molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church (2000) to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.

Background: Microarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy5 and Cy3 as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneChips ) and cDNA microarray data. Current normalization techniques are most often linear and therefore not capable of fully correcting for these effects.

Data from gene expression arrays are influenced by many experimental parameters that lead to variations not simply accessible by standard quantification methods. To compare measurements from gene expression array experiments, quantitative data are commonly normalised using reference genes or global normalisationmethodsbasedonmeanormedian values. These methods are based on the assumption that (i) selected reference genes are expressed at a standard level in all experiments or (ii) that mean or median signal of expression will give a quantitative reference for each individual experiment. We introduce here a new ranking diagram, with which we can show how the different normalisation methods compare, and how they are influenced by variations in measurements (noise) that occur in every experiment. Furthermore, we show that an upper trimmed mean provides a simple and robust method for normalisation of larger sets of experiments by comparative analysis.

Abstract: Acquisition of microarray data is prone to systematic errors. A correction, called normalisation, must be applied to the data before further analysis is performed. With many normalisation techniques published and in use, the best way of executing this correction remains an open question. In this study, a variety of single-slide normalisation techniques, and different parameter settings for these techniques, were compared over many replicated microarray experiments. Different normalisation techniques were assessed through the distribution of the standard deviation of replicates from one biological sample across different slides. It is shown that local normalisation outperformed global normalisation and that intensity-based ‘lowess’ outperformed trimmed mean and median normalisation techniques. Overall, the top performing normalisation technique was a print-tip-based lowess with zero robust iterations. Lastly, we validated this evaluation methodology by examining the ability to predict oestrogen receptorpositive and-negative breast cancer samples with data that had been normalised using different techniques.

The advento f DNAmicro32ys has facilitated a fundamental transitio fro gene scienceto genoe science. By perfo020mas - sively parallel experiments otho)8(2- o genes ato nce, scientists have, fo the first time, the capabilityo fo bserving theco()R0 relatioO23)V between genes under cotro)J8 experimental co000)-O2) Ho wever, the immense voQJ0 o f data being generated by micro2-O y experiments requires soes-R83JJ-O datapro cessingto match. Machine learning metho ds are particularly well suitedto this task, as the typeso f reasoQ8we want to make ab oDNAmicro2V - yso-QV invo( es extracting patterns fro the data, and disco vering meaningful ways to view these patterns. In this paper we demo38-O2J ho w machine learningmetho ds can be used to extract bio()J-O(8V significant insightsfro DNA microJVy gene expressio data. 1M otivation adven t of DNA microarrays has facilitated afun1U[b tal tranW29bz from gen scien] togen"" scien9U Ratherthan performin experimen ts an gatherin data for asin1O gen at a time, scien tistsn w have the ability to perform massively parallel experimen ts on ten ofthousanb ofgen9 atonUW The power of DNA microarrays doesnb1 however, lie solelyin experimen tale#cien[O they allow us to observe the complex relation1910 between gen22 an the e#ect certain conU]W9Wb have on an en tire gen]92[ expression profile. However, the power of DNA microarrays is a double-edged sword: tohanU] theenbOU]9 amoun t of data bein gen199[ by microarray experimen ts, we nU] sophisticated dataanb1092 techn1]"1 to match. More specifically, wenU2 to extract biologicallymeangical inan ts from the morass of DNA microarray data,an apply thisnsb1 gain1 kn wledge in ameanObzO" way. The types ofinW90"bzO[ scien tists wan t to extract from DNA microarray datacan be regarded aspattern or r...

High-density oligonucleotide arrays have become a valuable tool for high-throughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics.

m g;i m g;j ; g 2 G(i; j) ; (2) will be used. Let (q 1 ; : : : ; q m i;j ) be the ascendingly sorted list of these quotients. The idea is to regard each of the ratios q g 2 Q i;j as an estimate of the pairwise relative bias r i;j ; In general, any measure of central tendency of the values in Q i;j , including median, mean, trimmed mean and weighted mean, may yield a sensible estimate r i;j for r i;j . However, care must be taken whenever values are averaged: the arithmetic mean should be computed in the space of log ratios in order to keep the symmetry of up- and downregulation. In the following it is assumed that reasonable estimates r i;j of the true quotients r i;j are given for all pairs i; j 2 A of microarrays. Now the task is to determine estimates s a of the array-dependent multiplicative errors s a for all arrays a 2 A. With such values, the measurement values m g;a can be made mutually comparable between different arrays a by rescaling them accordingly via m g;a !

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© 2005 Zhao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

ProteinChip ® technology, a suite of analytical tools that includes retentate chromatography, on-chip protein characterization, and multivariate analysis, allows researchers to examine patterns of protein expression and modification. Based on the surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) approach (17), ProteinChip technology has been pioneered by researchers at Ciphergen Biosystems (Fremont, CA, USA), as well as by users of Ciphergen’s commercial embodiment of this technology, the ProteinChip Biomarker System. This report will begin with a background of the technology and describe its applications in clinical proteomics and will then conclude with a discussion of tools and strategies to mine the large amounts of data generated during the course of a typical clinical proteomics study. Because biological systems possess a large dynamic range of protein expression, proteomics techniques generally consist of