A statistical framework for protein quantitation in bottom-up MS-based proteomics

Abstract

The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).

P1–206: Quantitative label‐free proteomics for discovery of biomarkers in cerebrospinal fluid: Assessment of technical and inter‐individual variation

“…The peptides were grouped as individual genes (Table S2) (Step 9). The gene-grouped peptide intensity data were imported into DAnTE-R for statistical analysis [53,54] Table S6. (TIF) Figure S6 Subject variance for each of 81 proteins, calculated using only the two most abundant peptides.…”

“…Fortunately, statistical approaches have been developed to address this problem and are available in the open source proteomics platform DAnTE (http://omics.pnnl.gov/software/) [54]. As demonstrated by this study, when DAnTE is applied to multiple replicates of a pooled sample, quantification at the protein level is highly reproducible (CV,5% for most of the proteins analyzed).…”

“…The gene-grouped and peptide intensity data were imported into DAnTE-R for statistical analysis [53,54]. Only proteins represented by 2 or more annotated peptides were considered for subsequent data analyses (all annotated peptides are reported in Table S2).…”

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens

“…Several statistical methods accommodate missing data and have been applied to MS data including accelerated failure time models (Tekwe et al, 2012), two-part models (Taylor and Pollard, 2009) and mixture models (Karpievitch et al, 2009;Taylor et al, 2013). The assumptions about the missing value mechanisms differ among these methods and as a consequence, missing values are modeled in different manners.…”

“…These models jointly test for a difference in the proportion of missing values and a difference in the means of the continuous components (Lachenbruch, 2001). Mixture models combine elements from survival analysis and two-part models by modeling missing values as a combination of censored values and the absence of a compound represented as a point-mass at 0 (Karpievitch et al, 2009;Taylor et al, 2013). These methods have only been used for single biospecimen analyses and few methods suitable for multivariate analysis of data with missing values have been developed.…”