394 research outputs found
The Rice-Sprout Song: la danza macabra dei contadini cinesi
No full textThe peasants’ life, traditionally a remote peripheral reality for the Chinese cultural élites, and the highly marginalized propaganda literature converge in the novel by Zhang Ailing, a bilingual Shanghainese woman writer who occupies a hyper-canonical place in the Sinophone literary world. The novel, written in English and self-translated into Chinese, was commissioned by the United States Intelligence Service in the 1950s as part of its cultural activities aimed to prevent the spread of communism in Asia, and provides a realistic portrait of the miserable daily life of peasants under the Land Reform implemented by the new-born People’s Republic of China. While making a harsh criticism of Chinese communism, the author, faithful to her poetics, subverts the conventions of propaganda literature in a tale that unites the oppressed and the oppressors in a common destiny of desolation. Ignored by the us public for its non-conformity with the tones and clichés of the Red Scare, and marginalized in the writer’s production because of its propaganda origins, the novel bears witness to Zhang Ailing’s resistance to the pressure of the ideological clash that marks the Cold War era and reveals her refusal to being assimilated into the ghettoizing role of native informant, assigned to the Chinese intellectuals who took refuge in the us following the Communist Party’s takeover of China
Statistical methods for the analysis of spatial gene expression data
No full textThesis (Ph.D.)--University of Washington, 2022In recent years, there has been rapid development of spatial gene expression and spatial transcriptomics technologies, though corresponding advances in computational and statistical tools for the analysis of the data generated from these technologies have lagged. Initial approaches often neglected to consider differences between spatial transcriptomics and its predecessors, thus leading to analyses that may not fully realize the potential of spatial transcriptomics to generate biological insights. The overall aim of my dissertation research is to develop statistical methods for the analysis of spatial gene expression data. Specifically, I present new approaches for spatial clustering and resolution enhancement of spatial transcriptomics data as well as a joint model for spatial transcriptomics and single-cell RNA sequencing data
Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling
No full textThesis (Ph.D.)--University of Washington, 2016-06This dissertation describes a set of statistical methods developed for analysis of single cell gene expression. A characteristic of single cell expression is bimodal expression, in which two clusters of expression are present. In any given transcript, the null cluster corresponds to cells without detectable expression (hence a non-zero measurement reflects measurement error) while the signal cluster contains cells with a positive, detectable level of expression. Statistical models that accommodate this characteristic are considered. • In Chapter 1, motivation and history of single cell gene expression is considered. Scientific and statistical questions addressable through single cell expression are discussed, and some statistical frameworks for bulk and single cell expression are described. • In Chapter 2, I consider data generated from replicates of single cells and 100 cell aggregates that were assayed through single cell reverse-transcriptase qPCR (rt-qPCR). In rt-qPCR the null cluster manifests as bona-fide zeros, so expression is characterized by zero-inflation of otherwise continuous values. The average expression from single cells and 100-cell replicates is compared to develop quality control metrics that optimize the single-cell, 100-cell concordance. A Hurdle model is proposed, which accounts for the fact that genes at the single-cell level can be on (and a continuous expression measure is recorded) or dichotomously off (and the recorded expression is zero). Based on this model, I derive a combined likelihood-ratio test for differential expression that incorporates both the discrete and continuous components. This chapter was originally published in McDavid et al. [2013]. • In Chapter 3, I consider application of the hurdle model to single cell RNA sequencing (scRNAseq). In these technologies, the binary zero-inflation described found in rt- qPCR-based assays manifests itself as continuous, bimodal expression, motivating a clustering and thresholding procedure to assign expression to a cluster. The Hurdle model, extended and cast as a vector generalized linear model (vGLM), is provided as an R package named MAST. The cellular detection rate (CDR) is defined as the number of expressed genes found in a cell. It is identified as an important latent factor in single cell experiments, and is argued to measure size and efficiency variations among cells. Gene set enrichment analysis using the Hurdle model, and use of residuals defined through such models are discussed. Parts of this chapter were originally published in Finak et al. [2015], McDavid et al. [2014]. • In Chapter 4, the Hurdle model is generalized to model multivariate dependences between cells, permitting the parametrization of graphical models. A neighborhood selection-based method is proposed to leverage group-l1 penalized regression. Networks estimated on single-cell and multi-cell experiments are contrasted and found to be very distinct. In order to synthesize graphs estimated on transcriptome-scale data, a test for enrichment of connections between and within gene ontology categories is proposed
Faculty Opinions recommendation of A general and flexible method for signal extraction from single-cell RNA-seq data.
No full textFaculty Opinions recommendation of SCnorm: robust normalization of single-cell RNA-seq data.
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Faculty Opinions recommendation of High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy.
No full textGVF-based anisotropic diffusion models
No full textIn this paper, the gradient vector flow fields are introduced in image restoration. Within the context of flow fields, the shock filter, mean curvature flow, and Perona-Malik equation are reformulated. Many advantages over the original models can be obtained; these include numerical stability, large capture range, and high-order derivative estimation. In addition, a fairing process is introduced in the anisotropic diffusion, which contains a fourth-order derivative and is reformulated as the intrinsic Laplacian of curvature under the level set framework. By applying this fairing process, the shape boundaries will become more apparent. In order to overcome numerical errors, the intrinsic Laplacian of curvature is computed from the gradient vector flow fields instead of the observed images
Faculty Opinions recommendation of VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.
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