MOTIVATION: The reverse-phase protein lysate arrays have been used to quantify the relative expression levels of a protein in a number of cellular samples simultaneously. To avoid quantification bias due to mis-specification of commonly used parametric models, a nonparametric approach based on monotone response curves may be used. The existing methods, however, aggregate the protein concentration levels of replicates of each sample, and therefore fail to account for within-sample variability. RESULTS: We propose a method of regularization on protein concentration estimation at the level of individual dilution series to account for within-sample or within-group variability. We use an efficient algorithm to optimize an approximate objective function, with a data-adaptive approach to choose the level of shrinkage. Simulation results show that the proposed method quantifies protein concentration levels well. We show through the analysis of protein lysate array data from cell lines of different cancer groups that accounting for within-sample variability leads to better statistical analysis. AVAILABILITY: Code written in statistical programming language R is available at: http://odin.mdacc.tmc.edu/~jhhu/Reno

The differentiation of embryonic stem cells is initiated by a gradual loss of pluripotency-associated transcripts and induction of differentiation genes. Accordingly, the detection of differentially expressed genes at the early stages of differentiation could assist the identification of the causal genes that either promote or inhibit differentiation. The previous methods of identifying differentially expressed genes by comparing different cell types would inevitably include a large portion of genes that respond to, rather than regulate, the differentiation process. We demonstrate through the use of biological replicates and a novel statistical approach that the gene expression data obtained without prior separation of cell types are informative for detecting differentially expressed genes at the early stages of differentiation. Applying the proposed method to analyze the differentiation of murine embryonic stem cells, we identified and then experimentally verified Smarcad1 as a novel regulator of pluripotency and self-renewal. We formalized this statistical approach as a statistical test that is generally applicable to analyze other differentiation processes.

MOTIVATION: Proteins play a crucial role in biological activity, so much can be learned from measuring protein expression and post-translational modification quantitatively. The reverse-phase protein lysate arrays allow us to quantify the relative expression levels of a protein in many different cellular samples simultaneously. Existing approaches to quantify protein arrays use parametric response curves fit to dilution series data. The results can be biased when the parametric function does not fit the data. RESULTS: We propose a non-parametric approach which adapts to any monotone response curve. The non-parametric approach is shown to be promising via both simulation and real data studies; it reduces the bias due to model misspecification and protects against outliers in the data. The non-parametric approach enables more reliable quantification of protein lysate arrays. AVAILABILITY: Code to implement the proposed method in the statistical package R is available at: http://odin.mdacc.tmc.edu/jhu/lysatearray-analysis/

A critical component of spline smoothing is the choice of knots, especially for curves with varying shapes and frequencies in its domain. We consider a two-stage knot selection scheme for adaptively fitting splines to data subject to noise. A potential set of knots is chosen based on information from certain wavelet decompositions with the intention of placing more points where the curve shows rapid changes. The final knot selection is then made based on statistical model selection ideas. We show that the proposed method is well suited for a variety of smoothing and noise filtering needs.