Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276,

Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

ObjectiveEvaluate the risk of pre-existing comorbidities on COVID-19 mortality, and provide clinical suggestions accordingly.SettingA nested case–control design using confirmed case reports released from the news or the national/provincial/municipal health commissions of China between 18 December 2019 and 8 March 2020.ParticipantsPatients with confirmed SARS-CoV-2 infection, excluding asymptomatic patients, in mainland China outside of Hubei Province.Outcome measuresPatient demographics, survival time and status, and history of comorbidities.MethodA total of 94 publicly reported deaths in locations outside of Hubei Province, mainland China, were included as cases. Each case was matched with up to three controls, based on gender and age ±1 year old (94 cases and 181 controls). The inverse probability-weighted Cox proportional hazard model was performed, controlling for age, gender and the early period of the outbreak.ResultsOf the 94 cases, the median age was 72.5 years old (IQR=16), and 59.6% were men, while in the control group the median age was 67 years old (IQR=22), and 64.6% were men. Adjusting for age, gender and the early period of the outbreak, poor health conditions were associated with a higher risk of COVID-19 mortality (HR of comorbidity score, 1.31 [95% CI 1.11 to 1.54]; p=0.001). The estimated mortality risk in patients with pre-existing coronary heart disease (CHD) was three times that of those without CHD (p<0.001). The estimated 30-day survival probability for a profile patient with pre-existing CHD (65-year-old woman with no other comorbidities) was 0.53 (95% CI 0.34 to 0.82), while it was 0.85 (95% CI 0.79 to 0.91) for those without CHD. Older age was also associated with increased mortality risk: every 1-year increase in age was associated with a 4% increased risk of mortality (p<0.001).ConclusionExtra care and early medical interventions are needed for patients with pre-existing comorbidities, especially CHD.

Background: China has experienced an outbreak of a novel human coronavirus (SARS-CoV-2) since December 2019, which quickly became a worldwide pandemic in early 2020. There is limited evidence on the mortality risk effect of pre-existing comorbidities for coronavirus disease 2019 (COVID-19), which has important implications for early treatment. Objective: Evaluate the risk of pre-existing comorbidities on COVID-19 mortality, and provide clinical suggestions accordingly. Method: This study used a nested case-control design. A total of 94 publicly reported deaths in locations outside of Hubei Province, China, between December 18 th , 2019 and March 8 th , 2020 were included as cases. Each case was matched with up to three controls, based on gender and age ± 1 year old (94 cases and 181 controls). The inverse probability weighted Cox proportional hazard model was performed.Results: History of comorbidities significantly increased the death risk of COVID-19: one additional pre-existing comorbidity led to an estimated 40% higher risk of death (p<0.001). The estimated mortality risk in patients with CHD was three times of those without CHD (p<0.001).The estimated 30-day survival probability for a profile patient with pre-existing CHD (65-yearold female with no other comorbidities) was 0.53 (95% CI [0.34-0.82]), while it was 0.85 (95% CI [0.79-0.91]) for those without CHD. Older age was also associated with increased death risk: every 5-year increase in age was associated with a 20% increased risk of mortality (p<0.001). Conclusion:Extra care and early medical intervention are needed for patients with pre-existing comorbidities, especially CHD.

Background Although many prognostic single-gene (SG) lists have been identified in cancer research, application of these features is hampered due to poor robustness and performance on independent datasets. Pathway-based approaches have thus emerged which embed biological knowledge to yield reproducible features. Methods Pathifier estimates pathways deregulation score (PDS) to represent the extent of pathway deregulation based on expression data, and most of its applications treat pathways as independent without addressing the effect of gene overlap between pathway pairs which we refer to as crosstalk . Here, we propose a novel procedure based on Pathifier methodology, which for the first time has been utilized with crosstalk accommodated to identify disease-specific features to predict prognosis in patients with hepatocellular carcinoma (HCC). Findings With the cohort (N = 355) of HCC patients from The Cancer Genome Atlas (TCGA), cross validation (CV) revealed that PDSs identified were more robust and accurate than the SG features by deep learning (DL)-based approach. When validated on external HCC datasets, these features outperformed the SGs consistently. Interpretation On average, we provide 10.2% improvement of prediction accuracy. Importantly, governing genes in these features provide valuable insight into the cancer hallmarks of HCC. We develop an R package PATHcrosstalk (available from GitHub https://github.com/fabotao/PATHcrosstalk ) with which users can discover pathways of interest with crosstalk effect considered.