The Latent Process Decomposition of cDNA Microarray Data Sets

cDNA Microarray Data Normalization

Normalization of cDNA microarray data.

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a wel...

Latent Process Decomposition Of High-Dimensional Count Data

Motivation: Next-generation sequencing (NGS) technologies have become the preferred way of exploring a genome. These data are high-dimensional discrete counts with correlated variables (e.g., genes). We present a novel latent factor model for high-dimensional count data, Latent Process Decomposition (LPD-C), that accounts for the correlations among genes and models the biological hypothesis tha...

Latent Process Decomposition of High-Dimensional Count Data By

Next-generation sequencing (NGS) technologies have become the preferred way of exploring a genome. These data are high-dimensional discrete counts with latent structure that, once revealed, will reduce the dimensions and will lead to the subsets of genes that are suitable for further exploration. Latent Process Decomposition of high-dimensional count data (LPD-C) is presented as a two stage app...

Fundamentals of cDNA microarray data analysis.

Microarray technology is a powerful approach for genomics research. The multi-step, data-intensive nature of this technology has created an unprecedented informatics and analytical challenge. It is important to understand the crucial steps that can affect the outcome of the analysis. In this review, we provide an overview of the contemporary trend on various main analysis steps in the microarra...