Variability in the analysis of a single neuroimaging dataset by many teams, bioRxiv, 2019-11-16

SummaryData analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed.

biorxiv neuroscience 500+-users 2019

Genomics of a complete butterfly continent, bioRxiv, 2019-11-05

Never before have we had the luxury of choosing a continent, picking a large phylogenetic group of animals, and obtaining genomic data for its every species. Here, we sequence all 845 species of butterflies recorded from North America north of Mexico. Our comprehensive approach reveals the pattern of diversification and adaptation occurring in this phylogenetic lineage as it has spread over the continent, which cannot be seen on a sample of selected species. We observe bursts of diversification that generated taxonomic ranks subfamily, tribe, subtribe, genus, and species. The older burst around 70 Mya resulted in the butterfly subfamilies, with the major evolutionary inventions being unique phenotypic traits shaped by high positive selection and gene duplications. The recent burst around 5 Mya is caused by explosive radiation in diverse butterfly groups associated with diversification in transcription and mRNA regulation, morphogenesis, and mate selection. Rapid radiation correlates with more frequent introgression of speciation-promoting and beneficial genes among radiating species. Radiation and extinction patterns over the last 100 million years suggest the following general model of animal evolution. A population spreads over the land, adapts to various conditions through mutations, and diversifies into several species. Occasional hybridization between these species results in accumulation of beneficial alleles in one, which eventually survives, while others become extinct. Not only butterflies, but also the hominids may have followed this path.

biorxiv genomics 500+-users 2019

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis, bioRxiv, 2019-10-26

AbstractHere we use deep transfer learning to quantify histopathological patterns across 17,396 H&amp;E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 2527 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.Key points<jatslist list-type=bullet><jatslist-item>Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types<jatslist-item><jatslist-item>Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations<jatslist-item><jatslist-item>Wide-spread correlations with gene expression indicative of immune infiltration and proliferation<jatslist-item><jatslist-item>Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers<jatslist-item>

biorxiv bioinformatics 500+-users 2019

Insights from a survey-based analysis of the academic job market, bioRxiv, 2019-10-09

AbstractApplying for a faculty position is a critical phase of many postdoctoral careers, but most postdoctoral researchers in STEM fields enter the academic job market with little knowledge of the process and expectations. A lack of data has made it difficult for applicants to assess their qualifications relative to the general applicant pool and for institutions to develop effective hiring policies. We analyzed responses to a survey of faculty job applicants between May 2018 and May 2019. We establish various background scholarly metrics for a typical faculty applicant and present an analysis of the interplay between those metrics and hiring outcomes. Traditional benchmarks of a positive research track record above a certain threshold of qualifications were unable to completely differentiate applicants with and without offers. Our findings suggest that there is no single clear path to a faculty job offer and that metrics such as career transition awards and publications in high impact factor journals were neither necessary nor sufficient for landing a faculty position. The applicants perceived the process as unnecessarily stressful, time-consuming, and largely lacking in feedback, irrespective of a successful outcome. Our findings emphasize the need to improve the transparency of the faculty job application process. In addition, we hope these and future data will help empower trainees to enter the academic job market with clearer expectations and improved confidence.

biorxiv scientific-communication-and-education 500+-users 2019

 

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