Biomedical subjects

Dilip Rajagopalan

Publications and source records attributed to Dilip Rajagopalan.

3 recordsLinked to original sources

PubMed2006

A comparison of statistical tests for detecting differential expression using Affymetrix oligonucleotide microarrays.

Signal quantification and detection of differential expression are critical steps in the analysis of Affymetrix microarray data. Many methods have been proposed in the literature for each of these steps. The goal of this paper is to evaluate several signal quantification methods (GCRMA, RSVD, VSN, MAS5, and Resolver) and statistical methods for differential expression (t test, Cyber-T, SAM, LPE, RankProducts, Resolver RatioBuild). Our particular focus is on the ability to detect differential expression via statistical tests. We have used two different datasets for our evaluation. First, we have used the HG-U133 Latin Square spike in dataset developed by Affymetrix. Second, we have used data from an in-house rat liver transcriptomics study following 30 different drug treatments generated using the Affymetrix RAE230A chip. Our overall recommendation based on this study is to use GCRMA for signal quantification. For detection of differential expression, GCRMA coupled with Cyber-T or SAM is the best approach, as measured by area under the receiver operating characteristic (ROC) curve. The integrated pipeline in Resolver RatioBuild combining signal quantification and detection of differential expression is an equally good alternative for detecting differentially expressed genes. For most of the differential expression algorithms we considered, the performance using MAS5 signal quantification was inferior to that of the other methods we evaluated.

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PubMed2004

Inferring pathways from gene lists using a literature-derived network of biological relationships.

MOTIVATION: A number of omic technologies such as transcriptional profiling, proteomics, literature searches, genetic association, etc. help in the identification of sets of important genes. A subset of these genes may act in a coordinated manner, possibly because they are part of the same biological pathway. Interpreting such gene lists and relating them to pathways is a challenging task. Databases of biological relationships between thousands of mammalian genes can help in deciphering omics data. The relationships between genes can be assembled into a biological network with each protein as a node and each relationship as an edge between two proteins (or nodes). This network may then be searched for subnetworks consisting largely of interesting genes from the omics experiment. The subset of genes in the subnetwork along with the web of relationships between them helps to decipher the underlying pathways. Finding such subnetworks that maximally include all proteins from the query set but few others is the focus for this paper. RESULTS: We present a heuristic algorithm and a scoring function that work well both on simulated data and on data from known pathways. The scoring function is an extension of a previous study for a single biological experiment. We use a simple set of heuristics that provide a more efficient solution than the simulated annealing method. We find that our method works on reasonably complex curated networks containing approximately 9000 biological entities (genes and metabolites), and approximately 30,000 biological relationships. We also show that our method can pick up a pathway signal from a query list including a moderate number of genes unrelated to the pathway. In addition, we quantify the sensitivity and specificity of the technique.

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PubMed2003

A comparison of statistical methods for analysis of high density oligonucleotide array data.

MOTIVATION: Gene expression profiling has become an invaluable tool in functional genomics. A wide variety of statistical methods have been employed to analyze the data generated in experiments using Affymetrix GeneChip microarrays. It is important to understand the relative performance of these methods in terms of accuracy in detecting and quantifying relative gene expression levels and changes in gene expression. RESULTS: Three different analysis approaches have been compared in this work: non-parametric statistical methods implemented in Affymetrix Microarray Analysis Suite v5.0 (MAS5); an error-modeling based approach implemented in Rosetta Resolver v3.1; and an intensity-modeling approach implemented in dChip v1.1. A Latin Square data set generated and made available by Affymetrix was used in the comparison. All three methods-Resolver, MAS5 and the version of dChip based on the difference between perfect match and mismatch intensities-perform well in quantifying gene expression. Presence calls made by MAS5 and Resolver perform well at high concentrations, but they cannot be relied upon at low concentrations. The performance of Resolver and MAS5 in detecting 2-fold changes in transcript concentration is superior to that of dChip. At a comparable false positive rate, Resolver and MAS5 are able to detect many more true changes in transcript concentration. Estimated fold changes calculated by all the methods are biased below the true values.

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