Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

A Robust Curve-Fitting Procedure for the Analysis of Plasmid DNA Strand Break Data from Gel Electrophoresis

A robust method for fitting to the results of gel electrophoresis assays of damage to plasmid DNA caused by radiation is presented. This method makes use of nonlinear regression to fit analytically derived dose response curves to observations of the supercoiled, open circular and linear plasmid forms simultaneously, allowing for more accurate results than fitting to individual forms. Comparisons with a commonly used analysis method show that while there is a relatively small benefit between the methods for data sets with small errors, the parameters generated by this method remain much more closely distributed around the true value in the face of increasing measurement uncertainties. This allows for parameters to be specified with greater confidence, reflected in a reduction of errors on fitted parameters. On test data sets, fitted uncertainties were reduced by 30%, similar to the improvement that would be offered by moving from triplicate to fivefold repeats (assuming standard errors). This method has been implemented in a popular spreadsheet package and made available online to improve its accessibility. (C) 2011 by Radiation Research Society

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Helping the most vulnerable out of the poverty trap and reducing inequality: Policies, strategies, and services for individuals with Autism Spectrum Disorder, including intellectual and neurodevelopmental disabilities: Benchmarking Autism Services Efficacy: BASE Project (Volume 3) Secondary Data analysis

1) Executive Summary
Legislation (Autism Act NI, 2011), a cross-departmental strategy (Autism Strategy 2013-2020) and a first action plan (2013-2016) have been developed in Northern Ireland in order to support individuals and families affected by Autism Spectrum Disorder (ASD) without a prior thorough baseline assessment of need. At the same time, there are large existing data sets about the population in NI that had never been subjected to a secondary data analysis with regards to data on ASD. This report covers the first comprehensive secondary data analysis and thereby aims to inform future policy and practice.
Following a search of all existing, large-scale, regional or national data sets that were relevant to the lives of individuals and families affected by Autism Spectrum Disorder (ASD) in Northern Ireland, extensive secondary data analyses were carried out. The focus of these secondary data analyses was to distill any ASD related data from larger generic data sets. The findings are reported for each data set and follow a lifespan perspective, i.e., data related to children is reported first before data related to adults.
Key findings:
Autism Prevalence:
Of children born in 2000 in the UK,
• 0.9% (1:109) were reported to have ASD, when they were 5-year old in 2005;
• 1.8% (1:55) were reported to have ASD, when they were 7-years old in 2007;
• 3.5% (1:29) were reported to have ASD, when they were 11-year old in 2011.
In mainstream schools in Northern Ireland
• 1.2% of the children were reported to have ASD in 2006/07;
• 1.8% of the children were reported to have ASD in 2012/13.

Economic Deprivation:
• Families of children with autism (CWA) were 9%-18% worse off per week than families of children not on the autism spectrum (COA).
• Between 2006-2013 deprivation of CWA compared to COA nearly doubled as measured by eligibility for free school meals (from near 20 % to 37%)
• In 2006, CWA and COA experienced similar levels of deprivation (approx. 20%), by 2013, a considerable deprivation gap had developed, with CWA experienced 6% more deprivation than COA.
• Nearly 1/3 of primary school CWA lived in the most deprived areas in Northern Ireland.
• Nearly ½ of children with Asperger’s Syndrome who attended special school lived in the most deprived areas.

Unemployment:
• Mothers of CWA were 6% less likely to be employed than mothers of COA.
• Mothers of CWA earned 35%-56% less than mothers of COA.
• CWA were 9% less likely to live in two income families than COA.

Health:
• Pre-diagnosis, CWA were more likely than COA to have physical health problems, including walking on level ground, speech and language, hearing, eyesight, and asthma.
• Aged 3 years of age CWA experienced poorer emotional and social health than COA, this difference increased significantly by the time they were 7 years of age.
• Mothers of young CWA had lower levels of life satisfaction and poorer mental health than mothers of young COA.
Education:
• In mainstream education, children with ASD aged 11-16 years reported less satisfaction with their social relationships than COA.
• Younger children with ASD (aged 5 and 7 years) were less likely to enjoy school, were bullied more, and were more reluctant to attend school than COA.
• CWA attended school 2-3 weeks less than COA .
• Children with Asperger’s Syndrome in special schools missed the equivalent of 8-13 school days more than children with Asperger’s Syndrome in mainstream schools.
• Children with ASD attending mainstream schooling were less likely to gain 5+ GCSEs A*-C or subsequently attend university.



Further and Higher Education:
• Enrolment rates for students with ASD have risen in Further Education (FE), from 0% to 0.7%.
• Enrolment rates for students with ASD have risen in Higher Education (HE), from 0.28% to 0.45%.
• Students with ASD chose to study different subjects than students without ASD, although other factors, e.g., gender, age etc. may have played a part in subject selection.
• Students with ASD from NI were more likely than students without ASD to choose Northern Irish HE Institutions rather than study outside NI.

Participation in adult life and employment:
• A small number of adults with ASD (n=99) have benefitted from DES employment provision over the past 12 years.
• It is unknown how many adults with ASD have received employment support elsewhere (e.g. Steps to Work).
•
Awareness and Attitudes in the General Population:
• In both the 2003 and 2012 NI Life and Times Survey (NILTS), NI public reported positive attitudes towards the inclusion of children with ASD in mainstream education (see also BASE Project Vol. 2).

Gap Analysis Recommendations:
This was the first comprehensive secondary analysis with regards to ASD of existing large-scale data sets in Northern Ireland. Data gaps were identified and further replications would benefit from the following data inclusion:
• ASD should be recorded routinely in the following datasets:
o Census;
o Northern Ireland Survey of Activity Limitation (NISALD);
o Training for Success/Steps to work; Steps to Success;
o Travel survey;
o Hate crime; and
o Labour Force Survey.
• Data should be collected on the destinations/qualifications of special school leavers.
• NILT Survey autism module should be repeated in 5 years time (2017) (see full report of 1st NILT Survey autism module 2012 in BASE Project Report Volume 2).
• General public attitudes and awareness should be assessed for children and young people, using the Young Life and Times Survey (YLT) and the Kids Life and Times Survey (KLT); (this work is underway, Dillenburger, McKerr, Schubolz, & Lloyd, 2014-2015).
 

Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices

In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases.

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis.

METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer.

RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs.

CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.

Tri-axial accelerometers quantify behaviour in the Eurasian badger (Meles meles): towards an automated interpretation of field data

The popularity of tri-axial accelerometer data loggers to quantify animal activity through the analysis of signature traces is increasing. However, there is no consensus on how to process the large data sets that these devices generate when recording at the necessary high sample rates. In addition, there have been few attempts to validate accelerometer traces with specific behaviours in non-domesticated terrestrial mammals.

Maintaining legacy data: saving Belfast Harbour (UK) tide-gauge data (1902-2010)

Tide gauge data are identified as legacy data given the radical transition between observation method and required output format associated with tide gauges over the 20th-century. Observed water level variation through tide-gauge records is regarded as the only significant basis for determining recent historical variation (decade to century) in mean sea-level and storm surge. There are limited tide gauge records that cover the 20th century, such that the Belfast (UK) Harbour tide gauge would be a strategic long-term (110 years) record, if the full paper-based records (marigrams) were digitally restructured to allow for consistent data analysis. This paper presents the methodology of extracting a consistent time series of observed water levels from the 5 different Belfast Harbour tide gauges’ positions/machine types, starting late 1901. Tide-gauge data was digitally retrieved from the original analogue (daily) records by scanning the marigrams and then extracting the sequential tidal elevations with graph-line seeking software (Ungraph™). This automation of signal extraction allowed the full Belfast series to be retrieved quickly, relative to any manual x–y digitisation of the signal. Restructuring variably lengthed tidal data sets to a consistent daily, monthly and annual file format was undertaken by project-developed software: Merge&Convert and MergeHYD allow consistent water level sampling both at 60 min (past standard) and 10 min intervals, the latter enhancing surge measurement. Belfast tide-gauge data have been rectified, validated and quality controlled (IOC 2006 standards). The result is a consistent annual-based legacy data series for Belfast Harbour that includes over 2 million tidal-level data observations.