Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.

Developments of single-cell RNA sequencing (scRNA-seq) technologies have enabled biological discoveries at the single-cell resolution with high throughput. However, large scRNA-seq datasets always suffer from massive technical noises, including batch effects and dropouts, and the dropout is often shown to be batch-dependent. Most existing methods only address one of the problems, and we show that the popularly used methods failed in trading off batch effect correction and dropout imputation. Here, inspired by the idea of causal inference, we propose a novel propensity score matching method for scRNA-seq data (scPSM) by borrowing information and taking the weighted average from similar cells in the deep sequenced batch, which simultaneously removes the batch effect, imputes dropout and denoises data in the entire gene expression space. The proposed method is testified on two simulation datasets and a variety of real scRNA-seq datasets, and the results show that scPSM is superior to other state-of-the-art methods. First, scPSM improves clustering accuracy and mixes cells of the same type, suggesting its ability to keep cell type separation while correcting for batch. Besides, using the scPSM-integrated data as input yields results free of batch effects or dropouts in the differential expression analysis. Moreover, scPSM not only achieves ideal denoising but also preserves real biological structure for downstream gene-based analyses. Furthermore, scPSM is robust to hyperparameters and small datasets with a few cells but enormous genes. Comprehensive evaluations demonstrate that scPSM jointly provides desirable batch effect correction, imputation and denoising for recovering the biologically meaningful expression in scRNA-seq data.

A retrospective analysis of preemptive pharmacogenomic testing in 22,918 individuals from China.

BACKGROUND: Pharmacogenomics (PGx) examines the influence of genetic variation on drug responses. With more and more Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines published, PGx is gradually shifting from the reactive testing of single gene toward the preemptive testing of multiple genes. But the profile of PGx genes, especially for the intra-country diversity, is not well understood in China. METHODS: We retrospectively collected preemptive PGx testing data of 22,918 participants from 20 provinces of China, analyzed frequencies of alleles, genotypes and phenotypes of pharmacogenes, predicted drug responses for each participant, and performed comparisons between different provinces. RESULTS AND CONCLUSION: After analyzing 15 pharmacogenes from CPIC guidelines of 31 drugs, we found that 99.97% of individuals may have an atypical response to at least one drug; the participants carry actionable genotypes leading to atypical dosage recommendation for a median of eight drugs. Over 99% of the participants were recommended a decreased warfarin dose based on genetic factors. There were 20 drugs with high-risk ratios from 0.18% to 58.25%, in which clopidogrel showed the highest high-risk ratio. In addition, the high-risk ratio of rasburicase in GUANGDONG (risk ratio (RR) = 13.17, 95%CI:4.06-33.22, p < 0.001) and GUANGXI (RR = 23.44, 95%CI:8.83-52.85, p < 0.001) were significantly higher than that in all provinces. Furthermore, the diversity we observed among 20 provinces suggests that preemptive PGx testing in different geographical regions in China may need to pay more attention to specific genes. These results emphasize the importance of preemptive PGx testing and provide essential evidence for promoting clinical implementation in China.

Mutation landscape of TSC1/TSC2 in Chinese patients with tuberous sclerosis complex.

Genetic testing of TSC1 and TSC2 is important for the diagnosis of tuberous sclerosis complex (TSC), an autosomal dominant neurocutaneous disease. This study retrospectively reviewed 347 samples from patients with clinically suspected TSC being tested for mutations in TSC1 and TSC2 genes using next-generation sequencing and multiplex ligation-dependent probe amplification. Two hundred eighty-one patients (80.98%) were classified as definite/possible/uncertain diagnosis of TSC and the mutational spectrum of TSC1/TSC2 was described. Two hundred eighteen unique nonsynonymous SNVs/Indels (64 in TSC1, 154 in TSC2) and 13 copy number variants (CNVs) were identified in 241 samples (85.77%), including 82 novel variants. CNVs involving 12 large deletions and one duplication were detected exclusively in TSC2. Both TSC1 and TSC2 mutations were nearly uniformly distributed in their protein-coding regions. Furthermore, a string of non-TSC1/TSC2 deleterious variants in 12 genes was identified in the patients, especially overwhelmingly present in the patients with no mutation identified (NMI) in TSC1/TSC2. Our study provides a comprehensive TSC1/TSC2 mutation landscape and reveal some potential risk non-TSCs variants present in patients with NMI.

[Randomized double-blind parallel controlled multicenter trial of Reyanning Mixture in treatment of acute tonsillitis].

This study is a randomized controlled trial of Reyanning Mixture in the treatment of acute tonsillitis. According to the ratio of 1∶1∶1, a total of 144 patients were randomly divided into Reyanning Mixture group(RYN), Reyanning Mixture+Amoxicillin Capsules group(RYN+Amoxil) and Amoxicillin Capsules group(Amoxil), with 48 cases in each group, in order to evaluate the efficacy and safety of RYN alone or combined with Amoxil in the treatment of acute tonsillitis, and provided high-quality evidences for treatment of infectious diseases with traditional Chinese medicine and reduced use of antibiotics. The dosage of RYN was 20 mL, 3 times a day, 100 mL/bottle, oral for 7 days, and Amoxil dosage was 0.5 g, 3 times a day, 0.5 g×12 tablets/plate, oral for 7 days. A total of 144 cases were included, 3 cases were excluded(1 case was mistakenly included, 2 cases did not take drugs after inclu-ded), and a total of 141 cases were included in the full analysis set(FAS). The results showed statistical differences in the recovery time of the disease, the disappearance rate of fever on the 3 rd day and the disappearance rate of tonsillar redness and swelling between RYN and Amoxil. There were statistical differences in the cure rate of disease, recovery time of disease, body temperature recovery time, fever disappearance rate on the 3 rd day, pharynx swelling and pain disappearance rate and tonsil swelling disappearance rate between the RYN+Amoxil and Amoxil, but with no significant difference in the above aspects compared with RYN. The DDD of antibiotic use in RYN+Amoxil was significantly lower than that in Amoxil(P<0.01). According to the findings, when RYN was used alone in the treatment of acute tonsillitis, it was superior to Amoxil in time of recovery, short-term improvement of fever and redness and swelling of tonsil. Compared with RYN+Amoxil, there was no difference in cure rate of disease, recovery time of disease, body temperature recovery time, short-term improvement of fever, swelling of pharynx and swelling of tonsil, with a better efficacy than Amoxil. The clinical effect of RYN was similar to that of combined Amoxil in the treatment of acute tonsillitis, and RYN was superior to Amoxil in the time of recovery, short-term improvement of fever and redness and swelling of tonsil, with no adverse event or adverse reaction. RYN+Amoxil can significantly reduce the DDD value of antibiotics in the treatment of acute tonsillitis, with significant clinical advantages over Amoxil.

Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel EEG.

Single-channel EEG based sleep staging is of interest to researchers due to its broad application prospect in daily sleep monitoring recently. We proposed using contextual scalograms as input and developed a convolutional neural network with attention modules named Co-ScaleNet for sleep staging. The contextual scalograms were obtained by combining the same color channels of three original RGB scalograms from consecutive epochs, and a simple and efficient data augmentation was designed according to their various forms. The Co-ScaleNet consists of two main parts. Firstly, three parallel convolutional branches with attention modules correspondingly extract and fuse features from contextual scalograms at the top layers. The remaining part is a stack of lightweight blocks. We achieved an overall accuracy of 87.0% for healthy individuals, 84.7% for depressed patients. And we obtained comparable performance on the public Sleep-EDFx (82.8%), ISRUC (84.6%) and SHHS datasets (87.7%), including a high recall of N1. The contextual scalograms of R channel as input achieved the best performance, which conform to the features of interest in visual scoring. The attention modules improved the recall of N1 and N3. Overall, the contextual scalograms provided a novel scheme for both contextual information extraction and data augmentation. Our study successfully expanded its application to depression datasets, as well as patients with sleep apnea, demonstrating its wide applicability.

Batch alignment of single-cell transcriptomics data using deep metric learning.

scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.

Investigating the influence of an adjustable zoned air mattress on sleep: a multinight polysomnography study.

Introduction: A comfortable mattress should improve sleep quality. In this study, we sought to investigate the specific sleep parameters that could be affected by a mattress and explore any potential differences between the effects felt by each sex. Methods: A total of 20 healthy young adults (10 females and 20 males; 22.10 ± 1.25 years) participated in the experiments. A smart adjustable zoned air mattress was designed to maintain comfortable support, and an ordinary mattress was used for comparison. The participants individually spent four nights on these two mattresses in four orders for polysomnography (PSG) scoring. Sleep architecture, electroencephalogram (EEG) spectrum, and heart rate variability (HRV), which reflect the central and autonomic nervous activities, were used to compare the difference between the two mattresses. Results: An individual difference exited in sleep performance. The modes of influence of the mattresses were different between the sexes. The adjustable air mattress and the increase in experimental nights improved female participants' sleep efficiency, while male participants exhibited a smaller response to different mattresses. With an increasing number of experiment nights, both sexes showed increased REM and decreased N2 proportions; the N3 sleep proportion decreased in the male participants, and the heart rate decreased in both sexes. The performance of the EEG spectrum supports the above results. In addition, the adjustable air mattress weakened automatic nerve activity during N3 sleep in most participants. The female participants appeared to be more sensitive to mattresses. Experiment night was associated with psychological factors. There were differences in the results for this influence between the sexes. Conclusion: This study may shed some light on the differences between the ideal sleep environment of each sex.

Semisupervised Prior Free Rare Category Detection With Mixed Criteria.

Rare category detection aims to find interesting and statistically significant anomalies and incorporates ideas from active learning and semisupervised learning. The challenge of rare category detection is to find the rare classes of the anomalies in a data set where the data distribution is skewed. Most existing rare category detection methods suppose that the user knows the specific number of all classes in advance, which cannot be satisfied in most real scenarios. In this paper, we propose a new rare category detection framework composed of active learning and semisupervised hierarchical density-based clustering. The advantage of our method is that it is prior free and can benefit the rare category detecting process with the labeled data. In addition, the proposed framework can handle tasks with nonlinear mappings, which increases the ability to find rare classes when the class boundary is sophisticated. Compared to existing methods, better results are achieved by our method on both real and synthetic data sets in the experiment.

Integrated Analysis of MicroRNA (miRNA) and mRNA Profiles Reveals Reduced Correlation between MicroRNA and Target Gene in Cancer.

BACKGROUND: Accumulating evidences demonstrated that microRNA-target gene pairs were closely related to tumorigenesis and development. However, the correlation between miRNA and target gene was insufficiently understood, especially its changes between tumor and normal tissues. OBJECTIVES: The aim of this study was to evaluate the changes of correlation of miRNAs-target pairs between normal and tumor. MATERIALS AND METHODS: 5680 mRNA and 5740 miRNA expression profiles of 11 major human cancers were downloaded from the Cancer Genome Atlas (TCGA). The 11 cancer types were bladder urothelial carcinoma, breast invasive carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, stomach adenocarcinoma, and thyroid carcinoma. For each cancer type, we firstly obtained differentially expressed miRNAs (DEMs) and genes (DEGs) in tumor and then acquired critical miRNA-target gene pairs by combining DEMs, DEGs and two experimentally validated miRNA-target interaction databases, miRTarBase and miRecords. We collected samples with both miRNA and mRNA expression values and performed a correlation analysis by Pearson method for miRNA-target pairs in normal and tumor, respectively. RESULTS: We totally got 4743 critical miRNA-target pairs across 11 cancer types, and 4572 of them showed weaker correlation in tumor than in normal. The average correlation coefficients of miRNA-target pairs were different greatly between normal (-0.38 ~ -0.61) and tumor (-0.04 ~ -0.26) for 11 cancer type. The pan-cancer network, which consisted of 108 edges connecting 35 miRNAs and 89 target genes, showed the interactions of pairs appeared in multicancers. CONCLUSIONS: This comprehensive analysis revealed that correlation between miRNAs and target genes was greatly reduced in tumor and these critical pairs we got were involved in cellular adhesion, proliferation, and migration. Our research could provide opportunities for investigating cancer molecular regulatory mechanism and seeking therapeutic targets.

Identification and Validation of Circulating MicroRNA Signatures for Breast Cancer Early Detection Based on Large Scale Tissue-Derived Data.

PURPOSE: Breast cancer is the most commonly occurring cancer among women worldwide, and therefore, improved approaches for its early detection are urgently needed. As microRNAs (miRNAs) are increasingly recognized as critical regulators in tumorigenesis and possess excellent stability in plasma, this study focused on using miRNAs to develop a method for identifying noninvasive biomarkers. METHODS: To discover critical candidates, differential expression analysis was performed on tissue-originated miRNA profiles of 409 early breast cancer patients and 87 healthy controls from The Cancer Genome Atlas database. We selected candidates from the differentially expressed miRNAs and then evaluated every possible molecular signature formed by the candidates. The best signature was validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls using reverse transcription quantitative real-time polymerase chain reaction. RESULTS: The miRNA candidates in our method were revealed to be associated with breast cancer according to previous studies and showed potential as useful biomarkers. When validated in independent serum samples, the area under curve of the final miRNA signature (miR-21-3p, miR-21-5p, and miR-99a-5p) was 0.895. Diagnostic sensitivity and specificity were 97.9% and 73.5%, respectively. CONCLUSION: The present study established a novel and effective method to identify biomarkers for early breast cancer. And the method, is also suitable for other cancer types. Furthermore, a combination of three miRNAs was identified as a prospective biomarker for breast cancer early detection.

Intrinsic 3D Dynamic Surface Tracking based on Dynamic Ricci Flow and Teichmüller Map.

3D dynamic surface tracking is an important research problem and plays a vital role in many computer vision and medical imaging applications. However, it is still challenging to efficiently register surface sequences which has large deformations and strong noise. In this paper, we propose a novel automatic method for non-rigid 3D dynamic surface tracking with surface Ricci flow and Teichmüller map methods. According to quasi-conformal Teichmüller theory, the Techmüller map minimizes the maximal dilation so that our method is able to automatically register surfaces with large deformations. Besides, the adoption of Delaunay triangulation and quadrilateral meshes makes our method applicable to low quality meshes. In our work, the 3D dynamic surfaces are acquired by a high speed 3D scanner. We first identified sparse surface features using machine learning methods in the texture space. Then we assign landmark features with different curvature settings and the Riemannian metric of the surface is computed by the dynamic Ricci flow method, such that all the curvatures are concentrated on the feature points and the surface is flat everywhere else. The registration among frames is computed by the Teichmüller mappings, which aligns the feature points with least angle distortions. We apply our new method to multiple sequences of 3D facial surfaces with large expression deformations and compare them with two other state-of-the-art tracking methods. The effectiveness of our method is demonstrated by the clearly improved accuracy and efficiency.

CPU-GPU mixed implementation of virtual node method for real-time interactive cutting of deformable objects using OpenCL.

PURPOSE: Surgical simulators need to simulate interactive cutting of deformable objects in real time. The goal of this work was to design an interactive cutting algorithm that eliminates traditional cutting state classification and can work simultaneously with real-time GPU-accelerated deformation without affecting its numerical stability. METHODS: A modified virtual node method for cutting is proposed. Deformable object is modeled as a real tetrahedral mesh embedded in a virtual tetrahedral mesh, and the former is used for graphics rendering and collision, while the latter is used for deformation. Cutting algorithm first subdivides real tetrahedrons to eliminate all face and edge intersections, then splits faces, edges and vertices along cutting tool trajectory to form cut surfaces. Next virtual tetrahedrons containing more than one connected real tetrahedral fragments are duplicated, and connectivity between virtual tetrahedrons is updated. Finally, embedding relationship between real and virtual tetrahedral meshes is updated. Co-rotational linear finite element method is used for deformation. Cutting and collision are processed by CPU, while deformation is carried out by GPU using OpenCL. RESULTS: Efficiency of GPU-accelerated deformation algorithm was tested using block models with varying numbers of tetrahedrons. Effectiveness of our cutting algorithm under multiple cuts and self-intersecting cuts was tested using a block model and a cylinder model. Cutting of a more complex liver model was performed, and detailed performance characteristics of cutting, deformation and collision were measured and analyzed. CONCLUSIONS: Our cutting algorithm can produce continuous cut surfaces when traditional minimal element creation algorithm fails. Our GPU-accelerated deformation algorithm remains stable with constant time step under multiple arbitrary cuts and works on both NVIDIA and AMD GPUs. GPU-CPU speed ratio can be as high as 10 for models with 80,000 tetrahedrons. Forty to sixty percent real-time performance and 100-200 Hz simulation rate are achieved for the liver model with 3,101 tetrahedrons. Major bottlenecks for simulation efficiency are cutting, collision processing and CPU-GPU data transfer. Future work needs to improve on these areas.