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Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

 

Advances in single-cell RNA sequencing (scRNAseq) technologies have allowed us to study the heterogeneity of cell populations. The cell compositions of tissues from different hosts may vary greatly, indicating the condition of the hosts, from which the samples are collected. However, the high sequencing cost and the lack of fresh tissues make single-cell approaches less appealing. In many cases, it is practically impossible to generate single-cell data in a large number of subjects, making it challenging to monitor changes in cell type compositions in various diseases. Here we introduce a novel approach, named Deconvolution using Weighted Elastic Net (DWEN), that allows researchers to accurately estimate the cell type compositions from bulk data samples without the need of generating single-cell data. It also allows for the re-analysis of bulk data collected from rare conditions to extract more in-depth cell-type level insights. The approach consists of two modules. The first module constructs the cell type signature matrix from single-cell data while the second module estimates the cell type compositions of input bulk samples. In an extensive analysis using 20 datasets generated from scRNA-seq data of different human tissues, we demonstrate that DWEN outperforms current state-of-the-arts in estimating cell type compositions of bulk samples. 

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method’s practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

 

In this article, we offer theory‐grounded narratives of a 4‐year participatory design process of a Learning Analytics tool with K‐12 educators. We describe how wedesign‐in‐partnershipby leveraging educators' routines, values and cultural representations into the designs of digital dashboards. We make our long‐term reasoning visible by reflecting upon how design decisions were made, discussing key tensions and analysing to what extent the developed tools were taken up in practice. Through thick design narratives, we reflect upon how cultural forms—recognizable cultural constructs that might cue and facilitate specific activities—were identified among educators and informed the design of a dashboard. We then examined the extent to which the designed tool supported coaches and teachers to engage in Generative Uncertainty, an interpretive stance in which educators manifest productive inquiries towards data. Our analysis highlights that attuning to cultural forms is a valuable first step but not enough towards designing LA tools for systems in ways that fit institutionalized practices, challenge instrumental uses and spur productive inquiry. We conclude by offering two key criteria for making culturally‐grounded design decisions in the context of long‐term partnerships.

What is already known about this topic

Participatory design can invite stakeholders to directly inform the creation of LA artefacts that fit their needs, context and cultural markers.

What this paper adds

Cultural forms can be identified and leveraged in the design of LA tools.

HCLA scholars ought to design forsystems—the complex body of organizational routines, cultural practices and interactions among multiple stakeholders—and not just forusers.

Implications for practice and/or policy

Leveraging cultural forms in LA needs to be accompanied by a critical view of which practices, behaviours, values and structures are suggested by such forms.

Designing features that are easy to use, are associated with concrete tasks, and fit into existing cultural practices are three criteria for embedding cultural forms into LA design.

 

People often confuse smell loss with taste loss, so it is unclear how much gustatory function is reduced in patients self-reporting taste loss. Our pre-registered cross-sectional study design included an online survey in 12 languages with instructions for self-administering chemosensory tests with 10 household items. Between June 2020 and March 2021, 10,953 individuals participated. Of these, 5,225 self-reported a respiratory illness and were grouped based on their reported COVID test results: COVID-positive (COVID+, N = 3,356), COVID-negative (COVID−, N = 602), and COVID unknown for those waiting for a test result (COVID?, N = 1,267). The participants who reported no respiratory illness were grouped by symptoms: sudden smell/taste changes (STC, N = 4,445), other symptoms excluding smell or taste changes (OthS, N = 832), and no symptoms (NoS, N = 416). Taste, smell, and oral irritation intensities and self-assessed abilities were rated on visual analog scales. Compared to the NoS group, COVID+ was associated with a 21% reduction in taste (95% confidence interval (CI): 15–28%), 47% in smell (95% CI: 37–56%), and 17% in oral irritation (95% CI: 10–25%) intensity. There were medium to strong correlations between perceived intensities and self-reported abilities (r = 0.84 for smell, r = 0.68 for taste, and r = 0.37 for oral irritation). Our study demonstrates that COVID-19-positive individuals report taste dysfunction when self-tested with stimuli that have little to none olfactory components. Assessing the smell and taste intensity of household items is a promising, cost-effective screening tool that complements self-reports and may help to disentangle taste loss from smell loss. However, it does not replace standardized validated psychophysical tests.

 

Abstract: Developing student interest is critical to supporting student learning in computer science. Research indicates that student interest is a key predictor of persistence and achievement. While there is a growing body of work on developing computing identities for diverse students, little research focuses on early exposure to develop multilingual students’ interest in computing. These students represent one of the fastest growing populations in the US, yet they are dramatically underrepresented in computer science education. This study examines identity development of upper elementary multilingual students as they engage in a year-long computational thinking curriculum, and follows their engagement across multiple settings (i.e., school, club, home, community). Findings from pre- and -post surveys of identity showed significant differences favoring students’ experiences with computer science, their perceptions of computer science, their perceptions of themselves as computer scientists, and their family support for computer science. Findings from follow-up interviews and prior research suggest that tailored instruction provides opportunities for connections to out-of-school learning environments with friends and family that may shift students’ perceptions of their abilities to pursue computer science and persist when encountering challenges. 

Project‐based learning has shown promise in improving learning outcomes for diverse students. However, studies on its impacts have largely focused on the perceptions of students and instructors or students' immediate performance. This study reports the impact of taking a project‐based introductory engineering course on students' subsequent academic success.

This quantitative study examines characteristics related to enrollment in the project‐based introductory engineering course and subsequent academic performance. We hypothesized that participation in the course would be associated with higher academic performance in subsequent engineering courses. In addition, we examined heterogeneity effects for students traditionally underrepresented in engineering education.

This study utilized data on students' demographics, academic preparation, course enrollment, and course performance from 1,318 engineering students from a large public university in Southern California. Logistic regression analysis with robust standard errors examined enrollment patterns. We applied propensity scores as inverse‐probability weights in multiple linear models to calculate the average treatment effect on the treated for participants from the project‐based introductory engineering course in five subsequent engineering courses. This analysis was conducted for all students and for selected student subgroups.

Enrollment in the project‐based introductory engineering course was positively associated with students' performance in some subsequent engineering courses and did not adversely affect students traditionally underrepresented in engineering.

This study provides an example of a project‐based introductory engineering course that can support students' academic success in engineering. The benefits detected for some student populations (e.g., female) are encouraging for broadening engineering pathways.