Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation.

10 11 12 Accurate and comprehensive extraction of information from high-dimensional single cell 13 datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art 14 algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to 15 produce clear representations of datasets when millions of cells are projected. We developed 16 opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Liebler 17 divergence evaluation in real time to tailor the early exaggeration and overall number of 18 gradient descent iterations in a dataset-specific manner. The precise calibration of early 19 exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically 20 improves computation time and enables high-quality visualization of large cytometry and 21 transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters 22

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BackgroundIn symbiotic legume nodules, endosymbiotic rhizobia (bacteroids) fix atmospheric N2, an ATP-dependent catalytic process yielding stoichiometric ammonium and hydrogen gas (H2). While in most legume nodules this H2 is quantitatively evolved, which loss drains metabolic energy, certain bacteroid strains employ uptake hydrogenase activity and thus evolve little or no H2. Rather, endogenous H2 is efficiently respired at the expense of O2, driving oxidative phosphorylation, recouping ATP used for H2 production, and increasing the efficiency of symbiotic nodule N2 fixation. In many ensuing investigations since its discovery as a physiological process, bacteroid uptake hydrogenase activity has been presumed a single entity.Methodology/Principal Findings Azorhizobium caulinodans, the nodule endosymbiont of Sesbania rostrata stems and roots, possesses both orthodox respiratory (exo-)hydrogenase and novel (endo-)hydrogenase activities. These two respiratory hydrogenases are structurally quite distinct and encoded by disparate, unlinked gene-sets. As shown here, in S. rostrata symbiotic nodules, haploid A. caulinodans bacteroids carrying single knockout alleles in either exo- or-endo-hydrogenase structural genes, like the wild-type parent, evolve no detectable H2 and thus are fully competent for endogenous H2 recycling. Whereas, nodules formed with A. caulinodans exo-, endo-hydrogenase double-mutants evolve endogenous H2 quantitatively and thus suffer complete loss of H2 recycling capability. More generally, from bioinformatic analyses, diazotrophic microaerophiles, including rhizobia, which respire H2 may carry both exo- and endo-hydrogenase gene-sets.Conclusions/SignificanceIn symbiotic S. rostrata nodules, A. caulinodans bacteroids can use either respiratory hydrogenase to recycle endogenous H2 produced by N2 fixation. Thus, H2 recycling by symbiotic legume nodules may involve multiple respiratory hydrogenases.