Biomedical subjects

Sach Mukherjee

Publications and source records attributed to Sach Mukherjee.

4 recordsLinked to original sources

PubMed2006

Next station in microarray data analysis: GEPAS.

The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

Cluster Analysis↗

PubMed2005

Data-adaptive test statistics for microarray data.

MOTIVATION: An important task in microarray data analysis is the selection of genes that are differentially expressed between different tissue samples, such as healthy and diseased. However, microarray data contain an enormous number of dimensions (genes) and very few samples (arrays), a mismatch which poses fundamental statistical problems for the selection process that have defied easy resolution. RESULTS: In this paper, we present a novel approach to the selection of differentially expressed genes in which test statistics are learned from data using a simple notion of reproducibility in selection results as the learning criterion. Reproducibility, as we define it, can be computed without any knowledge of the 'ground-truth', but takes advantage of certain properties of microarray data to provide an asymptotically valid guide to expected loss under the true data-generating distribution. We are therefore able to indirectly minimize expected loss, and obtain results substantially more robust than conventional methods. We apply our method to simulated and oligonucleotide array data. AVAILABILITY: By request to the corresponding author.

Algorithms↗

PubMed2005

A theoretical analysis of the selection of differentially expressed genes.

A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ("gene selection"). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting differentially expressed genes, using a given ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to methods based on simulation, the approach presented here is computationally efficient and can be used to examine the behavior of gene selection algorithms under a wide variety of conditions, even when the number of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three widely-used gene selection methods.

Algorithms↗

PubMed2004

A theoretical analysis of gene selection.

A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ('gene selection'). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample-sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting relevant genes, using a given gene ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to empirical methods, the analysis can easily be used to examine the behaviour of gene selection algorithms under a wide variety of conditions, even when the numbers of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three well-known gene ranking functions.

Algorithms↗