clValid: An R Package for Cluster Validation

The R package clValid contains functions for validating the results of a clustering analysis. There are three main types of cluster validation measures available, "internal", "stability", and "biological". The user can choose from nine clustering algorithms in existing R packages, including hierarchical, K-means, self-organizing maps (SOM), and model-based clustering. In addition, we provide a function to perform the self-organizing tree algorithm (SOTA) method of clustering. Any combination of validation measures and clustering methods can be requested in a single function call. This allows the user to simultaneously evaluate several clustering algorithms while varying the number of clusters, to help determine the most appropriate method and number of clusters for the dataset of interest. Additionally, the package can automatically make use of the biological information contained in the Gene Ontology (GO) database to calculate the biological validation measures, via the annotation packages available in Bioconductor. The function returns an object of S4 class "clValid", which has summary, plot, print, and additional methods which allow the user to display the optimal validation scores and extract clustering results.

Computational biology touches all bases

A report of the 6th Annual Rocky Mountain Bioinformatics Conference, Aspen, USA, 4-7 December 2008

RankAggreg, an R package for weighted rank aggregation

<p>Abstract</p> <p>Background</p> <p>Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.</p> <p>Results</p> <p>The <it>RankAggreg </it>package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments.</p> <p>Conclusion</p> <p>The two examples described in the manuscript clearly show the utility of the <it>RankAggreg </it>package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.</p

clValid: An R Package for Cluster Validation

The R package clValid contains functions for validating the results of a clustering analysis. There are three main types of cluster validation measures available, "internal", "stability", and "biological". The user can choose from nine clustering algorithms in existing R packages, including hierarchical, K-means, self-organizing maps (SOM), and model-based clustering. In addition, we provide a function to perform the self-organizing tree algorithm (SOTA) method of clustering. Any combination of validation measures and clustering methods can be requested in a single function call. This allows the user to simultaneously evaluate several clustering algorithms while varying the number of clusters, to help determine the most appropriate method and number of clusters for the dataset of interest. Additionally, the package can automatically make use of the biological information contained in the Gene Ontology (GO) database to calculate the biological validation measures, via the annotation packages available in Bioconductor. The function returns an object of S4 class "clValid", which has summary, plot, print, and additional methods which allow the user to display the optimal validation scores and extract clustering results

A novel statistical approach for identification of the master regulator transcription factor

Test Dataset. This file contains an example test dataset where our method can be implemented. This simulated data contains 10 transcription factors, namely TF 1, TF 2, …, TF 10 along with 105 genes that were regulated by these transcription factors. Among the transcription factors, TF 1 was generated to play the role of the master regulator. (CSV 1382 kb

Re-imaging Capitalism through Social Entrepreneurship

Abstract: Social Entrepreneurship focuses on activities that make world a better place to live in Social Entrepreneurship addresses various social issues. One such issue is rural development and poverty eradication. One way to achieve this is through self-Help groups. Self –Help group ( SHG) is a unique concept in India Self Help group is a homogenous group of people who have come together with the intention of increasing their income, improve their standard of living and status in society. Self –Help groups is a tool to eradicate poverty and encourage rural development. This study looks into journey of two women from two self-help groups of West Bengal. One SHG is located in rural area and another is in urban area. Self help groups helped them in developing their enterprise. These two micro entrepreneurs in turn provided livelihood to many women in their locality. They have been instrumental in providing other women in their locality with decent income. Self-Help groups not only helped in eradication of poverty but also helped in empowerment of women by providing them with income and social recognition