Latent Protein Trees

Details Latent Protein Trees Latent Protein Trees

2011-08-11

arxiv.org/abs/1108.2471v1

Statistics

Applications

28 pages, 9 figures, supplemental materials included

Scientific paper

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing are a collection of features (10's to 100's of thousands) and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore there is the potential for very complex and informative correlational structure inherent in this data. However, attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. These associations may be computed from hypothesis testing (with correction for multiple testing) or from various regression models. However, to date there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model the known correlation structure -- both at the feature level and at the protein level -- in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation structure in the data set in order to appropriately compress features into metaproteins and to estimate the correlation structure of those identified metaproteins. We demonstrate the effectiveness of the model in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.

Affiliated with

Also associated with

No associations

Rating

Latent Protein Trees does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Latent Protein Trees, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Latent Protein Trees will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-276673