the reader to appreciate the usefulness of CEUS in this clinical situation.

Genome-wide copy number alterations detection in fresh frozen and matched FFPE samples using SNP 6.0 arrays

SNP arrays offer the opportunity to get a genome‐wide view on copy number alterations and are increasingly used in oncology. DNA from formalin‐fixed paraffin‐embedded material (FFPE) is partially degraded which limits the application of those technologies for retrospective studies. We present the use of Affymetrix GeneChip SNP6.0 for identification of copy number alterations in fresh frozen (FF) and matched FFPE samples. Fifteen pairs of adenocarcinomas with both frozen and FFPE embedded material were analyzed. We present an optimization of the sample preparation and show the importance of correcting the measured intensities for fragment length and GC‐content when using FFPE samples. The absence of GC content correction results in a chromosome specific “wave pattern” which may lead to the misclassification of genomic regions as being altered. The highest concordance between FFPE and matched FF were found in samples with the highest call rates. Nineteen of the 23 high level amplifications (83%) seen using FF samples were also detected in the corresponding FFPE material. For limiting the rate of “false positive” alterations, we have chosen a conservative False Discovery Rate (FDR). We observed better results using SNP probes than CNV probes for copy number analysis of FFPE material. This is the first report on the detection of copy number alterations in FFPE samples using Affymetrix GeneChip SNP6.0.

Color duplex ultrasound and contrast-enhanced ultrasound in comparison to MS-CT in the detection of endoleak following endovascular aneurysm repair

The purpose of this study was to compare Color Duplex Ultrasound (CDU), Contrast-Enhanced Ultrasound (CEUS) and Multislice Computed Tomography (MS-CT) angiography in the routine follow up of patients following Endovascular Repair (EVAR) of Abdominal Aortic Aneurysm (AAA).43 consecutive patients with AAA underwent endovascular aneurysm repair and were imaged with CDU, CEUS and MS-CT angiography at regular intervals after the procedure. Each imaging modality was evaluated for the detection of endoleaks. The presence of endoleaks was analyzed and the conspicuity of findings was assessed.CTA was used as gold standard in determining the presence of endoleaks. CDU was true positive for endoleaks in 5/43 patients (11.6%) and false positive for endoleaks in 2/43 patients (4.6%). The sensitivity of CDU was therefore 33.3% and its specificity 92.8%; the positive and negative predictive values were 0.71 and 0.72, respectively. CEUS was true positive for the detection of endoleaks in 15/43 patients (34.9%) and false positive in 2/43 patients (4.6%). The sensitivity of CEUS was therefore 100% and its specificity 93%; the positive and negative predictive values were 0.88 and 1. In the follow up the two false positive endoleaks in CEUS were confirmed as true positive endoleaks by CEUS and MS-CT. In our small patient group, contrast-enhanced ultrasound seemed to be more accurately in demonstrating endoleaks after EVAR than MS-CT angiography and may be considered as a primary surveillance modality whereas duplex ultrasound scanning alone is not as sensitive as CEUS and MS-CT angiography in detection of endoleaks. Especially in patients with contraindications for CT contrast agents (e.g. due to renal failure or severe allergy) CEUS provides a good alternative to MS-CT.

Contrast-enhanced ultrasound versus MS-CT in blunt abdominal trauma

To evaluate the effectiveness of contrast-enhanced ultrasound (CEUS) in the diagnosis and characterization of hepatic, renal and splenic traumatic injuries versus conventional ultrasound (US) and multislice computed tomography (MS-CT). Between January 2005 and January 2007, 78 patients (48 males, 30 females, mean age 56 years) with blunt abdominal trauma were examined by conventional US, CEUS and MS-CT. CEUS employed a low-MI technique using 1.2 to 2.4 ml of SonoVue (Bracco, Italy) i.v. and a multifrequency transducer (2-4 MHz, Siemens, Sequoia, Acuson). CT examinations were performed on a 64 detector CT scanner (Somatom Sensation 16 or 64, Siemens Medical Systems, Forchheim, Germany) before and after administration of 120 ml intravenous contrast agent (Solutrast, Bracco, Milan, Italy) followed by 50 ml saline. The presence of hepatic, renal and splenic injuries was analyzed and the conspicuousness of findings was assessed. In 15 of the 78 patients conventional US identified solid organ injuries: 8 hepatic, 2 renal and 5 splenic injuries. CEUS identified 3 more injuries (2 hepatic and 1 splenic) that had been missed by conventional US. CEUS identified traumatic lesions in 18/78 patients. In one of the 18 patients even active bleeding could be identified by CEUS. In CEUS solid organ injuries appeared hypoechoic. MS-CT identified 18 solid organ injuries in 78 patients, corroborating the CEUS results.CEUS greatly improves the visualization and characterization of hepatic, renal and splenic injuries compared to conventional ultrasound and correlates well with MS-CT. The imaging technique detects even minor blood flow and is able to depict vascular structures in detail. At our institution it is used as an additional examination technique which supplements MS-CT in unclear cases. Owing to its bedside availability, CEUS provides a good alternative to MS-CT, especially in patients with contraindications to CT contrast agents (e.g. due to renal failure or severe allergy) and in hemodynamically compromised patients.

Contrast-enhanced ultrasound versus conventional ultrasound and MS-CT in the diagnosis of abdominal aortic dissection

PURPOSE To evaluate the diagnostic results of different ultrasound techniques: B-scan, color-coded Doppler sonography (CCDS) and contrast-enhanced ultrasound in the diagnosis of abdominal aortic dissection in comparison to multislice computed tomography (MS-CT). MATERIALS AND METHODS Between March 2006 and December 2008, 35 patients (28 males, 7 females) with a mean age of 58 years (range 37-87 years) with abdominal aortic dissection and 15 patients (11 males, 4 females) with a mean age of 53 years (range 42-78 years) without abdominal aortic dissection as a control group were examined with B-scan, CCDS and contrast-enhanced ultrasound (CEUS) after injection of 1.0-1.2 cc of SonoVue (Bracco, Italy). The examinations were performed using a Sequoia 512 (Siemens/Acuson, Mountain View) system with CPS software. Standardized MS-CTA using a 16 or 64 row scanner (Somatom Sensation 16 or 64, Siemens Medical Systems, Forchheim, Germany) served as the reference standard. RESULTS The sensitivity of B-scan and CCDS for detecting abdominal aortic dissections were both 23/35 (68%); for contrast-enhanced ultrasound it was 34/35 (97%). Dissection membrane, differentiation of true and false lumen and flow direction within the true and false lumen were better detected by CEUS than by CCDS. The lack of angle dependence of the US probe and lack of flow and pulsations artifacts in CEUS made the examination procedure easier. All findings were confirmed by MS-CT. CONCLUSION With contrast-enhanced ultrasound, diagnostic accuracy sensitivity and specificity for the diagnosis of abdominal aortic dissections is improved as compared to B-scan and CCDS. Dissection membrane and flow within the true and false lumen are clearly differentiated by CEUS. Thus CEUS is a promising alternative for patients whose condition does not allow an examination by CTA.

cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate

Cost-effective oligonucleotide genotyping arrays like the Affymetrix SNP 6.0 are still the predominant technique to measure DNA copy number variations (CNVs). However, CNV detection methods for microarrays overestimate both the number and the size of CNV regions and, consequently, suffer from a high false discovery rate (FDR). A high FDR means that many CNVs are wrongly detected and therefore not associated with a disease in a clinical study, though correction for multiple testing takes them into account and thereby decreases the studys discovery power. For controlling the FDR, we propose a probabilistic latent variable model, ‘cn.FARMS’, which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. On HapMap data, cn.FARMS clearly outperformed the two most prevalent methods with respect to sensitivity and FDR. The software cn.FARMS is publicly available as a R package at http://www.bioinf.jku.at/software/cnfarms/cnfarms.html.

Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach

The strength and weakness of microarray technology can be attributed to the enormous amount of information it is generating. To fully enhance the benefit of microarray technology for testing differentially expressed genes and classification, there is a need to minimize the amount of irrelevant genes present in microarray data. A major interest is to use probe-level data to call genes informative or noninformative based on the trade-off between the array-to-array variability and the measurement error. Existing works in this direction include filtering likely uninformative sets of hybridization (FLUSH; Calza et al., 2007) and I/NI calls for the exclusion of noninformative genes using FARMS (I/NI calls; Talloen et al., 2007; Hochreiter et al., 2006). In this paper, we propose a linear mixed model as a more flexible method that performs equally good as I/NI calls and outperforms FLUSH. We also introduce other criteria for gene filtering, such as, R2 and intra-cluster correlation. Additionally, we include some objective criteria based on likelihood ratio testing, the Akaike information criteria (AIC; Akaike, 1973) and the Bayesian information criterion (BIC; Schwarz, 1978 ).Based on the HGU-133A Spiked-in data set, it is shown that the linear mixed model approach outperforms FLUSH, a method that filters genes based on a quantile regression. The linear model is equivalent to a factor analysis model when either the factor loadings are set to a constant with the variance of the latent factor equal to one, or if the factor loadings are set to one together with unconstrained variance of the latent factor. Filtering based on conditional variance calls a probe set informative when the intensity of one or more probes is consistent across the arrays, while filtering using R2 or intra-cluster correlation calls a probe set informative only when average intensity of a probe set is consistent across the arrays. Filtering based on likelihood ratio test AIC and BIC are less stringent compared to the other criteria.