Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

<p>Abstract</p> <p>Background</p> <p>The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).</p> <p>Results</p> <p>We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.</p> <p>Conclusion</p> <p>The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.</p

Expectations and outcomes of a doctorate abroad : career development and mobility patterns of expatriate researchers in social sciences.

The research presented in this thesis explores how expatriate researchers who completed a PhD abroad evaluate and articulate their experience of academic mobility with regard to their early postdoctoral career and personal development. Having adopted a mixed-methods approach, this study draws on an original dataset including 20 semi-structured interviews and 281 replies to an online survey. By conducting thematic analysis of the interview data in NVivo and descriptive analysis of the survey data in SPSS, the research presented in this thesis provides replies to the following three questions. First, what do expatriate researchers expect from their doctoral experience of academic mobility? Second, does academic mobility during a doctorate result in career-related outcomes, according to the perceptions of expatriate researchers? Finally, what is the individual value of a doctorate abroad for expatriate researchers? This study argues that expatriate researchers in social sciences embark on a doctoral study abroad without necessarily expecting any immediate career-related returns but are influenced by contextual factors, such as the opportunity structure and insecure employment conditions in the labour market for PhD graduates. In addition, the present research has not found any strong evidence showing that academic mobility directly brings immediate career-related returns. In summary, this research provides evidence of widespread agreement among expatriate researchers that the value of a doctoral degree from abroad is in gaining a meaningful personal experience resulting in personal development and skills acquisition, rather than directly resulting in career advancement. This finding contributes to the knowledge of the value of a doctoral study abroad on the individual level, suggested as an under-researched area by the scholarly literature in the field (Raddon and Sung, 2006; Nerad and Cerny, 2000; Casey, 2009)

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-4

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p> using the parameters = 5 and = 10, visualized using Java TreeView [42]. NNN clusters have been colored, internally hierarchically clustered, and the cluster centroids have in turn been hierarchically clustered to provide an easily interpretable tree

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-0

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p> using the parameters = 5 and = 10, visualized using Java TreeView [42]. NNN clusters have been colored, internally hierarchically clustered, and the cluster centroids have in turn been hierarchically clustered to provide an easily interpretable tree

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-3

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p>rocesses for which data was available in our analysis. For each algorithm, the maximum AUC across all six data sets was determined, and the resulting AUCs are presented here in descending order per algorithm. NNN correctly clusters genes from substantially more biological processes relative to previous methods

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-2

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p>and GO term basis. Each cell represents an AUC score calculated analytically using the Wilcoxon Rank Sum formula; below baseline performance appears in blue, and yellow indicates higher performance. Data set and term combinations for which ten or fewer pairs were able to be evaluated are excluded and appear as gray missing values; functions for which less than 10% of methods were available due to gene exclusion by NNN, QTC, or SAMBA were removed. Visualization provided by TIGR MeV [41]