Cosmin Lazar

TL;DR: This survey focuses on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery, and presents them in a unified framework.

TL;DR: Practical guidelines are formulated regarding the choice and the application of an imputation method in a proteomics context and it is shown that a supposedly "under-performing" method, if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the 'appropriate' nature of missing values, can outperform a blindly applied, supposedly "better-performing' method.

TL;DR: Methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments are reviewed in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process.

TL;DR: DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments and contain procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate.

TL;DR: The newly released inSilicoMerging R/Bioconductor package allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets and enables researchers to fully explore the potential of combining gene expressionData for downstream analysis.