rapmad: Robust analysis of peptide microarray data

cDNA Microarray Data Normalization

Robust singular value decomposition analysis of microarray data.

In microarray data there are a number of biological samples, each assessed for the level of gene expression for a typically large number of genes. There is a need to examine these data with statistical techniques to help discern possible patterns in the data. Our technique applies a combination of mathematical and statistical methods to progressively take the data set apart so that different as...

Inferential, robust non-negative matrix factorization analysis of microarray data

MOTIVATION Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory; even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Correlations among predictors are used to improve the statistical analysis....

Robust Processing of Microarray Data by Independent Component Analysis

Microarray Data Processing is becoming a field of important activity for Signal Processing and Pattern Recognition areas, as the extraction and mining of meaningful data from large groupings of microarray patterns is of vital importance in Medicine, Genomics, Proteomics, Pharmacology, etc. In this paper emphasis is placed on studying and cataloging the nature of possible sources of corruption o...

Biological assessment of robust noise models in microarray data analysis

MOTIVATION Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus...