FEED: a feature selection method based on gene expression decomposition for single cell clustering.

Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.

Computational approaches for detecting disease-associated alternative splicing events.

Alternative splicing (AS) is a key transcriptional regulation pathway. Recent studies have shown that AS events are associated with the occurrence of complex diseases. Various computational approaches have been developed for the detection of disease-associated AS events. In this review, we first describe the metrics used for quantitative characterization of AS events. Second, we review and discuss the three types of methods for detecting disease-associated splicing events, which are differential splicing analysis, aberrant splicing detection and splicing-related network analysis. Third, to further exploit the genetic mechanism of disease-associated AS events, we describe the methods for detecting genetic variants that potentially regulate splicing. For each type of methods, we conducted experimental comparison to illustrate their performance. Finally, we discuss the limitations of these methods and point out potential ways to address them. We anticipate that this review provides a systematic understanding of computational approaches for the analysis of disease-associated splicing.

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. RESULTS: Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. AVAILABILITY AND IMPLEMENTATION: DISHyper is freely available for download at https://github.com/genemine/DISHyper.

Realized Record Capacity and Rate Performance of Dual-Carbon Batteries Constructed by Introducing Superhard Nanodiamonds.

Dual-ion batteries (DIBs) are becoming an important technology for energy storage. To overcome the disadvantages of traditional electrodes and electrolytes, here we assemble a dual-carbon DIB with nanodiamond (ND)-modified crimped graphene (DCG) and electrolyte. The DCG anode and cathode realize high capacities of 1121 mA h g-1 and 149 mA h g-1, respectively, at 0.1 A g-1. The corresponding DCG//DCG full cells present a high capacity of 143 mA h g-1 at 1 A g-1 after 3300 cycles, which is superior to most reported results. Achieving these record performances is strongly dependent on the formed DCG electrodes with expanded interlayer spacing and abundant active sites, and NDs dispersed in DCG and electrolytes are very helpful for enhancing the storage of both cations and anions, effectively suppressing the irreversible decomposition of electrolytes. This work breaks through the bottleneck of graphitic-based DIBs, paving the way for realizing high-performance DIBs applied in industry.

Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis.

PURPOSE: Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment. METHODS: We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls. RESULTS: We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC. CONCLUSION: Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

Charge Redistribution of Lattice-Mismatched Co─Cu3P Boosting pH-Universal Water/Seawater Hydrogen Evolution.

Practical applications of the hydrogen evolution reaction (HER) rely on the development of highly efficient, stable, and low-cost catalysts. Tuning the electronic structure, morphology, and architecture of catalysts is an important way to realize efficient and stable HER electrocatalysts. Herein, Co-doped Cu3P-based sugar-gourd structures (Co─Cu3P/CF) are prepared on copper foam as active electrocatalysts for hydrogen evolution. This hierarchical structure facilitates fast mass transport during electrocatalysis. Notably, the introduction of Co not only induces a charge redistribution but also leads to lattice-mismatch on the atomic scale, which creates defects and performs as additional active sites. Therefore, Co─Cu3P/CF requires an overpotential of only 81, 111, 185, and 230 mV to reach currents of 50, 100, 500, and 1000 mA cm-2 in alkaline media and remains stable after 10 000 CV cycles in a row and up to 110 h i-t stability tests. In addition, it also shows excellent HER performance in water/seawater electrolytes of different pH values. Experimental and DFT show that the introduction of Co modulates the electronic and energy level structures of the catalyst, optimizes the adsorption and desorption behavior of the intermediate, reduces the water dissociation energy barrier during the reaction, accelerates the Volmer step reaction, and thus improves the HER performance.

In Situ, Rapid Synthesis of Carbon-Loaded High Density and Ultrasmall High Entropy Oxide Nanoparticles as Efficient Electrocatalysts.

High entropy materials offer almost unlimited catalytic possibilities due to their variable composition, unique structure, and excellent electrocatalytic performance. However, due to the strong tendency of nanoparticles to coarsen and agglomerate, it is still a challenge to synthesize nanoparticles using simple methods to precisely control the morphology and size of the nanoparticles in large quantities, and their large-scale application is limited by high costs and low yields. Herein, a series of high-entropy oxides (HEOs) nanoparticles with high-density and ultrasmall size (<5 nm) loaded on carbon nanosheets with large quantities are prepared by Joule-heating treatment of gel precursors in a short period of time (≈60 s). Among them, the prepared (FeCoNiRuMn)3O4-x catalyst shows the best electrocatalytic activity for oxygen evolution reaction, with low overpotentials (230 mV @10 mA cm-2, 270 mV @100 mA cm-2), small Tafel slope (39.4 mV dec-1), and excellent stability without significant decay at 100 mA cm-2 after 100 h. The excellent performance of (FeCoNiRuMn)3O4-x can be attributed to the synergistic effect of multiple elements and the inherent structural stability of high entropy systems. This study provides a more comprehensive design idea for the preparation of efficient and stable high entropy catalysts.

Oxygen-Terminated Polycrystalline Boron-Doped Diamond Superhydrophobic Surface with Excellent Mechanical and Thermal Stabilities.

Superhydrophobic surfaces are of great interest because of their remarkable properties. Due to its maximal hardness and chemical inertness, diamond film has great potential in fabricating robust superhydrophobic surfaces. In the present study, an oxygen-terminated polycrystalline boron-doped diamond (O-PBDD) superhydrophobic surface with micro/nano-hierarchical porous structures is developed. The preparation method is very simple, requiring only sputtering and dewetting procedures. The former involves sputtering gold and copper particles onto the hydrogen-terminated polycrystalline boron-doped diamond (H-PBDD) to form gold/copper films, whereas the latter involves placing the samples in an atmospheric tube furnace to form hierarchical pores. By controlling the etching parameters, the wettability of the O-PBDD surface can be adjusted from hydrophilic to superhydrophobic, which is significantly different to the normal hydrophilicity feature of O-termination diamonds. The water contact angle of the obtained O-PBDD surface can reach 165 ± 5°, which is higher than the superhydrophobic diamond surfaces that are reported in the literature. In addition, the O-PBDD surface exhibits excellent durability; it can maintain satisfactory superhydrophobicity even after high-pressure, high-temperature, and sandpaper friction tests. This work provides a new research direction for fabricating robust superhydrophobic materials with diamond film.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

MOTIVATION: A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction. RESULTS: In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods. AVAILABILITY AND IMPLEMENTATION: IsoFrog is freely available at https://github.com/genemine/IsoFrog.

CellBRF: a feature selection method for single-cell clustering using cell balance and random forest.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. RESULTS: We develop CellBRF, a feature selection method that considers genes' relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. AVAILABILITY AND IMPLEMENTATION: All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF.

Characterised intron retention profiles in muscle tissue of idiopathic inflammatory myopathy subtypes.

OBJECTIVES: Idiopathic inflammatory myopathies (IIMs) are a group of heterogeneous autoimmune diseases. Intron retention (IR) serves as an important post-transcriptional and translational regulatory mechanism. This study aims to identify changes in IR profiles in IIM subtypes, investigating their influence on proteins and their correlations with clinical features. METHODS: RNA sequencing and liquid chromatography-tandem mass spectrometry were performed on muscle tissues obtained from 174 patients with IIM and 19 controls, following QC procedures. GTFtools and iREAD software were used for IR identification. An analysis of differentially expressed IRs (DEIs), exons and proteins was carried out using edgeR or DEP. Functional analysis was performed with clusterProfiler, and SPIRON was used to assess splicing factors. RESULTS: A total of 6783 IRs located in 3111 unique genes were identified in all IIM subtypes compared with controls. IIM subtype-specific DEIs were associated with the pathogenesis of respective IIM subtypes. Splicing factors YBX1 and HSPA2 exhibited the most changes in dermatomyositis and immune-mediated necrotising myopathy. Increased IR was associated with reduced protein expression. Some of the IIM-specific DEIs were correlated with clinical parameters (skin rash, MMT-8 scores and muscle enzymes) and muscle histopathological features (myofiber necrosis, regeneration and inflammation). IRs in IFIH1 and TRIM21 were strongly correlated with anti-MDA5+ antibody, while IRs in SRP14 were associated with anti-SRP+ antibody. CONCLUSION: This study revealed distinct IRs and specific splicing factors associated with IIM subtypes, which might be contributing to the pathogenesis of IIM. We also emphasised the potential impact of IR on protein expression in IIM muscles.

Defective Diamane: A Superior Sensor for Toxic Gases Capture and Detection with Excellent Selectivity, Sensitivity, and Reversibility at Room Temperature.

The toxic gases emitted from industrial production have caused significant damage to the environment and human health, necessitating efficient gas sensors for their detection and removal. In this work, first-principles calculations are employed to investigate the potential application of diamanes for high-performance toxic gas sensors. The results show that nine gas molecules (CO, CO2, NO, NO2, NH3, SO2, N2, O2, and H2O) are physisorbed on pristine diamane by weak van der Waals interactions. After introducing H/F defects, diamane can effectively capture specific toxic gases (CO, NO, NO2, and SO2) in the presence of interfering gases (N2, O2, and H2O), suggesting excellent selectivity and anti-interference ability. Orbital hybridization and significant charge redistribution between gas molecules and defective diamane dominate the enhanced adsorbate-substrate interactions. More importantly, the high sensitivity and good reversibility of defective diamane for detecting CO, NO, and SO2 molecules enable its reuse as a superior resistance-type gas sensor. Our calculations provide valuable insights into the potential of defective diamane for detecting toxic gases and shed light on the practical application of novel carbon-based materials in the gas-sensing field.

Sulfur-Induced Electronic Optimization of N-Doped Carbon with CoP/Co2P Heterostructure by Precursor Design for Rechargeable Zinc-Air Batteries.

Heteroatom doping and heterostructure construction are the key methods to improve the performance of electrocatalysts. However, developing such catalysts remains a challenging task. Herein, we designed two comparable polymers, phytic acid/thiourea polymer (PATP) and phytic acid/urea polymer (PAUP), as precursors, which contain C, N, S/O, and P by microwave heating. To pinpoint how the introduction of sulfur would affect the electronic structure and catalytic activity, these two polymers were physically blended with CoCo-Prussian blue analogue (CoCo-PBA) and further calcination, respectively. The highly dispersed CoP/Co2P-rich interfacial catalysts anchored on the N,S-codoped or N-doped carbon support were successfully prepared (CoP/Co2P@CNS and CoP/Co2P@CN). The prepared CoP/Co2P@CNS catalyst showed good ORR properties (E1/2 = 0.856 V vs RHE) and OER properties (Ej10 = 1.54 V vs RHE), which were superior to the commercial Pt/C and RuO2 catalysts. The reversible oxygen electrode index (ΔE = Ej10 - E1/2) can reach ∼0.684 V. Meanwhile, the rechargeable zinc-air battery assembled with a CoP/Co2P@CNS catalyst as the air cathode also showed excellent performance, with a charge-discharge cycle stability of up to 900 h. DFT calculations further confirm that the introduction of S atoms can affect the electronic structure and enhance the catalytic activity of C and N atoms on carbon support.

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

Activation of Ga Liquid Catalyst with Continuously Exposed Active Sites for Electrocatalytic C-N Coupling.

Environmentally friendly electrocatalytic coupling of CO2 and N2 for urea synthesis is a promising strategy. However, it is still facing problems such as low yield as well as low stability. Here, a new carbon-coated liquid alloy catalyst, Ga79Cu11Mo10@C is designed for efficient electrochemical urea synthesis by activating Ga active sites. During the N2 and CO2 co-reduction process, the yield of urea reaches 28.25 mmol h-1 g-1, which is the highest yield reported so far under the same conditions, the Faraday efficiency (FE) is also as high as 60.6 % at -0.4 V vs. RHE. In addition, the catalyst shows excellent stability under 100 h of testing. Comprehensive analyses showed that sequential exposure of a high density of active sites promoted the adsorption and activation of N2 and CO2 for efficient coupling reactions. This coupling reaction occurs through a thermodynamic spontaneous reaction between *N=N* and CO to form a C-N bond. The deformability of the liquid state facilitates catalyst recovery and enhances stability and resistance to poisoning. Moreover, the introduction of Cu and Mo stimulates the Ga active sites, which successfully synthesises the *NCON* intermediate. The reaction energy barrier of the third proton-coupled electron transfer process rate-determining step (RDS) *NHCONH→*NHCONH2 was lowered, ensuring the efficient synthesis of urea.

Screening prognostic markers for hepatocellular carcinoma based on pyroptosis-related lncRNA pairs.

BACKGROUND: Pyroptosis is closely related to cancer prognosis. In this study, we tried to construct an individualized prognostic risk model for hepatocellular carcinoma (HCC) based on within-sample relative expression orderings (REOs) of pyroptosis-related lncRNAs (PRlncRNAs). METHODS: RNA-seq data of 343 HCC samples derived from The Cancer Genome Atlas (TCGA) database were analyzed. PRlncRNAs were detected based on differentially expressed lncRNAs between sample groups clustered by 40 reported pyroptosis-related genes (PRGs). Univariate Cox regression was used to screen out prognosis-related PRlncRNA pairs. Then, based on REOs of prognosis-related PRlncRNA pairs, a risk model for HCC was constructed by combining LASSO and stepwise multivariate Cox regression analysis. Finally, a prognosis-related competing endogenous RNA (ceRNA) network was built based on information about lncRNA-miRNA-mRNA interactions derived from the miRNet and TargetScan databases. RESULTS: Hierarchical clustering of HCC patients according to the 40 PRGs identified two groups with a significant survival difference (Kaplan-Meier log-rank, p = 0.026). Between the two groups, 104 differentially expressed lncRNAs were identified (|log2(FC)|> 1 and FDR < 5%). Among them, 83 PRlncRNA pairs showed significant associations between their REOs within HCC samples and overall survival (Univariate Cox regression, p < 0.005). An optimal 11-PRlncRNA-pair prognostic risk model was constructed for HCC. The areas under the curves (AUCs) of time-dependent receiver operating characteristic (ROC) curves of the risk model for 1-, 3-, and 5-year survival were 0.737, 0.705, and 0.797 in the validation set, respectively. Gene Set Enrichment Analysis showed that inflammation-related interleukin signaling pathways were upregulated in the predicted high-risk group (p < 0.05). Tumor immune infiltration analysis revealed a higher abundance of regulatory T cells (Tregs) and M2 macrophages and a lower abundance of CD8 + T cells in the high-risk group, indicating that excessive pyroptosis might occur in high-risk patients. Finally, eleven lncRNA-miRNA-mRNA regulatory axes associated with pyroptosis were established. CONCLUSION: Our risk model allowed us to determine the robustness of the REO-based PRlncRNA prognostic biomarkers in the stratification of HCC patients at high and low risk. The model is also helpful for understanding the molecular mechanisms between pyroptosis and HCC prognosis. High-risk patients may have excessive pyroptosis and thus be less sensitive to immune therapy.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs.

BACKGROUND: Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability. METHODS: We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs. RESULTS: Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer's disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015). CONCLUSIONS: The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

Identifying the tissues-of-origin of circulating cell-free DNAs is a promising way in noninvasive diagnostics.

Advances in sequencing technologies facilitate personalized disease-risk profiling and clinical diagnosis. In recent years, some great progress has been made in noninvasive diagnoses based on cell-free DNAs (cfDNAs). It exploits the fact that dead cells release DNA fragments into the circulation, and some DNA fragments carry information that indicates their tissues-of-origin (TOOs). Based on the signals used for identifying the TOOs of cfDNAs, the existing methods can be classified into three categories: cfDNA mutation-based methods, methylation pattern-based methods and cfDNA fragmentation pattern-based methods. In cfDNA mutation-based methods, the SNP information or the detected mutations in driven genes of certain diseases are employed to identify the TOOs of cfDNAs. Methylation pattern-based methods are developed to identify the TOOs of cfDNAs based on the tissue-specific methylation patterns. In cfDNA fragmentation pattern-based methods, cfDNA fragmentation patterns, such as nucleosome positioning or preferred end coordinates of cfDNAs, are used to predict the TOOs of cfDNAs. In this paper, the strategies and challenges in each category are reviewed. Furthermore, the representative applications based on the TOOs of cfDNAs, including noninvasive prenatal testing, noninvasive cancer screening, transplantation rejection monitoring and parasitic infection detection, are also reviewed. Moreover, the challenges and future work in identifying the TOOs of cfDNAs are discussed. Our research provides a comprehensive picture of the development and challenges in identifying the TOOs of cfDNAs, which may benefit bioinformatics researchers to develop new methods to improve the identification of the TOOs of cfDNAs.

REBET: a method to determine the number of cell clusters based on batch effect removal.

In single-cell RNA-seq (scRNA-seq) data analysis, a fundamental problem is to determine the number of cell clusters based on the gene expression profiles. However, the performance of current methods is still far from satisfactory, presumably due to their limitations in capturing the expression variability among cell clusters. Batch effects represent the undesired variability between data measured in different batches. When data are obtained from different labs or protocols batch effects occur. Motivated by the practice of batch effect removal, we considered cell clusters as batches. We hypothesized that the number of cell clusters (i.e. batches) could be correctly determined if the variances among clusters (i.e. batch effects) were removed. We developed a new method, namely, removal of batch effect and testing (REBET), for determining the number of cell clusters. In this method, cells are first partitioned into k clusters. Second, the batch effects among these k clusters are then removed. Third, the quality of batch effect removal is evaluated with the average range of normalized mutual information (ARNMI), which measures how uniformly the cells with batch-effects-removal are mixed. By testing a range of k values, the k value that corresponds to the lowest ARNMI is determined to be the optimal number of clusters. We compared REBET with state-of-the-art methods on 32 simulated datasets and 14 published scRNA-seq datasets. The results show that REBET can accurately and robustly estimate the number of cell clusters and outperform existing methods. Contact: H.D.L. (hongdong@csu.edu.cn) or Q.S.X. (qsxu@csu.edu.cn).