Adjustment of systematic microarray data biases

Adjustment of systematic microarray data biases

MOTIVATION Systematic differences due to experimental features of microarray experiments are present in most large microarray data sets. Many different experimental features can cause biases including different sources of RNA, different production lots of microarrays or different microarray platforms. These systematic effects present a substantial hurdle to the analysis of microarray data. RE...

cDNA Microarray Data Normalization

Generalized Adjustment Under Confounding and Selection Biases

Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditi...

Microarray Data Analysis Toolbox (MDAT): for normalization, adjustment and analysis of gene expression data

SUMMARY We introduce a novel Matlab toolbox for microarray data analysis. This toolbox uses normalization based upon a normally distributed background and differential gene expression based on five statistical measures. The objects in this toolbox are open source and can be implemented to suit your application. AVAILABILITY MDAT v1.0 is a Matlab toolbox and requires Matlab to run. MDAT is fre...

Improved biclustering of microarray data demonstrated through systematic performance tests

A new algorithm is presented for 4tting the plaid model, a biclustering method developed for clustering gene expression data. The approach is based on speedy individual di6erences clustering and uses binary least squares to update the cluster membership parameters, making use of the binary constraints on these parameters and simplifying the other parameter updates. The performance of both algor...