Engineering topics

Karpievitch, Yuliya

Publications and source records attributed to Karpievitch, Yuliya.

3 records

DOE OSTI2012

Normalization and missing value imputation for label-free LC-MS analysis

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2012

DanteR: an extensible R-based tool for quantitative analysis of -omics data

Motivation: The size and complex nature of LC-MS proteomics data sets motivates development of specialized software for statistical data analysis and exploration. We present DanteR, a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab. Availability: DanteR and its associated user guide are available for download at http://omics.pnl.gov/software/. For Windows, a single click automatically installs DanteR along with the R programming environment. For Linux and Mac OS X, users must first install R and then follow instructions on the DanteR web site for package installation.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2012

Label-Free Quantitative LC–MS Proteomics of Alzheimer’s Disease and Normally Aged Human Brains

uantitative proteomics analysis of cortical samples of 10 Alzheimer’s disease (AD) brains versus 10 normally aged brains was performed by following the accurate mass and time tag (AMT) approach with the high resolution LTQ Orbitrap mass spectrometer. More than 1400 proteins were identified and quantitated. A conservative approach of selecting only the consensus results of four normalization methods was suggested and used. A total of 197 proteins were shown to be significantly differentially abundant (p-values <0.05, corrected for multiplicity of testing) in AD versus control brain samples. Thirty-seven of these proteins were reported as differentially abundant or modified in AD in previous proteomics and transcriptomics publications. The rest to the best of our knowledge are new. Mapping of the discovered proteins with bioinformatic tools revealed significant enrichment with differentially abundant proteins of pathways and processes known to be important in AD, including signal transduction, regulation of protein phosphorylation, immune response, cytoskeleton organization, lipid metabolism, energy production, and cell death.

Alzheimer’s disease, brain, cortical samples, prot↗