0000000000017484
AUTHOR
Fede C
A Statistical Calibration Method based on Non-Linear Mixed Model for Affymetrix Probe Level Data
Gene expression microarrays allow a researcher to measure the simultaneous response of thousands of genes to external conditions. Affymetrix GeneChipr expression array technology has become a standard tool in medical research. Anyway, a preprocessing step is usually necessary in order to obtain a gene expression measure. Aim of this paper is to propose a calibration method to estimate the nominal concentration based on a non-linear mixed model. This method is an enhancement of a method proposed in Mineo et al. (2006). The relationship between raw intensities and concentration is obtained by using the Langmuir isotherm theory.
A new proposal for microarray background correction by means of a GLMM
La tecnologia microarray ha il grosso pregio di misurare simultaneamente il livello di espressione di migliaia di geni. All’elevata quantità d’informazione fornita da un singolo chip si contrappone la necessità di un adeguato pretrattamento dei dati grezzi al fine di ottenere una misura “affidabile” del livello di espressione genetico. Scopo del lavoro è analizzare, attraverso un modello lineare generalizzato misto, il legame esistente fra il livello d’intensità osservato ed il livello di concentrazione, attraverso l’utilizzo degli esperimenti Spike-In forniti dall’Affymetrix. Si propone, quindi, un nuovo metodo per la correzione del background.
Modelling the background correction in microarray data analysis
Microarray technology has been adopted in many areas of biomedical research for quantitative and highly parallel measurements of gene expressions. In this field, the high density oligonucleotide microarray technology is the most used platform; in this platform oligonucleotides of 25 base pairs are used as probe genes. Two types of probes are considered: perfect match (PM) and mismatch (MM) probes. In theory, MM probes are used to quantify and remove two types of error: optical noise and non specific binding. The correction of these two types of error is known as background correction. Preprocessing is an essential step of the analysis in which the intensity, read from each probe, is manipul…
Prediction of the gene expression measure by means of a GLMM
Microarrays permit to scientists the screening of thousands of genes simultaneously to determine, for example, whether those genes are active, hyperactive or silent in normal or cancerous tissues. A primary task in microarray analysis is to obtain a good measure of the gene expression that can be used for a so called higher level analysis. Different methods have been proposed for high density oligonucleotide arrays (see Cope et al. (2004) for a review). Aim of this paper is to obtain a new gene expression measure based on the background correction model proposed by Mineo et al. (2006). The proposed method is validated by means of a free available data-set called Spike-In133 experiment, wher…