scholarly journals SCHiRM: Single Cell Hierarchical Regression Model to detect dependencies in read count data

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Henrik Mannerström ◽  
Saara Hiltunen ◽  
Harri Lähdesmäki
Keyword(s):  
Regression Model ◽  
Single Cell ◽  
Count Data ◽  
Regulatory Networks ◽  
Research Question ◽  
Read Count ◽  
Hierarchical Regression ◽  
Specific Gene ◽  
Hierarchical Regression Model ◽  
Log Normal

AbstractMotivationModern single cell RNA sequencing (scRNA-seq) technologies have made it possible to measure the RNA content of individual cells. The scRNA-seq data provide us with detailed information about the cellular states but, despite several pioneering efforts, it remains an open research question how regulatory networks could be inferred from these noisy discrete read count data.ResultsHere, we introduce a hierarchical regression model which is designed for detecting dependencies in scRNA-seq and other count data. We model count data using the Poisson-log normal distribution and, by means of our hierarchical formulation, detect the dependencies between genes using linear regression model for the latent, cell-specific gene expression rate parameters. The hierarchical formulation allows us to model count data without artificial data transformations and makes it possible to incorporate normalization information directly into the latent layer of the model. We test the proposed approach using both simulated and experimental data. Our results show that the proposed approach performs better than standard regression techniques in parameter inference task as well as in variable selection task.AvailabilityAn implementation of the method is available athttps://github.com/jeintos/[email protected],[email protected]

scholarly journals DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

2021 ◽  
Author(s):  
Jiaxing Chen ◽  
Chinwang Cheong ◽  
Liang Lan ◽  
Xin Zhou ◽  
Jiming Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Regulatory Networks ◽  
Deep Neural Network ◽  
Neighborhood Context ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

scholarly journals Researcher Perceptions of a Self-Service Online Portal to Facilitate Volunteer Recruitment into Clinical Trials

ACI Open ◽  
2021 ◽  
Vol 05 (02) ◽  
pp. e59-e66
Author(s):  
Srinivas Emani ◽  
Yichuan Grace Hsieh ◽  
Greg Estey ◽  
Holly M. Parker ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Regression Model ◽  
Behavioral Intention ◽  
Self Efficacy ◽  
Hierarchical Regression ◽  
Intention To Use ◽  
Hierarchical Regression Model ◽  
Volunteer Recruitment ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

Abstract Background Recruitment of volunteers is a major challenge for clinical trials. There has been increasing development and use of Internet-based portals in recruitment for clinical research. There has been little research on researcher use and perceptions of these portals. Objectives This study evaluated researcher perceptions of use of Rally, an Internet-based portal for clinical trial volunteer recruitment. Methods A cross-sectional survey was developed and implemented to understand researcher perceptions. From theoretical models of information technology use, the survey adopted items in four domains: ease of use, usefulness, facilitating conditions, and self-efficacy. The dependent variable was researchers' behavioral intention to use Rally. The survey captured characteristics of researchers such as gender, age, and role. It was implemented using the REDCap survey tool. An email invitation followed by three reminders was sent to researchers. A hierarchical regression model was applied to assess predictors of behavioral intention. Results The survey response rate was 35.6% (152 surveys received from 427 contacted researchers). In the hierarchical regression model, facilitating conditions and self-efficacy predicted behavioral intention (F (4,94) = 6.478; p <0.001). The model explained 21.6% of the variance in behavioral intention (R-square change = 21.3%, p <0.001). Conclusion Facilitating conditions and self-efficacy predicted researchers' behavioral intention to use Rally for volunteer recruitment into clinical trials. Future research should document best practices and strategies for enhancing researcher use of online portals for volunteer recruitment.

scholarly journals The Influence of Clinical Nurses' Empathy on their Death Competence: A Cross-Sectional Survey Study

2021 ◽  
Author(s):  
Jiahao Wang ◽  
Zhengying Chen ◽  
Yiting Liu ◽  
Xiaoli Liao ◽  
Liuxin Long ◽  
...  
Keyword(s):  
Regression Model ◽  
Pearson Correlation ◽  
General Information ◽  
Survey Study ◽  
Hierarchical Regression ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Clinical Nurses ◽  
Hierarchical Regression Model ◽  
The Status

Abstract BackgroundEmpathy and death competence are important competences for clinical nurses. However, there is no clear consensus about what impact empathy has on death competency. Our study aimed to understand the status of the empathy and death competence of clinical nurses in China and to explore the effect of empathy on their competence.MethodsFor a survey conducted from May–June 2021, 1415 clinical nurses were selected by convenience sampling as the research objects. The Coping with Death Scale, the Jefferson Scale of Empathy—Health Professionals and a general information questionnaire designed by the researchers were used to investigate the status of the empathy and death competence of clinical nurses. The relationship between empathy and death competence was analysed by Pearson correlation, and the influence of the empathy of clinical nurses on their death competence was analysed by a hierarchical regression model.ResultPearson correlation analysis revealed that death competence was positively correlated with each dimension of empathy. Hierarchical regression model analysis revealed that after controlling for the influence of general information, nurses' empathy had a significant influence on their death competence, and this independently explained 5.8% of the variance in death competence.ConclusionsThe death competence of the clinical nurses in this sample was moderate to low level. Emotional nursing and transposition thinking are important influencing factors of death competence. Nursing managers should improve the empathy of clinical nurses to promote their death competence.

scholarly journals Human colorectal pre-cancer atlas identifies distinct molecular programs underlying two major subclasses of pre-malignant tumors

2021 ◽  
Author(s):  
Bob Chen ◽  
Eliot T. McKinley ◽  
Alan J. Simmons ◽  
Marisol A. Ramirez-Solano ◽  
Xiangzhu Zhu ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Immune Cell ◽  
Malignant Tumors ◽  
Colorectal Polyps ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Normal Colonic Mucosa ◽  
Serrated Polyps ◽  
Targeted Gene Sequencing

AbstractMost colorectal cancers (CRCs) develop from either adenomas (ADs) or sessile serrated lesions (SSLs). The origins and molecular landscapes of these histologically distinct pre-cancerous polyps remain incompletely understood. Here, we present an atlas at single-cell resolution of sporadic conventional tubular/tubulovillous ADs, SSLs, hyperplastic polyps (HPs), microsatellite stable (MSS) and unstable (MSI-H) CRC, and normal colonic mucosa. Using single-cell transcriptomics and multiplex imaging, we studied 69 datasets from 33 participants. We also examined separate sets of 66 and 274 polyps for RNA and targeted gene sequencing, respectively. We performed multiplex imaging on a tissue microarray of 14 ADs and 15 CRCs, and we integrated pre-cancer polyp data with published single-cell and The Cancer Genome Atlas (TCGA) bulk CRC data to establish potential polyp-cancer relationships. Striking differences were observed between ADs and SSLs that extended to MSS and MSI-H CRCs, respectively, reflecting their distinct origins and trajectories. ADs arose from WNT pathway dysregulation in stem cells, which aberrantly expanded and expressed a Hippo and ASCL2 regenerative program. In marked contrast, SSLs were depleted of stem cell-like populations and instead exhibited a program of gastric metaplasia in the setting of elevated cytotoxic inflammation. Using subtype-specific gene regulatory networks and shared genetic variant analysis, we implicated serrated polyps, including some HPs conventionally considered benign, as arising from a metaplastic program in committed absorptive cells. ADs and SSLs displayed distinct patterns of immune cell infiltration that may influence their natural history. Our multi-omic atlas provides novel insights into the malignant potential of colorectal polyps and serves as a framework for precision surveillance and prevention of sporadic CRC.

scholarly journals Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Saeed Sharifi-Malvajerdi ◽  
Feiyu Zhu ◽  
Colin B. Fogarty ◽  
Michael P. Fay ◽  
Rick M. Fairhurst ◽  
...  
Keyword(s):  
Regression Model ◽  
Clearance Rate ◽  
Software Package ◽  
Malaria Parasite ◽  
Parasite Clearance ◽  
Hierarchical Regression ◽  
R Software ◽  
Bayesian Hierarchical ◽  
Hierarchical Regression Model

scholarly journals LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution

2020 ◽  
Vol 49 (D1) ◽  
pp. D125-D133
Author(s):  
Peng Wang ◽  
Qiuyan Guo ◽  
Yangyang Hao ◽  
Qian Liu ◽  
Yue Gao ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Tumour Microenvironment ◽  
Fine Tuning ◽  
Specific Gene ◽  
Related Data ◽  
Tumour Metastasis ◽  
Gene Regulatory ◽  
The One

Abstract Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the ‘One Cell, One World’ theory. LnCeCell is curated with cellular-specific ceRNA regulations from &gt;94 000 cells across 25 types of cancers and provides &gt;9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes.

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