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"Li, Jessica"
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The transcultural streams of Chinese Canadian identities
\"Highlighting the geopolitical and economic circumstances that have prompted migration from Hong Kong and mainland China to Canada, The Transcultural Streams of Chinese Canadian Identities examines the Chinese Canadian community as a simultaneously transcultural, transnational, and domestic social and cultural formation. Essays in this volume argue that Chinese Canadians, a population that has produced significant cultural imprints on Canadian society, must create and constantly redefine their identities as manifested in social science, literary, and historical spheres. These perpetual negotiations reflect social and cultural ideologies and practices and demonstrate Chinese Canadians&' recreations of their self-perception, self-expression, and self-projection in relation to others. Contextualized within larger debates on multicultural society and specific Chinese Canadian cultural experiences, this book considers diverse cultural presentations of literary expression, the “model minority” and the influence of gender and profession on success and failure, the gendered dynamics of migration and the growth of transnational (“astronaut”) families in the 1980s, and inter-ethnic boundary crossing. Taking an innovative approach to the ways in which Chinese Canadians adapt to and construct the Canadian multicultural mosaic, The Transcultural Streams of Chinese Canadian Identities explores various patterns of Chinese cultural interchanges in Canada and how they intertwine with the community's sense of disengagement and belonging.\"-- Provided by publisher.
Book
An accurate and robust imputation method scImpute for single-cell RNA-seq data
The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.
Despite being widely performed in exploring cell heterogeneity and gene expression stochasticity, single cell RNA-seq analysis is complicated by excess zero counts (dropouts). Here, Li and Li develop scImpute for statistical imputation of dropouts in scRNA-seq data.
Journal Article
How Bad Apples Promote Bad Barrels: Unethical Leader Behavior and the Selective Attrition Effect
We present a theoretical rationale and supporting studies revealing how unethical leader behavior fosters an unethical climate within workgroups that increases member turnover intentions and malfeasance. Drawing on the attraction-selection-attrition model of organizational behavior, we propose a selective attrition effect whereby unethical leader behavior results in the retention of group members who are more comfortable with dishonesty and, consequently, more likely to engage in unethical behavior toward their group. In two experiments, exposure to unethical leader behavior (vs. ethical leader behavior) increased group members' likelihood of choosing to leave the group. Members who chose to remain in a group with an unethical leader were more likely than those who chose to leave to cheat their group in a subsequent task. A two time-period survey replicated these findings and identified psychological distress as the mechanism driving group members' turnover intentions. This research extends our understanding of the complex relationships between unethical leadership and follower turnover intentions, psychological distress, and malfeasance. We contribute to the behavioral ethics literature by identifying a previously underappreciated form of selective attrition that produces internal costs to groups and organizations, independent of reputational consequences and whether the unethicality is publicized.
Journal Article
Exaggerated false positives by popular differential expression methods when analyzing human population samples
When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.
Journal Article
The contribution of wildfire to PM2.5 trends in the USA
Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy
1
,
2
, have led to public health benefits
1
–
4
. However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM
2.5
), a pollutant regulated under the Clean Air Act
1
, have stagnated or begun to reverse throughout much of the USA
5
. Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM
2.5
trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM
2.5
concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM
2.5
concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM
2.5
concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM
2.5
concentrations are unregulated under current air pollution law
6
and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm.
Ground- and satellite-based air pollution data from 2000 to 2022 quantify the contribution of wildfire smoke to stagnation or reversal in PM
2.5
concentration trends, showing that this contribution will grow as the climate continues to warm.
Journal Article
scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics
We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.
The challenge of simulating multiomic single-cell data is addressed by a probabilistic model.
Journal Article
PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data
To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed
p
-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated
p
-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.
Journal Article
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.
Journal Article
Smile Big or Not? Effects of Smile Intensity on Perceptions of Warmth and Competence
While previous work has focused on the positive impact of smiles on interpersonal perceptions, this research proposes and finds that smile intensity differentially affects two fundamental dimensions of social judgments—warmth and competence. A marketer displaying a broad smile, compared to a slight smile, is more likely to be perceived by consumers as warmer but less competent. Furthermore, the facilitative effect of smile intensity on warmth perceptions is more prominent among promotion-focused consumers and in low-risk consumption contexts, while the detrimental effect of smile intensity on competence perceptions is more likely to occur among prevention-focused consumers and in high-risk consumption situations. Field observations in a crowdfunding context further indicate that the effects of smile intensity on warmth and competence perceptions have downstream consequences on actual consumer behaviors.
Journal Article
PCA outperforms popular hidden variable inference methods for molecular QTL mapping
Background
Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth “QTL”) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose.
Results
Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use.
Conclusions
To help researchers use PCA in their QTL analysis, we provide an R package
PCAForQTL
along with a detailed guide, both of which are freely available at
https://github.com/heatherjzhou/PCAForQTL
. We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.
Journal Article