MOTIVATION: Pre-processing of SELDI-TOF mass spectrometry data is currently performed on a largel y ad hoc basis. This makes comparison of results from independent analyses troublesome and does not provide a framework for distinguishing different sources of variation in data. RESULTS: In this article, we consider the task of pooling a large number of single-shot spectra, a task commonly performed automatically by the instrument software. By viewing the underlying statistical problem as one of heteroscedastic linear regression, we provide a framework for introducing robust methods and for dealing with missing data resulting from a limited span of recordable intensity values provided by the instrument. Our framework provides an interpretation of currently used methods as a maximum-likelihood estimator and allows theoretical derivation of its variance. We observe that this variance depends crucially on the total number of ionic species, which can vary considerably between different pooled spectra. This variation in variance can potentially invalidate the results from naive methods of discrimination/classification and we outline appropriate data transformations. Introducing methods from robust statistics did not improve the standard errors of the pooled samples. Imputing missing values however-using the EM algorithm-had a notable effect on the result; for our data, the pooled height of peaks which were frequently truncated increased by up to 30%.

MOTIVATION: In recent years, a range of techniques for analysis and segmentation of array comparative genomic hybridization (aCGH) data have been proposed. For array designs in which clones are of unequal lengths, are unevenly spaced or overlap, the discrete-index view typically adopted by such methods may be questionable or improved. RESULTS: We describe a continuous-index hidden Markov model for aCGH data as well as a Monte Carlo EM algorithm to estimate its parameters. It is shown that for a dataset from the BT-474 cell line analysed on 32K BAC tiling microarrays, this model yields considerably better model fit in terms of lag-1 residual autocorrelations compared to a discrete-index HMM, and it is also shown how to use the model for e.g. estimation of change points on the base-pair scale and for estimation of conditional state probabilities across the genome. In addition, the model is applied to the Glioblastoma Multiforme data used in the comparative study by Lai et al. (Lai,W.R. et al. (2005) Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics, 21, 3763-3370.) giving result similar to theirs but with certain features highlighted in the continuous-index setting.

A time sequence is a discrete sequence of vahtes, e.g.temperature measurements, varying over time.Conventional indexes for time sequences are built on the time domain and cannot derd with inverse queries on a time sequence (i.e.computing the times when the values satis~some conditions).To process an inverse query the entire time sequence has to be scanned.This paper presents a dynamic indexing technique on the vahte domain for large time sequences which can be implemented using regular ordered indexing techniques (e.g.B-trees).Our index (termed ZP-irtdex) dramatically improves the query processing time of inverse queries compared to linear scanning.For periodic time sequences that have a limited range and precision on their value domain (most time sequences have this property), the W-index has an upper bound for insertion time and search time.

Presents a difference calculus for determining changes to rule conditions in an active DBMS. The calculus has been used for implementing an algorithm to efficiently monitor rules with complex conditions. The calculus is based on partial differencing of queries derived from rule conditions. For each rule condition, several partially differentiated queries are generated that each considers changes to a single base relation or view that the condition depends on. The calculus considers both insertions and deletions. The algorithm is optimized for deferred rule condition monitoring in transactions with few updates. The calculus allows us to optimize both space and time. Space optimization is achieved since the calculus and the algorithm does not presuppose materialization of monitored conditions to find its previous state. This is achieved by using a breadth-first, bottom-up propagation algorithm and by calculating previous states by doing a logical rollback. Time optimization is achieved through incremental evaluation techniques. The algorithm has been implemented and a performance study is presented at the end of the paper.