Influence of environmental variability and Emiliania huxleyi ecotypes on alkenone-derived temperature reconstructions in the subantarctic Southern Ocean.

Long-chain unsaturated alkenones produced by haptophyte algae are widely used as paleotemperature indicators. The unsaturation relationship to temperature is linear at mid-latitudes, however, non-linear responses detected in subpolar regions of both hemispheres have suggested complicating factors in these environments. To assess the influence of biotic and abiotic factors in alkenone production and preservation in the Subantarctic Zone, alkenone fluxes were quantified in three vertically-moored sediment traps deployed at the SOTS observatory (140°E, 47°S) during a year. Alkenone fluxes were compared with coccolithophore assemblages, satellite measurements and surface-water properties obtained by sensors at SOTS. Alkenone-based temperature reconstructions generally mirrored the seasonal variations of SSTs, except for late winter when significant deviations were observed (3-10 °C). Annual flux-weighted averages in the 3800 m trap returned alkenone-derived temperatures ~1.5 °C warmer than those derived from the 1000 m trap, a distortion attributed to surface production and signal preservation during its transit through the water column. Notably, changes in the relative abundance of E. huxleyi var. huxleyi were positively correlated with temperature deviations between the alkenone-derived temperatures and in situ SSTs (r = 0.6 and 0.7 at 1000 and 2000 m, respectively), while E. huxleyi var. aurorae, displayed an opposite trend. Our results suggest that E. huxleyi var. aurorae produces a higher proportion of C37:3 relative to C37:2 compared to its counterparts. Therefore, the dominance of var. aurorae south of the Subtropical Front could be at least partially responsible for the less accurate alkenone-based SST reconstructions in the Southern Ocean using global calibrations. However, the observed correlations were largely influenced by the samples collected during winter, a period characterized by low particle fluxes and slow sinking rates. Thus, it is likely that other factors such as selective degradation of the most unsaturated alkenones could also account for the deviations of the alkenone paleothermometer.

DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling.

MOTIVATION: Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation. RESULTS: DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor-response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification. AVAILABILITY AND IMPLEMENTATION: DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.