A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities

Cardiac fibroblast sub-types in vitro reflect pathological cardiac remodeling in vivo

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A panel of 12 fibrosis related genes clearly identified heart failure (HF) patients better than measurement of the collagen-related hydroxyproline content.

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A subcluster enriched for ischemic HF was recognized, but not for diabetes, obesity, or gender.

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Single-cell transcriptomic analysis of in vitro differentiated mouse cardiac fibroblasts distinguished 6 subpopulations, including a contractile Acta2high precursor population, and Acta2low subpopulations with high production of extracellular matrix molecules.

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The 12 gene profile identified in HF patients showed highest similarity to the fibroblast subset with the strongest expression of extracellular matrix molecules.

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Major features of cardiac fibroblast differentiation in heart failure patients, in murine heart disease models, and in cell culture of primary murine cardiac fibroblast are shared.

Many heart diseases are associated with fibrosis, but it is unclear whether different types of heart disease correlate with different subtypes of activated fibroblasts and to which extent such diversity is modeled during in vitro activation of primary cardiac fibroblasts. Analyzing the expression of 82 fibrosis related genes in 65 heart failure (HF) patients, we identified a panel of 12 genes clearly distinguishing HF patients better from healthy controls than measurement of the collagen-related hydroxyproline content. A subcluster enriched in ischemic HF was recognized, but not for diabetes, high BMI, or gender. Single-cell transcriptomic analysis of in vitro activated mouse cardiac fibroblasts distinguished 6 subpopulations, including a contractile Acta2^high precursor population, which was predicted by time trajectory analysis to develop into Acta2^low subpopulations with high production of extracellular matrix molecules. The 12 gene profile identified in HF patients showed highest similarity to the fibroblast subset with the strongest expression of extracellular matrix molecules. Population markers identified were furthermore able to clearly cluster different disease stages in a murine model for myocardial infarct. These data suggest that major features of cardiac fibroblast activation in heart failure patients, in murine heart disease models, and in cell culture of primary murine cardiac fibroblast are shared.

A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications

Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.

Algebraic comparison of partial lists in bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or to a meta-analysis comparison, it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained, instead of just one list. Here we introduce a method, based on permutations, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated by finding and comparing gene profiles on a large prostate cancer dataset, consisting of two cohorts of patients from different countries, for a total of 455 samples.