Supplementary MaterialsS1 Table: Input datasets used in this study. define the

Supplementary MaterialsS1 Table: Input datasets used in this study. define the as the CSF vector across all individuals (Fig 1A, right). Open in a separate window Fig 1 Overview of the CCCE method.(A) The input data, consisting of cell subpopulation signatures shown across the cell subfunctions (left), and the CSF traits of each subpopulation across dizygotic and monozygotic twins (right). (B) The pre-processing step, presenting the common environment effects for each cell subpopulations, calculated using the Falconers formula. (C) CCCE step 1 1. Regression of the common environmental effects using the cell functions as predictors. (D) CCCE step 2 2. A plot AB1010 novel inhibtior of the distribution of permutation-based prediction errors compared to the actual prediction error, providing a statistical significance score. (E) The leading subfunction. Shown are the resulting regression coefficients of each subfunction, highlighting the leading subfunction. Abbreviation: c2the common, non-age-related, environmental effect. Overall, the CCCE input dataset is a collection of 2different CSF traits measured using a certain reflects the existence of one particular protein on the cell surface of a given cell type, regardless of the combination with any other cell surface protein (Fig 1A, left). Throughout this study we therefore distinguish between two interrelated terms: whereas a cell subpopulation refers to a group of cells carrying the same combination of protein markers, a cell subfunction refers to the functionality of a single protein, which may appear in many different cell subpopulations. Overview of CCCE The CCCE input is a single dataset consisting of a collection of CSF traits for a AB1010 novel inhibtior single cell type (that is, a single flow cytometry panel) across the individuals participating in the study (all monozygotic and dizygotic twins). Each of the traits is accompanied by its corresponding signature of cell subfunctions (Fig 1A). Given these inputs, the algorithm aims to identify common environmental effects on specific cell subfunctions. Our rationale is that calculations of common environmental effects on the frequencies of cell subfunctions may lead to false positive predictions due to confoundings related to imbalance in cell subpopulation frequencies. For example, assume a highly AB1010 novel inhibtior prevalent cell subpopulation A that carries a cell surface marker resides on the cell surface of several rare subpopulations in the same tissue. We consider a scenario in which the common environmental effect acts only on the frequency of subpopulation A and has no effect on any other subpopulation. Due to the high prevalence of type-A cells in the data, it may be erroneously determined that the common environmental effect acts on the presence of marker (subfunction x) rather than on the cell subpopulation A. To discriminate between these possibilities, CCCE evaluates the relations between the common environment and cell subfunctions while eliminating potential biases due to subpopulation-specific evidence. In particular, CCCE first utilizes AB1010 novel inhibtior standard methods to calculate the common environmental effect for each cell subpopulation (Fig 1B). Next, CCCE aims to assess the ability of the various cell subfunctions to predict the common environmental effect, using a regularized regression framework and assuming an unbiased evidence from the different cell subpopulations (Fig 1C). Using permutations, CCCE determines the statistical significance of the relation between the immune subfunctions and the common environmental effects (a = ? = = ? = ? ? is the traits correlation between the monozygotic twins, and is the traits correlation between the dizygotic twins. The Falconer formula thus allows evaluation of the common environment AB1010 novel inhibtior effect solely based on phenotypic variation in dizygotic and monozygotic twins, without requiring direct environmental measurements. CCCE assumes a single common environmental effect acting on each of the cell subpopulations. The common environmental effects were calculated using the Falconer formula as described in Roederer et al. [8] (Fig 1B). Briefly, the calculation involves two steps: first, evaluating the age effect, estimated based on a linear least-squares fit of the CSF trait value by age, and then adjusting the trait for the confounding effect of individual age. Second, for each non-age-related CSF trait, the Falconers formula calculates the genetic and environmental effects. We hereby denote by c2 the common, non-age-related, environmental effect (c2 values were downloaded from [8]). Step 1 1: Identify common environment effects on cell functions CCCE aims to calculate the extent to which the cell subfunctions are related to the heterogeneity of the common environmental, non-age-related effects UVO (c2) across cell subpopulations. Naively, this can be done by calculating the c2 values for single-marker subpopulations (as if the panel consists of only one.