Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between 'self-contained' versus 'competitive' methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of 'core members' that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.
Efficient single-cell assignment is essential for single-cell sequencing data analysis. With the explosive growth of single-cell sequencing data, multiple single-cell sequencing data sources are available for the same kind of tissue, which can be integrated to further improve single-cell assignment; however, an efficient integration strategy is still lacking due to the great challenges of data heterogeneity existing in multiple references. To this end, we present mtSC, a flexible single-cell assignment framework that integrates multiple references based on multitask deep metric learning designed specifically for cell type identification within tissues with multiple single-cell sequencing data as references. We evaluated mtSC on a comprehensive set of publicly available benchmark datasets and demonstrated its state-of-the-art effectiveness for integrative single-cell assignment with multiple references.
Multiferroic materials with flexibility are expected to make great contributions to flexible electronic applications, such as sensors, memories, and wearable devices. In this work, super-flexible freestanding BiMnO 3 membranes with simultaneous ferroelectricity and ferromagnetism are synthesized using water-soluble Sr 3 Al 2 O 6 as the sacrificial buffer layer. The super-flexibility of BiMnO 3 membranes is demonstrated by undergoing an ≈180°folding during an in situ bending test, which is consistent with the results of first-principles calculations. The piezoelectric signal under a bending radius of ≈500 μm confirms the stable existence of electric polarization in freestanding BiMnO 3 membranes. Moreover, the stable ferromagnetism of freestanding BiMnO 3 membranes is demonstrated after 100 times bending cycles with a bending radius of ≈2 mm. 5.1% uniaxial tensile strain is achieved in freestanding BiMnO 3 membranes, and the piezoresponse force microscopy (PFM) phase retention behaviors confirm that the ferroelectricity of membranes can survive stably up to the strain of 1.7%. These super-flexible membranes with stable ferroelectricity and ferromagnetism pave ways to the realizations of multifunctional flexible electronics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.