Factor analysis (FA) provides linear factors that describe the relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe the relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also naturally extends canonical correlation analysis to more than two sets, in a way that is more flexible than previous extensions. Our solution is formulated as a variational inference of a latent variable model with structural sparsity, and it consists of two hierarchical levels: 1) the higher level models the relationships between the groups and 2) the lower models the observed variables given the higher level. We show that the resulting solution solves the group factor analysis (GFA) problem accurately, outperforming alternative FA-based solutions as well as more straightforward implementations of GFA. The method is demonstrated on two life science data sets, one on brain activation and the other on systems biology, illustrating its applicability to the analysis of different types of high-dimensional data sources.

Ewing sarcoma family of tumors (ESFT), characterized by t(11;22)(q24;q12), is one of the most common tumors of bone in children and young adults. In addition to EWS/FLI1 gene fusion, copy number changes are known to be significant for the underlying neoplastic development of ESFT and for patient outcome. Our genome-wide high-resolution analysis aspired to pinpoint genomic regions of highest interest and possible target genes in these areas. Array comparative genomic hybridization (CGH) and expression arrays were used to screen for copy number alterations and expression changes in ESFT patient samples. A total of 31 ESFT samples were analyzed by aCGH and in 16 patients DNA and RNA level data, created by expression arrays, was integrated. Time of the follow-up of these patients was 5–192 months. Clinical outcome was statistically evaluated by Kaplan-Meier/Logrank methods and RT-PCR was applied on 42 patient samples to study the gene of the highest interest. Copy number changes were detected in 87% of the cases. The most recurrent copy number changes were gains at 1q, 2, 8, and 12, and losses at 9p and 16q. Cumulative event free survival (ESFT) and overall survival (OS) were significantly better (P < 0.05) for primary tumors with three or less copy number changes than for tumors with higher number of copy number aberrations. In three samples copy number imbalances were detected in chromosomes 11 and 22 affecting the FLI1 and EWSR1 loci, suggesting that an unbalanced t(11;22) and subsequent duplication of the derivative chromosome harboring fusion gene is a common event in ESFT. Further, amplifications on chromosomes 20 and 22 seen in one patient sample suggest a novel translocation type between EWSR1 and an unidentified fusion partner at 20q. In total 20 novel ESFT associated putative oncogenes and tumor suppressor genes were found in the integration analysis of array CGH and expression data. Quantitative RT-PCR to study the expression levels of the most interesting gene, HDGF, confirmed that its expression was higher than in control samples. However, no association between HDGF expression and patient survival was observed. We conclude that array CGH and integration analysis proved to be effective methods to identify chromosome regions and novel target genes involved in the tumorigenesis of ESFT.

Studies on retention and success in introductory programming course have suggested that previous programming experience contributes to students' course outcomes. If such background information could be automatically distilled from students' working process, additional guidance and support mechanisms could be provided even to those, who do not wish to disclose such information. In this study, we explore methods for automatically distinguishing novice programmers from more experienced programmers using fine-grained source code snapshot data. We approach the issue by partially replicating a previous study that used students' keystroke latencies as a proxy to introductory programming course outcomes, and follow this by an exploration of machine learning methods to separate those students with little to no previous programming experience from those with more experience. Our results confirm that students' keystroke latencies can be used as a metric for measuring course outcomes. At the same time, our results show that students programming experience can be identified to some extent from keystroke latency data, which means that such data has potential as a source of information for customizing the students' learning experience.