Abstract

The research of modern Chinese architectural history formally started in the mid-1980s and the first conference held in 1986 in Beijing marks the establishment of the field. Over the past 26 years, this emerging field has developed fast and steadily. As a result, thirteen biennial conferences have been held since 1986, and academic products of various forms with over ten million characters have been published. This article surveys the development of modern Chinese architectural history as a field of scholarly inquiry in China and outlines some of keystone events in the past 26 years. It also charts out how some key concepts of the field, such as timeline, geography and research approaches have been evolving over time. The article introduces some of the most significant studies in modern Chinese architectural history from the middle 1980s to the present.

Abstract

We present pipeComp ( https://github.com/plger/pipeComp ), a flexible R framework for pipeline comparison handling interactions between analysis steps and relying on multi-level evaluation metrics. We apply it to the benchmark of single-cell RNA-sequencing analysis pipelines using simulated and real datasets with known cell identities, covering common methods of filtering, doublet detection, normalization, feature selection, denoising, dimensionality reduction, and clustering. pipeComp can easily integrate any other step, tool, or evaluation metric, allowing extensible benchmarks and easy applications to other fields, as we demonstrate through a study of the impact of removal of unwanted variation on differential expression analysis. Document type: Article

Abstract

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0940-1) contains supplementary material, which is available to authorized users. Document type: Article