Microarray image analysis: background estimation using quantile and morphological filters

BACKGROUND: In a microarray experiment the difference in expression between genes on the same slide is up to 10(3 )fold or more. At low expression, even a small error in the estimate will have great influence on the final test and reference ratios. In addition to the true spot intensity the scanned signal consists of different kinds of noise referred to as background. In order to assess the true spot intensity background must be subtracted. The standard approach to estimate background intensities is to assume they are equal to the intensity levels between spots. In the literature, morphological opening is suggested to be one of the best methods for estimating background this way. RESULTS: This paper examines fundamental properties of rank and quantile filters, which include morphological filters at the extremes, with focus on their ability to estimate between-spot intensity levels. The bias and variance of these filter estimates are driven by the number of background pixels used and their distributions. A new rank-filter algorithm is implemented and compared to methods available in Spot by CSIRO and GenePix Pro by Axon Instruments. Spot's morphological opening has a mean bias between -47 and -248 compared to a bias between 2 and -2 for the rank filter and the variability of the morphological opening estimate is 3 times higher than for the rank filter. The mean bias of Spot's second method, morph.close.open, is between -5 and -16 and the variability is approximately the same as for morphological opening. The variability of GenePix Pro's region-based estimate is more than ten times higher than the variability of the rank-filter estimate and with slightly more bias. The large variability is because the size of the background window changes with spot size. To overcome this, a non-adaptive region-based method is implemented. Its bias and variability are comparable to that of the rank filter. CONCLUSION: The performance of more advanced rank filters is equal to the best region-based methods. However, in order to get unbiased estimates these filters have to be implemented with great care. The performance of morphological opening is in general poor with a substantial spatial-dependent bias

Low-level analysis of microarray data

This thesis consists of an extensive introduction followed by seven papers (A-F) on low-level analysis of microarray data. Focus is on calibration and normalization of observed data. The introduction gives a brief background of the microarray technology and its applications in order for anyone not familiar with the field to read the thesis. Formal definitions of calibration and normalization are given. Paper A illustrates a typical statistical analysis of microarray data with background correction, normalization, and identification of differentially expressed genes (among thousands of candidates). A small analysis on the final results for different number of replicates and different image analysis software is also given. Paper B introduces a novel way for displaying microarray data called the print-order plot, which displays data in the order the corresponding spots were printed to the array. Utilizing these, so called (microtiter-) plate effects are identified. Then, based on a simple variability measure for replicated spots across arrays, different normalization sequences are tested and evidence for the existence of plate effects are claimed. Paper C presents an object-oriented extension with transparent reference variables to the R language. It is provides the necessary foundation in order to implement the microarray analysis package described in Paper F. Paper D is on affine transformations of two-channel microarray data and their effects on the log-ratio log-intensity transform. Affine transformations, that is, the existence of channel biases, can explain commonly observed intensity-dependent effects in the log-ratios. In the light of the affine transformation, several normalization methods are revisited. At the end of the paper, a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis. Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the image-analysis methods introduce. Observed data strongly support this method. In addition, multiscan-calibrated data has an extended dynamical range and higher signal-to-noise levels. This is real-world evidence for the existence of affine transformations of microarray data. Paper F describes the aroma package – An R Object-oriented Microarray Analysis environment – implemented in R and that provides easy access to our and others low-level analysis methods. Paper G provides an calibration method for spotted microarrays with dilution series or spike-ins. The method is based on a heteroscedastic affine stochastic model. The parameter estimates are robust against model misspecification

Trust and Growth in the 1990s: A Robustness Analysis

We conduct an extensive robustness analysis of the relationship between trust and growth for a later time period (the 1990s) and with a bigger sample (63 countries) than previous studies. In addition to robustness tests that focus on model uncertainty, we use Least Trimmed Squares, a robust estimation technique, to identify outliers and investigate how they affect the results. We find that the trust-growth relationship is less robust with respect to empirical specification and to countries in the sample than previously claimed, and that outliers affect the results. Nevertheless trust seems quite important compared with many other growth-regression variables.trust; growth; robustness analysis; extreme bounds analysis; social capital; least trimmed squares; outliers

The R.oo package – object-oriented programming with references using standard R code

An easy to install and platform independent package named R.oo, which provides support for references and mutable objects via a specific class model using standard R code, has been developed. The root class Object implements and encapsulates all the mechanisms needed for references in a way such that object fields are accessed similarly to how elements of a list are accessed with the important difference that the fields can be reassigned within methods. The class model also provides an easy way for defining classes that inherit directly or indirectly from the Object class. Any instance of a class that inherits from the Object class can be passed to functions by reference. Supplementary utility functions for defining constructors and methods in a simple and robust way are also made available. For instance, generic functions are created automatically and if non-generic functions with the same name already exist, they are, if possible, modified to become default functions. Currently, S3 classes and S3 methods are defined, but future versions of the package are likely to support S4 too. We also suggest an R coding convention, which the utility functions test against, with the intention to bring additional structure to the source code. The package also extends the current exception handling mechanism in R such that exception objects can be caught based on their class. The R.oo package has successfully been used in a medium-size microarray project

Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method

BACKGROUND: Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general. RESULTS: A methodological study of affine models for gene expression data is carried out. Focus is on two-channel comparative studies, but the findings generalize also to single- and multi-channel data. The discussion applies to spotted as well as in-situ synthesized microarray data. Existing normalization methods such as curve-fit ("lowess") normalization, parallel and perpendicular translation normalization, and quantile normalization, but also dye-swap normalization are revisited in the light of the affine model and their strengths and weaknesses are investigated in this context. As a direct result from this study, we propose a robust non-parametric multi-dimensional affine normalization method, which can be applied to any number of microarrays with any number of channels either individually or all at once. A high-quality cDNA microarray data set with spike-in controls is used to demonstrate the power of the affine model and the proposed normalization method. CONCLUSION: We find that an affine model can explain non-linear intensity-dependent systematic effects in observed log-ratios. Affine normalization removes such artifacts for non-differentially expressed genes and assures that symmetry between negative and positive log-ratios is obtained, which is fundamental when identifying differentially expressed genes. In addition, affine normalization makes the empirical distributions in different channels more equal, which is the purpose of quantile normalization, and may also explain why dye-swap normalization works or fails. All methods are made available in the aroma package, which is a platform-independent package for R

Stimulation within the cuneate nucleus suppresses synaptic activation of climbing fibers.

Several lines of research have shown that the excitability of the inferior olive is suppressed during different phases of movement. A number of different structures like the cerebral cortex, the red nucleus, and the cerebellum have been suggested as candidate structures for mediating this gating. The inhibition of the responses of the inferior olivary neurons from the red nucleus has been studied extensively and anatomical studies have found specific areas within the cuneate nucleus to be target areas for projections from the magnocellular red nucleus. In addition, GABA-ergic cells projecting from the cuneate nucleus to the inferior olive have been found. We therefore tested if direct stimulation of the cuneate nucleus had inhibitory effects on a climbing fiber field response, evoked by electrical stimulation of the pyramidal tract, recorded on the surface of the cerebellum. When the pyramidal tract stimulation was preceded by weak electrical stimulation (5-20 μA) within the cuneate nucleus, the amplitude of the climbing fiber field potential was strongly suppressed (approx. 90% reduction). The time course of this suppression was similar to that found after red nucleus stimulation, with a peak suppression occurring at 70 ms after the cuneate stimulation. Application of CNQX (6-cyano-7-nitroquinoxaline-2,3-dione, disodium salt) on the cuneate nucleus blocked the suppression almost completely. We conclude that a relay through the cuneate nucleus is a possible pathway for movement-related suppression of climbing fiber excitability

Metallhantverket i Uppåkra ? en studie av ett hantverksrelaterat material

The aim for this essay is to identify and distinguish the metal casting of Uppåkra during late Iron Age by examine related material that primarily consist of finds made with a metal detector. I intend to, by analysing maps, concentrations in the detector material and probable constructions within the settlement, find patterns that can indicate a metal casting activity. The results of the essay show that a number of concentrations can be seen and that they both indicate metalworking activities and to some degree a structural specialization in the settlement. The exclusive artefacts also indicate that Uppåkra was a settlement with over-regional functions when it comes to traditions dealing with metal casting

Local ethnic composition and Natives’ and Immigrants’ geographic mobility in France, 1982-1999

This article provides empirical results on patterns of native and immigrant geographic mobility in France. Using longitudinal data, we measure mobility from one French municipality (commune) to another over time and estimate the effect of the initial municipality’s ethnic composition on the probability of moving out. These data allow us to use panel techniques to correct for biases related to selection based on geographic and individual unobservables. Our findings tend to discredit the hypothesis of a “white flight” pattern in residential mobility dynamics in France. Some evidence does show ethnic avoidance mechanisms in natives’ relocating. We also find a strong negative and highly robust effect of co-ethnics’ presence on immigrants’ geographic mobility