"Virus hunting" using radial distance weighted discrimination

Motivated by the challenge of using DNA-seq data to identify viruses in human blood samples, we propose a novel classification algorithm called "Radial Distance Weighted Discrimination" (or Radial DWD). This classifier is designed for binary classification, assuming one class is surrounded by the other class in very diverse radial directions, which is seen to be typical for our virus detection data. This separation of the 2 classes in multiple radial directions naturally motivates the development of Radial DWD. While classical machine learning methods such as the Support Vector Machine and linear Distance Weighted Discrimination can sometimes give reasonable answers for a given data set, their generalizability is severely compromised because of the linear separating boundary. Radial DWD addresses this challenge by using a more appropriate (in this particular case) spherical separating boundary. Simulations show that for appropriate radial contexts, this gives much better generalizability than linear methods, and also much better than conventional kernel based (nonlinear) Support Vector Machines, because the latter methods essentially use much of the information in the data for determining the shape of the separating boundary. The effectiveness of Radial DWD is demonstrated for real virus detection.Comment: Published at http://dx.doi.org/10.1214/15-AOAS869 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Composition-Dependent Passivation Efficiency at the CdS/CuIn1-xGaxSe2 Interface

International audienc

Epstein-Barr Virus-Positive Cancers Show Altered B-Cell Clonality

Epstein-Barr virus (EBV) is convincingly associated with gastric cancer, nasopharyngeal carcinoma, and certain lymphomas, but its role in other cancer types remains controversial. To test the hypothesis that there are additional cancer types with high prevalence of EBV, we determined EBV viral expression in all the Cancer Genome Atlas Project (TCGA) mRNA sequencing (mRNA-seq) samples (n 10,396) from 32 different tumor types. We found that EBV was present in gastric adenocarcinoma and lymphoma, as expected, and was also present in 5% of samples in 10 additional tumor types. For most samples, EBV transcript levels were low, which suggests that EBV was likely present due to infected infiltrating B cells. In order to determine if there was a difference in the B-cell populations, we assembled B-cell receptors for each sample and found B-cell receptor abundance (P 1.4 1020) and diversity (P 8.3 1027) were significantly higher in EBV-positive samples. Moreover, diversity was independent of B-cell abundance, suggesting that the presence of EBV was associated with an increased and altered B-cell population. IMPORTANCE Around 20% of human cancers are associated with viruses. Epstein-Barr virus (EBV) contributes to gastric cancer, nasopharyngeal carcinoma, and certain lymphomas, but its role in other cancer types remains controversial. We assessed the prevalence of EBV in RNA-seq from 32 tumor types in the Cancer Genome Atlas Project (TCGA) and found EBV to be present in 5% of samples in 12 tumor types. EBV infects epithelial cells and B cells and in B cells causes proliferation. We hypothesized that the low expression of EBV in most of the tumor types was due to infiltration of B cells into the tumor. The increase in B-cell abundance and diversity in subjects where EBV was detected in the tumors strengthens this hypothesis. Overall, we found that EBV was associated with an increased and altered immune response. This result is not evidence of causality, but a potential novel biomarker for tumor immune status

Ovarian dysgenesis associated with an unbalanced X;6 translocation: first characterisation of reproductive anatomy and cytogenetic evaluation in partial trisomy 6 with breakpoints at Xq22 and 6p23.

The aim of this study was to describe the clinical and laboratory findings associated with a previously unreported unbalanced X;6 translocation. Physical examination, reproductive history and cytogenetic techniques were used to characterise a novel chromosomal anomaly associated with gonadal dysgenesis. A healthy non-dysmorphic 23 year-old phenotypic female with primary amenorrhea and infertility presented for reproductive endocrinology evaluation. No discrete ovarian tissue was identified on transvaginal ultrasound, although the uterus appeared essentially normal. BMI was 19 kg/m2. Serum FSH and oestradiol were 111 mIU/ml and 15 pmol/l, respectively. TSH, prolactin and all infectious serologies were all normal. The karyotype of 46,X,der(X)t(X;6)(q22;p23) was determined following cytogenetic analysis of peripheral blood lymphocytes via fluorescence in situ hybridisation (FISH) with whole chromosome paint for chromosome 6, and a separate FISH analysis using a 6p subtelomeric probe. The patient was continued on hormone replacement therapy and underwent genetic counselling; the patient subsequently enrolled as a recipient in an anonymous donor oocyte IVF treatment. Translocations involving autosomes and chromosome X are rare. While female carriers of balanced X;autosome translocations are generally phenotypically normal, the impact of unbalanced X;autosome translocations can be severe. This is the first known report of an unbalanced translocation involving X;6. This abnormality was associated with ovarian dysgenesis, but an otherwise normal female phenotype. From this investigation, the observed developmental impact of the unbalanced translocation with breakpoints at Xq22 and 6p23 appears to be limited to ovarian failure

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

A General Framework for Consistency of principal component analysis

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